by karin burk busby

TEXAS SCIENCE TEACHER CHARACTERISTICS AND CONCEPTUAL

UNDERSTANDING OF NEWTON’S LAWS OF MOTION

by

Karin Burk Busby

APPROVED BY SUPERVISORY COMMITTEE:

___________________________________________

Mary Urquhart, Chair

___________________________________________

Jim McConnell

___________________________________________

Stephanie Taylor

Copyright 2017

Karin Burk Busby

All Rights Reserved

To my family, who has always supported me,

my husband David, my parents Larry and Joan Burk,

and my children, Michael, Anastasia, Alexander, and the two in my tummy.



by

KARIN BURK BUSBY, BA, MED

THESIS

Presented to the Faculty of

The University of Texas at Dallas

in Partial Fulfillment

of the Requirements

for the Degree of

MASTER OF ARTS IN TEACHING IN

SCIENCE EDUCATION

THE UNIVERSITY OF TEXAS AT DALLAS

May 2017

v

ACKNOWLEDGMENTS

I would like to express my profound respect and gratitude to my committee chair Dr. Mary

Urquhart. You saw the value in my work and helped me through the worst. Your dedication to

the art of science education has helped me become both a better teacher and a better scholar. I

would also like to thank the members of my thesis committee, Dr. Jim McConnell and Dr.

Stephanie Taylor. You both gave me your time and resources in order to improve this research

and I am deeply humbled. To Dr. Homer Montgomery, thank you for always pushing me to

produce the best body of work possible and to not accept good enough from myself. To Georgia

Stuart, whose statistical knowledge and gracious heart helped produced a statistically sound

piece of work. This research is a testament to all of your dedication to science education.

A special thank you to the TRC and Dr. Carol Fletcher for allowing me to work with your

archival data. To the two school districts who allowed me to conduct research, thank you. I will

keep your anonymity but know this body of research comes from your trust.

Finally, thank you to the people behind the scenes who stood by me and pushed me when I was

ready to quit. To my late father, Larry C. Burk, who taught me how to write and how to question;

those skills created this piece of work. To my mother, Joan B. Burk, your life has been a

dedication to the craft of education and I am humbled to follow in your footsteps. To my

husband, David M. Busby, thank you for stepping up as a single parent so many nights so I could

write. To my children, I hope one day you see this thesis and know that anything is possible, no

matter how difficult it looks to be.

April 2017

vi



Karin Burk Busby, MAT

The University of Texas at Dallas, 2017

ABSTRACT

Supervising Professor: Mary Urquhart

Misconceptions of Newtonian mechanics and other physical science concepts are well

documented in primary and pre-service teacher populations (Burgoon, Heddle, & Duran, 2009;

Allen & Coole, 2012; Kruger, Summers, & Palacio, 1990; Ginns & Watters, 1995; Trumper,

1999; Asikainen & Hirovonen, 2014). These misconceptions match the misconceptions held by

students, leaving teachers ill-equipped to rectify these concepts in the classroom (Kind, 2014;

Kruger et al., 1990; Cochran & Jones, 1998). Little research has been devoted to misconceptions

held by in-service secondary teachers, the population responsible for teaching Newtonian

mechanics. This study focuses on Texas in-service science teachers in middle school and high

school science, specifically sixth grade science, seventh grade science, eighth grade science,

integrated physics and chemistry, and physics teachers.

This study utilizes two instruments to gauge conceptual understanding of Newton’s laws of

motion: the Force Concept Inventory [FCI] (Hestenes, Wells, & Swackhamer, 1992) and a

custom instrument developed for the Texas Regional Collaboratives for Excellence in Science

and Mathematics Teaching (Urquhart, M., e-mail, April 4, 2017). Use of each instrument had its

vii

strengths and limitations. In the initial work of this study, the FCI was given to middle and high

school teacher volunteers in two urban school districts in the Dallas- Fort Worth area to assess

current conceptual understanding of Newtonian mechanics. Along with the FCI, each participant

was asked to complete a demographic survey. Demographic data collected included participant’s

sex, years of service in teaching position, current teaching position, degrees, certification type,

and current certifications for science education. Correlations between variables and overall

average on the FCI were determined by t-tests and ANOVA tests with a post-hoc Holm-

Bonferroni correction test. Test questions pertaining to each of Newton’s three laws of motion

were extrapolated to determine any correlations. The sample size for this study was small (n=24),

requiring a second study investigate potential correlations to teacher characteristics.

The second study was conducted using the 2013-2014 school year participants in the Texas

Regional Collaboratives for Excellence in Science and Mathematics Teaching [TRC] (Texas

Regional Collaborative for Excellence in Science and Mathematics Teaching, 2013), a statewide

program led by The University of Texas at Austin Center for STEM Education (Texas Regional

Collaborative for Excellence in Science and Mathematics Teaching, 2013). Participants

completed a demographic survey and took the TRC Physics Assessment instrument developed

for the TRC to determine current conceptual understanding of Newtonian mechanics as defined

by the Texas Essential Knowledge and Skills. The TRC also collected demographic data

including Texas Educational Agency region, participant’s sex, years of service in teaching,

current teaching position, level of highest degree earned, whether or not the participant had a

STEM degree, and certification type. Correlations were determined between overall average and

conceptual force questions only. The sample size was substantial (n=368) but due to time

viii

constraints in its development, the TRC Physics Assessment was unable to undergo reliability or

validity testing before implementation. Test question pertaining to each of Newton’s three laws

of motion were extrapolated to determine any correlations. A significance value of p= 0.05 was

used for all tests.

Both content assessments indicated that, on average, teacher-participants had a considerable

misunderstanding of Newtonian mechanics with Newton’s third law questions especially

difficult for the populations. Teachers’ current teaching assignment was statistically significant

for most tests, suggesting that high school physics teachers have more conceptual understanding

of Newtonian mechanics than middle school teachers but have not necessarily mastered

Newtonian mechanics. STEM majors and participant’s sex were significant only for the TRC

Physics Assessment.

One outcome of this study is a recommendation that the Texas teacher certification process for

middle school science change to include a general science test that includes physical science.

Also, in-service science teachers responsible for teaching Newton’s laws of motion should

participate in specific professional development from a physics content educational expert to

address misconceptions. Additional recommendations include that physics teachers take a

mentoring role to help other teachers in physical science concepts and that middle school

curriculum provide assistance to teachers for addressing misconceptions of Newton’s third law.

ix

TABLE OF CONTENTS

ACKNOWLEDGMENTS ………………………………………………………………………. v

ABSTRACT ……………………………………………………………………………………. vi

LIST OF TABLES ……………………………………………………………………………… xi

LIST OF FIGURES ……………………………………………………………………………. xv

CHAPTER 1 INTRODUCTION ………………………………………………………………... 1

CHAPTER 2 BACKGROUND ………………………………………………………………... 12

CHAPTER 3 METHODOLOGY ……………………………………………………………… 22

CHAPTER 4 RESULTS ……………………………………………………………………….. 33

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS ……………………………… 82

APPENDIX A ………………………………………………………………………………….. 88

APPENDIX B ………………………………………………………………………………….. 91

APPENDIX C ………………………………………………………………………………… 99

APPENDIX D ………………………………………………………………………………… 102

APPENDIX E ………………………………………………………………………………… 105

APPENDIX F ………………………………………………………………………………… 106

APPENDIX G ……………………………………...…………………………………………. 113

APPENDIX H…………………………………………………………………………………. 117

APPENDIX I …………………………………………………………………………………. 120

APPENDIX J …………………………………………………………………………………. 124

APPENDIX K ……………………………………………………………………………….... 126

APPENDIX L ………………………………………………………………………………… 197

x

APPENDIX M………………………………………………………………………………… 204

REFERENCES ……………………………………………………………………………….. 205

BIOGRAPHICAL SKETCH …………………………………………………………………. 210

CURRICULUM VITAE

xi

LIST OF TABLES

2.1 Types of Certification for each subject…………………………………………………… 20

3.1 FCI question breakdown by Newton’s laws ……………………………….………...…... 25

3.2 TEKS identified as low performing ……………………………………………………… 26

3.3 TRC Physics Assessment question breakdown by conceptual force and Newton’s laws

questions ………………………………………………………………………….……… 28

4.1 Descriptive statistics for FCI overall averages …………………………………...……… 33

4.2 FCI overall averages by knowledge threshold ……………………………………...…… 34

4.3 Descriptive statistics for FCI Newton’s first law questions ……………………………... 34

4.4 FCI Newton’s first law questions by knowledge threshold …………………………..….. 35

4.5 Descriptive statistics for FCI Newton’s second law questions …………………………... 35

4.6 FCI Newton’s second law questions by knowledge threshold ……………………….….. 36

4.7 Descriptive statistics for FCI Newton’s third law questions …………………………….. 37

4.8 FCI Newton’s third law questions by knowledge threshold ……………………………... 38

4.9 ANOVA of grade level FCI overall averages ……………………………………...…….. 38

4.10 Descriptive statistics of grade level FCI overall averages ……………………………….. 39

4.11 ANOVA of grade level taught for Newton's third law question of the FCI ……………... 40

4.12 Descriptive statistics of grade level taught for Newton's third law question of the FCI … 41

4.13 Results of Chi-square test and descriptive statistics for knowledge threshold by grade level

taught for FCI Newton's third law…………………………………………………………41

4.14 FCI statistical test with adjusted p-values > 0.05 ……………………………..………..... 42

4.15 Descriptive statistics for TRC Physics Assessment overall ………………………..…….. 43

4.16 Descriptive statistics for TRC Physics Assessment conceptual force questions ………… 44

xii

4.17 Descriptive statistics for TRC Physics Assessment Newton's first law questions ………. 44

4.18 Descriptive statistics for TRC Physics Assessment Newton's second law questions ……. 45

4.19 Descriptive statistics for TRC Physics Assessment Newton's third law ………………… 46

4.20 ANOVA of region for TRC Physics Assessment overall average …………………..…... 48

4.21 Descriptive statistics of regions for TRC Physics Assessment overall averages ……........ 49

4.22 ANOVA of region type for TRC Physics Assessment overall average ………….…......... 50

4.23 Descriptive statistics of region type for TRC Physics Assessment overall averages ……. 50

4.24 Descriptive statistics of participant’s sex for TRC Physics Assessment overall averages.. 51

4.25 t-Test: Two-sample assuming unequal variances of sex for TRC Physics Assessment

overall averages .…………………………………………………………………………. 51

4.26 Descriptive statistics of STEM major for TRC Physics Assessment overall averages ….. 52

4.27 Two-sample assuming unequal variances of STEM major for TRC Physics Assessment

overall averages ….………………………………………………………………………..52

4.28 ANOVA of grades taught (MS vs HS) for TRC Physics Assessment overall averages …. 53

4.29 Descriptive statistics of grades taught (MS vs HS) for TRC Physics Assessment overall

averages ……………………………………………………………………….…………. 54

4.30 ANOVA of grades taught (Eighth Grade vs HS) for TRC Physics Assessment overall

averages ..……………………………………………………………….………………… 55

4.31 Descriptive statistics of grades taught (Eighth Grade vs HS) for TRC Physics Assessment

overall averages ..………………………………………………………………………… 55

4.32 ANOVA of grades taught for TRC Physics Assessment overall average ……………….. 56

4.33 Descriptive statistics of grades taught for TRC Physics Assessment overall averages …. 57

4.34 ANOVA of region for TRC Physics Assessment conceptual force questions ………....... 58

4.35 Descriptive statistics of region for TRC Physics Assessment conceptual force questions . 59

xiii

4.36 Descriptive statistics of participants’ sex for TRC Physics Assessment conceptual force

questions …………………………………………………………………………………. 60


conceptual force questions ...……………………………………………………………... 60

4.38 Descriptive statistics of STEM degree for TRC Physics Assessment .…………………... 61

4.39 t-Test: Two-sample assuming unequal variances of STEM degree for TRC Physics

Assessment conceptual force questions ...………………………………………………... 61

4.40 ANOVA of grades taught (MS vs HS) for TRC Physics Assessment conceptual force

questions …………………………………………………………………………………. 62

4.41 Descriptive statistics of grades taught (MS vs HS) for TRC Physics Assessment conceptual

force questions …………….……………………………………………………………... 63

4.42 ANOVA of grades taught (Eighth vs HS) for TRC Physics Assessment conceptual force

questions …………………………………………………………………………………. 64

4.43 Descriptive statistics of grades taught (Eighth vs HS) for TRC Physics Assessment

conceptual force questions …………….…………………………………………………. 64

4.44 ANOVA of grades taught for TRC Physics Assessment conceptual force questions……. 65

4.45 Descriptive statistics of grades taught for TRC Physics Assessment conceptual force

questions …………………………………………………………………………………. 66

4.46 Descriptive statistics of STEM degree for TRC Physics Assessment Newton's first law

questions …………………………………………………………………………………. 67

4.47 t-Test: Two-sample assuming unequal variances of STEM degree for TRC Physics

Assessment Newton's first law questions ……………………………………………....... 68

4.48 ANOVA of grades taught (MS vs HS) for TRC Physics Assessment Newton's first law

questions …………………………………………………………………………………. 69

4.49 Descriptive statistics of grades taught (MS vs HS) for TRC Physics Assessment Newton's

first law questions ...……………………………………………………………………… 69

4.50 ANOVA of grades taught for TRC Physics Assessment Newton’s first law questions …. 70

4.51 Descriptive Statistics of Grades Taught for TRC Physics Assessment Newton’s first law

questions …………………………………………………………………………………. 71

xiv

4.52 ANOVA of grades taught (MS vs HS) for TRC Physics Assessment Newton's second law

questions …….…………………………………………………………………………… 72

4.53 Descriptive statistics of grades taught (MS vs HS) for TRC Physics Assessment Newton's

second law questions ..…………………………………………………………………… 73

4.54 ANOVA of grades taught (Eighth vs HS) for TRC Physics Assessment Newton's second

law questions ………..……………………………………………………………………. 74

4.55 Descriptive statistics of grades taught (Eighth vs HS) for TRC Physics Assessment

Newton's second law questions ...………………………………………………………… 74

4.56 ANOVA of grades taught for TRC Physics Assessment Newton's second law questions . 75

4.57 Descriptive statistics of grades taught for TRC Physics Assessment Newton's second law

questions …………………………………………………………………………………. 76

4.58 ANOVA of rural regions for TRC Physics Assessment Newton's third law questions ..... 78

4.59 Descriptive statistics of rural regions for TRC Physics Assessment Newton's third law

questions …………………………………………………………………………………. 78

4.60 Descriptive statistics of participants’ sex for TRC Physics Assessment Newton’s third law

questions ...……………………………………………………………………………….. 79


Newton’s third law questions …………………………………………………………….. 79

4.62 TRC Physics Assessment statistical tests with adjusted p-values > 0.05 ………………... 80

xv

LIST OF FIGURES

1.1 Research Questions Flow Chart …………………………………………………………… 7

1.2 Hypotheses Flow Chart ……………………………………………………………………. 8

4.1 FCI distribution for overall averages …………………………………………………….. 33

4.2 FCI distribution for Newton’s first law questions ……………………………………...... 35

4.3 FCI distribution for Newton’s second law questions…………………………………...… 36

4.4 FCI distribution for Newton’s third law questions ………………………………………. 37

4.5 TRC Physics Assessment distribution for overall averages ……………………………... 43

4.6 TRC Physics Assessment distribution for conceptual force questions …………………... 44

4.7 TRC Physics Assessment distribution for Newton’s first law questions ………………... 45

4.8 TRC Physics Assessment distribution for Newton’s second law questions ……………... 46

4.9 TRC Physics Assessment distribution for Newton’s third law questions ……………….. 47

5.1 Results Flow Chart …..…………………………………………………………………... 82

1

CHAPTER 1

INTRODUCTION

Teacher knowledge directly influences student knowledge (Sadler, Sonnert, Coyle, Cook-

Smith, & Miller, 2013; Abell, 2007). Teacher knowledge is defined as both content knowledge,

referred to as CK in this study, and pedagogical content knowledge, referred to as PCK (Abell,

2007). The influence of CK versus PCK is mixed with some research showing teacher degrees in

formal education an influencing factor (Hill, Rowan, & Ball, 2005; Hashweh, 1987) and other

studies finding teacher experience more influential (Sanders, Borko, & Lockard, 1993; Deng,

2007). However, the research indicates that the most effective teachers are those who have both

strong CK and PCK (Abell, 2007; Etkina, 2010; Sadler et al., 2013).

Current research in physical science content knowledge has focused mainly on in-service

elementary science teachers (Burgoon, Heddle, & Duran, 2009; Allen & Coole, 2012; Kruger,

Summers, & Palacio, 1990; Ginns & Watters, 1995) and pre-service physics teachers (Trumper,

1999; Asikainen & Hirovonen, 2014). There are limited studies on physical science content

knowledge in secondary science in the United States (Sadler et al., 2013).

Middle school science teachers are responsible for teaching basic physical science

concepts including Newton’s three laws of motion (Next Generation Science Standards, 2013;

Texas Essential Knowledge and Skill [TEKS], 2010). Sadler’s et al. (2013) national US study

tested 181 current in-service middle school physical science teachers’ content knowledge using a

multiple choice pretest and posttest covering the grades five to eight physical science standards

of the National Science Education Standards [NSES]. Initially, they found teacher subject

2

knowledge of the physical science standards of the NSES was strong, but there were “noticeable

holes in their knowledge (p. 1043).” Yip, Chung, & Mak (1998) studied a similar population of

147 in-service middle school science teachers in Hong Kong using a true false physical science

competency test and found the teacher population’s content knowledge of physical science was

weak. Yip et al. emphasized that those teachers who majored in physics had stronger physical

science knowledge than teachers who did not major in physics, but they did not possess enough

physical science knowledge for mastery. Harrell (2010) studied 93 in-service eighth grade

science teachers described as highly qualified for the content by Texas certification. Using

undergraduate transcripts and the 8-12 science Texas Examination of Educator Standards

[TExES] diagnostic examination, she found physical science content knowledge for the

population as overall insufficient.

The American Association of Physics Teachers [AAPT] (1988) described the ideal

physics teacher as a teacher who majored in physics. However, Trumper’s (1999) study on 25

pre-service physics teachers in Israel using a multiple choice test determined the teachers still

had misconceptions about force and motion after completing a four year study in physical

science education. Asikainen and Hirvonen (2014) studied nine pre-service physics teachers and

18 in-service physics teachers in Israel. Using interviews and an open-ended test, they

ascertained that the population also had physical science misconceptions although this study

focused on quantum mechanics. Galil and Lehavi’s (2006) study tasked 75 in-service physics

teachers to define physics concepts. They determined that the resulting definitions were

insufficient, which alludes to conceptual misconceptions but is not definitive.

3

Little research in the United States has focused on secondary in-service teachers’ physical

science content knowledge and what characteristics correspond with misconceptions (Sadler et

al., 2013). Arzi and White (2007) found that formal education was forgotten and specific content

knowledge was retained as a teacher continued teaching through years of service. Similarly,

Sander et al. (1993) found that when teachers were forced to teach content outside their

knowledge base, expert teachers would overcome knowledge deficits without intervention but

the process would take time. Kind’s (2013) study of chemistry teachers found misconceptions

are more prevalent in teachers responsible for science subjects not related to their degrees.

However, teachers who majored in physics still have physical science misconceptions (Galili &

Lehavi, 2006). It is unclear how prevalent physical science misconceptions are in the current

teaching population.

This study focuses on Texas in-service secondary science teachers who are responsible

for teaching physical science content, specifically Newton’s three laws of motion. This study

identifies current population’s conceptual understanding of Newton’s three laws of motion and

identifies any correlations between teacher physical science content knowledge and teacher’s

characteristics, including educational background, certification, and years of service.

Statement of Problem

As previously noted, research has shown that teacher content knowledge affects student

knowledge. Current research cannot adequately describe how prevalent misconceptions about

Newton’s three laws of motion are within the physical science teacher population. Conflicting

research cannot clarify if teacher background, formal science education, or years of service

4

correlate with proper understanding of Newtonian mechanics (Kind, 2013; Sadler et al., 2013;

Galili & Lehavi, 2006; Trumper, 1999).

Purpose of Study

The purpose of this study is to identify current Texas sixth grade science teachers,

seventh grade science teachers, eighth grade science teachers, integrated physics and chemistry

teachers, and physics teachers’ conceptual understanding of Newton’s laws of motion and to

identify any correlations between teacher characteristics such as formal science education at the

undergraduate level, certification, and years of service with conceptual understanding of

Newtonian mechanics. According to the Texas science standards, the TEKS, these teachers

directly teach Newtonian mechanics to their students, so teacher misconceptions could pose an

issue for student learning (Sadler et al., 2013; Burgoon et al., 2009; Berg & Brouwer, 1991) (see

Appendix A for a full list of TEKS).

Research Questions

For the purpose of this study, the following questions were addressed (see Figure 1.1):

1. What is the overall conceptual understanding of Newtonian mechanics in the current

Texas sixth grade science teacher, seventh grade science teacher, eighth grade science

teacher, integrated physics and chemistry teacher, and physics teacher population?

2. What is the conceptual understanding of each of Newton’s three laws of motion in the

current Texas sixth grade science teacher, seventh grade science teacher, eighth grade

5

science teacher, integrated physics and chemistry teachers, and physics teacher

population?

3. Is there a correlation between the sex of the teacher and his or her conceptual

understanding of Newtonian mechanics in the current Texas sixth grade science teacher,

seventh grade science teacher, eighth grade science teacher, integrated physics and

chemistry teacher, and physics teacher population?

4. Is there a correlation between school district or region and conceptual understanding of

Newtonian mechanics in the current Texas sixth grade science teacher, seventh grade

science teacher, eighth grade science teacher, integrated physics and chemistry teacher,

and physics teacher population?

5. Is there a correlation between grade level taught and conceptual understanding of




6. Is there a correlation between years of service and conceptual understanding of




7. Is there a correlation between highest earned degree and conceptual understanding of




6

8. Is there a correlation between undergraduate major and conceptual understanding of




9. Is there a correlation between type of certification program and conceptual understanding

of Newtonian mechanics in the current Texas sixth grade science teacher, seventh grade



10. Is there a correlation between the teacher subject certification and conceptual

understanding of Newtonian mechanics in the current Texas sixth grade science teacher,

seventh grade science teacher, eighth grade science teacher, integrated physics and

chemistry teacher, and physics teacher population?

Hypotheses

This investigation included the following hypotheses (see Figure 1.2):

1. Teacher misconceptions about Newtonian mechanics are significantly prevalent in the

Texas sixth grade science teacher, seventh grade science teacher, eighth grade science

teacher, integrated physics and chemistry teacher, and physics teacher population.

2. No correlation exists between sex of the teacher, school district, undergraduate major,

certification program, or type of teacher subject certification with conceptual

understanding of Newtonian mechanics in the Texas sixth grade science teacher, seventh

grade science teacher, eighth grade science teacher, integrated physics and chemistry

teacher, and physics teacher population.

7

Figure 1.1. Research Questions Flow Chart

3. A positive correlation exists between undergraduate major in STEM and conceptual

understanding of Newtonian mechanics in the Texas sixth grade science teacher, seventh

grade science teacher, eighth grade science teacher, integrated physics and chemistry

teacher, and physics teacher population.

4. A positive correlation exists between grade level taught and conceptual understanding of

Newtonian mechanics in the Texas sixth grade science teacher, seventh grade science

8

teacher, eighth grade science teacher, integrated physics and chemistry teacher, and

physics teacher population.

5. A positive correlation exists between years of service and conceptual understanding of

Newtonian mechanics in the Texas sixth grade science teacher, seventh grade science

teacher, eighth grade science teacher, integrated physics and chemistry teacher, and

physics teacher population.

Figure 1.2. Hypotheses Flow Chart

9

Definition of Terms

1. Significantly Prevalent- the target population exhibits >40 percent of Newtonian

misconceptions as defined by Hestenes and Halloun’s (1995) response to exploring the

Force Concept Inventory.

2. Grade level taught- the target’s current teaching position will only be considered. In the

case of split level teaching, target’s majority >50 percent of teaching time, will be

considered current teaching position as defined by the Texas Education Agency (2015).

3. Certification Program- the target’s type of program in which a teaching certificate was

earned, either defined as traditional certification program or alternative certification

program as determined by Texas Education Agency (2015).

4. School District- the target’s current school district at time of participation.

5. Undergraduate Degree- an undergraduate degree will be defined as a Bachelor of

Science, a Bachelor of Art, Bachelor of Fine Arts, and Bachelor of Business

Administration.

6. Years of Service- years of service will count upon the continuation of one academic year.

Targets who are in their first year of teaching will have earned 1 years of service.

Semester only years will not count.

7. Science Teacher Certificate- science teacher certificates are divided into three categories;

Generalist, Science Generalist, Specialized Science. Targets will choose all certifications,

but targets will be categorized by most specialized degree.

10

8. Generalist- a target whose certification is of the following; Generalist EC-6, Generalist 4-

8, Core Subject EC-6, and Core Subjects 4-8 or of similar type. (See Appendix B for full

description.)

9. Science Generalist- a target whose certification is of the following; Science 4-8; Science

7-12; Mathematics Science 4-8; or of a similar type. (See Appendix B for full

description)

10. Specialized Science- a target whose certification is of the following; Chemistry 7-12; Life

Science 7-12; Physical Science 6-12; Mathematics/Physical Science/Engineering 6-12;

Mathematics/Physical Science/Engineering 8-12; Physics/Mathematics 7-12, Physics/

Mathematics 8-12. (See Appendix B for full description)

11. Knowledge Threshold- the amount of knowledge a target has about conceptual physics

determined by the Force Concept Inventory (Hestenes & Halloun, 1995).

12. Content Knowledge- content knowledge is understanding of physical science principles

and theories

13. Pedagogical Content Knowledge- pedagogical content knowledge is understanding how

students learn a particular subject, underlying misconceptions students may have in that

subject, and how to relate subject content to students’ everyday life.

Study

This study is composed of two parts which both have considerable limitations. Study One

used the Force Concept Inventory [FCI] instrument to determine teacher physical science content

knowledge. The FCI is a valid and reliable instrument for assessing conceptual understanding of

force and motion. The sampling size for Study One was small (n=24) which limited any findings

11

as suggestive only. Demographic information was included at the end of the instrument to

identify teacher characteristics. Correlations were identified using t-Test and ANOVA when

appropriate. A Chi-squared contingency table was used to identify correlations in terms of

knowledge thresholds.

To support any suggestive findings in Study One, a second study was implemented. As

previously described, Study Two included the participants of the 2013-2014 Texas Regional

Collaboratives for Excellence in Science and Mathematics Teaching [TRC], a state-wide

professional development program supported by The University of Texas at Austin. The TRC

collects individual demographic data on all participants as part of the participant profile, which

was used to determine population characteristics. The professional development focus for 2013-

2014 school year was on physical science Texas Essential Knowledge and Skills [TEKS] (see

Appendix A). As part of the program, Dr. Mary Urquhart of The University of Texas at Dallas

was requested to develop the TRC Physics Assessment to align with TEKS for middle school

science force and motion over a period of two months. Due to the time constraints, the

instrument was unable to undergo validity and reliability testing before implementation. The

TRC Physics Assessment was administered twice, once as a pre-test and once as a post-test. This

study only analyzed pre-test data. Individual demographic data was uniquely linked to individual

pre-tests, allowing for the analysis in this study. The sample size for Study Two was substantial

(n=368) but not necessarily representative of the Texas teacher population due to self-selection

bias for participating in the 100 contract-hour TRC professional development program.

Correlations were identified using t-Test and ANOVA when appropriate. Both studies’ results

were compared to determine any trends in the data.

12

CHAPTER 2

BACKGROUND

Teacher knowledge, which includes CK and PCK, directly influences student knowledge

(Sadler et al., 2013; Abell, 2007; Etkina, 2010) however which is more influential is unresolved.

Teacher CK is influenced by misconceptions, which are resilient (Trumper, 1999; Kikas, 2002;

Viennot, 1979; Solomon 1983) lasting well beyond formal education. Teacher misconceptions in

physical science have been documented in pre-service high school physics teachers (Trumper,

1999; Asikainen & Hirovonen, 2014) and in-service elementary science teachers (Burgoon et al.,

2009; Kruger, Summers, & Palacio, 1990; Ginn & Watters, 1995). Research on middle school

and high school physical science teacher misconceptions are limited and mixed, with some

research showing prevalent misconceptions (Yip et. al, 1998) while other research finding

minimal (Sadler et al., 2013). Teacher misconceptions are more prevalent in those with science

degrees not related to their field of teaching (Kind, 2014), but misconceptions have been

documented in current physics teacher holding physics degrees (Galili & Lehavai, 2006).

Nature of Scientific Misconceptions

Students form an understanding of scientific process before any formal instruction,

(National Research Council [NRC], 2005). The NRC explained further that these pre-educational

constructs can be inaccurate or incomplete, developing misconceptions in student’s knowledge

before he or she is formally trained. A scientific misconception describes a science conception

that differs from currently accepted scientific knowledge as described by Burgoon’s et al. (2009)

study on 103 Ohio elementary teachers’ physical science knowledge and correlations between

13

teacher misconceptions and student misconceptions. Kikas’ (2004) study of 198 in-service

teacher understanding of object’s motion derived that misconceptions arise as people “attempt to

understand complicated knowledge” (p. 435) as determined by an evaluation and problem task

questionnaire. These misconceptions are vast and prevalent (Poutot & Blandin, 2015; Kikas,

2002; Trumper, 1999; Asikainen and Hirovonen, 2014; Galili & Lehavai, 2006). Student

misconceptions about scientific principles have been documented in all science subjects,

(American Association for the Advancement of Science [AAAS], 1993). Gönen (2008) found

that “regardless of students’ level of schooling, misconceptions are prevalent and resistant” (p.

79) in his study of 267 pre-service science and physics teacher’s understanding of mass and

gravity as determined by open-ended physical science questions. Similarly, Galili & Lehavai’s

(2006) study of high school physics teachers’ ability to define physics concept resulted in

inadequate definitions which is indicative of misconceptions although not conclusive.

Developed misconceptions are resistant to change; students revert back to previously held

notions even when presented with contradicting evidence. Allen and Coole (2012) studied 47

pre-service teachers in England and determined that when a student is presented with content that

disagrees with his or her own understanding, the student will first rely on his or her own

understanding before accepting the new scientific process indicated by pretest and posttest of

physical science concepts. Furthermore, after treatment to correct specific misconceptions, some

participants returned to prior held misconceptions after six weeks, dropping overall post-test

corrections. Secondary physics classes fail to make lasting impact on student understanding of

physical concepts (Viennot, 1979; Solomon, 1983; Driver and Oldham, 1986).

14

Teacher Knowledge

Teacher knowledge is multifaceted and more complex than simply knowing the content

being presented. Teachers must understand the content and relate it to students in engaging and

meaningful ways. As previously stated, this study will distinguish between two types of teacher

knowledge, content knowledge referred to as CK and pedagogical content knowledge referred to

as PCK. The influence of CK and PCK are seen by Sadler’s et al. (2013) study of 181 middle

school physical science teachers and 9,506 middle school students. Students made the highest

gains from teachers with both CK and PCK over teachers with only one of the knowledge types

as determined by pretest and posttest assessment of the physical science standards of the NSES.

Similar, Etkina (2010) argues that a teacher must have “deep content knowledge (p. 020110 2)”

in order to convey conceptual understanding to students in her study of pedagogical practices of

the Rutgers Physics/ Physical Science Teacher Preparation Program. She elaborated that a

teacher must understand the history of a concept, the nuances of the concepts, and the

relationship the concept has with other scientific knowledge to convey meaning to students. She

was careful to state that this breath of knowledge was not a standalone factor, but must be

integrated into pedagogical practice to be effective. Mantyla and Nousiainen (2013) argued for

content knowledge first “because to construct teaching approaches and plans in which content

knowledge is properly organized, the teacher needs to know how the concepts can be introduced

in teaching in a logically justified manner” (p. 1584) in their study of didactic reconstructions of

physical science content to support pre-service physics teachers. Similarly, Abell (2007)

reviewed research in science teacher knowledge determined that both CK and PCK are required

for teacher preparation, but PCK is not well defined nor formally taught. (p. 1115)

15

Student achievement cannot be mastered without teachers possessing both CK and PCK,

but as to which is more influential has not been determined by current research. Deng (2007)

suggested that to understand the nature of secondary science content, the pedagogical and

sociocultural dimensions of the subject are more essential to teaching than knowing the academic

discipline in his study of academic disciplines versus school subject in relation to teacher

knowledge. This in no way means that lack of CK will lead to student success, but that the PCK

is more important. Similarly, Sander et al. (1993) found that when teachers were forced to teach

content outside their knowledge base, expert teachers would overcome knowledge deficits

without intervention but the process would take time. Novice teachers were unable to overcome

the deficit and taught to a rudimentary understanding only.

In determining student achievement in mathematics, Hill et al. (2005) found teacher

content knowledge to be a significant factor in student gains in their study of student

achievement in mathematics in115 elementary schools as indicated by interviews and

achievement testing. Arzi and White’s (2007) longitudinal study of teacher content knowledge in

teachers from pre-service to 17 years of service found that “the absence of university background

cannot be readily compensated for on-the-job textbook learning” (p. 245), and teachers’ content

knowledge does not grow linearly over time. Furthermore, teachers were often asked to teach

outside their content expertise and had persistent difficulties in subjects in which they did not

have sufficient background knowledge. Sadler et al. (2013) had similar findings, where teachers

had expertise in specific areas, but overall subject knowledge was fragmented. This

fragmentation left holes that affected student achievement of particular concepts in the same

areas.

16

Misconceptions in Teachers

With teacher CK fragmented and misconceptions being resistant to change, it is

reasonable to expect misconceptions to persist in the teacher population. Teacher misconceptions

in fundamental science concepts were first documented in the late 1930’s (Ralya & Ralya, 1938).

A shift in educational research turned attention away from teacher CK and focused on PCK until

the late 1980s (Abell, 2007). Trumper (1999) studied 25 pre-service physics teachers in Israel.

He determined that 76 percent of the physics students in the pre-service teacher training program

still maintained their prior misconceptions about forces after completing a four year program.

Berg and Brouwer’s (1991) study of 20 senior high school physics teachers found that “over one

third of the teachers held one or more alternate conception themselves” (p. 16) by open-ended

questionnaire. Hashweh (1987) found similar results in his study of content knowledge in six

experienced science teachers. He found that “almost every teacher had pre-conceptions or

knowledge inaccuracies” (p. 112) as illustrated by a free response questionnaire. Ginns and

Watters’ (1995) study of 321 pre-service elementary teachers indicated “that many prospective

elementary teachers demonstrate a range of inaccurate scientific concepts in the areas of science

that form important components of elementary science curriculum” (p. 219) as seen by an open-

ended survey. Kruger, Palacio, and Summers studied England elementary science teacher’s

physical knowledge extensively after the implementation of the 1988 Education Act (Kruger, et

al., 1990; Kruger, et al. 1992; Kruger, Summers, & Palacio, 1990). They also found a significant

amount of teacher misconceptions within elementary in-service science teacher population

regardless to any secondary physics schooling.

17

Teacher and student misconceptions are often similar. Burgoon et al. (2009) found that

elementary science teachers demonstrated the same misconceptions held by students about

gravity, magnetism, and temperature. Similarly, Cochran and Jones (1998) determined

elementary teachers have similar concepts about physical phenomena as those held by primary

students in their review of research on the nature and development of subject matter knowledge

of pre-service teachers. Sadler et al. (2013) found that when a middle school teacher did not

know the science content, he or she most likely selected the dominant student misconception as

correct. Kruger et al. 1990 study of 20 elementary in-service teachers in England reasoned that

the misuse of scientific language by teachers observed correlates with the same “undifferentiated

ideas in children” (p. 394) as indicated by in depth interviews. Kind’s (2014) study of 265 United

Kingdom pre-service teachers’ chemistry science knowledge indicated that pre-service teachers’

misconceptions about chemistry matched those of high school students determined by a 28

question survey.

Although formal education does diminish teacher misconceptions, it does not eradicate

them with certainty. Allen and Coole (2012) suggested that primary teachers’ misconceptions

remain dominant due to a lack of formal scientific education. However Gönen (2008) found both

physics and general science teachers could not explain specific concepts, even after studying

those concepts in undergraduate programs. Kind (2014) determined that a science degree was

“not enough to correct teacher misconceptions about chemistry” (p. 1336). Kind and Kind (2011)

study of 150 pre-service teachers found prevailing chemical misconceptions from a chemical

concept questionnaire. They noted that the teachers studied were designated as well-qualified

and “almost all succeeded in becoming science teachers” (p. 2149). Trumper (1999) found that

18

pre-service physics teachers do not abandon their original physical science misconceptions about

physical misconceptions, even after four years of physical science study. Similarly, Yip et al.

(1998) found that physics majors “significantly outperformed teachers in other disciplines, [but]

their performance was by no means satisfactory” (p. 322). Arzi and White (2007) found that as

teachers continued to teach, formal content knowledge of a subject was forgotten while specific

knowledge to the current curriculum remained, i.e. a middle school science teacher would forget

the college level physics learned but retain physical science material pertinent to middle school

science. Similarly, Asikainen and Hirovonen’s (2014) study of nine pre-service and 18 in-service

physics teachers found similar misconceptions about quantum mechanics examined by paper-

and-pencil test and interviews.

Influence of Teacher Content Knowledge and Teacher Misconception on Student

Achievement

Teacher CK impacts student achievement in dynamic ways. Sadler et al. (2013) found

students scored higher on physical science content questions when a teacher had high CK. In a

related study, Wayne and Young’s (2003) review of research in teacher characteristics and

student achievement gains determined that high school mathematics students learned more from

teachers who have certification in mathematics, degrees related to mathematics, and mathematics

coursework, while student performance in lower grades were inconclusive. Hill et al. (2005)

found students achieved more from teachers with strong mathematical content knowledge.

Teacher misconceptions can negatively affect student achievement. Sadler et al. (2013)

determined students with low mathematical and reading ability made no significant gains if the

19

teacher did not have the required CK. Likewise, Burgoon et al. (2009) suggested teachers who

have the same misconception as their students will be unable to address and correct their own

students’ misunderstandings. Berg and Brouwer (1991) reasoned that some misconceptions had

been passed on to students directly because “teachers expressed frustration over the difficulty in

ridding their students of what they perceived to be incorrect conceptions, which were in fact

correct” (p. 16).

Teacher Profile Education

In the United States, the majority of secondary science teachers’ bachelors’ degrees are in

biology (Hill & Gruber, 2011). According to the American Association of Physics Teachers

[AAPT] (1988), “a teacher of high school physics course should have an undergraduate

preparation in physics, mathematics, and related science equivalent to a physics major” (p.

5). Neuschatz and McFarling (2000) study of the Nationwide Survey of High School Physics

Teachers found 33 percent of the physics teachers majored in physics or physics education, but

most teachers had taken at least one college level physics course. National Center for Education

Statistics (Snyder, deBrey, & Dillow, 2016) national school survey of 2012 found that overall, 79

percent of science teacher had a science degree, but only 46 teaching physics have a degree in

physics and only 38 percent were certified to teach it. The teaching population in the United

Kingdom is similar, with a larger proportion of biology degrees versus chemistry or physics

(Kind & Kind, 2011). Australia also mimics these standards, with 86 percent of senior biology

teachers majoring in biology while only 57 percent of senior physics teachers majoring in

physics as shown by Panizzon, Westwell, & Elliott’s (2010) study of 601 South Australian

20

secondary science teachers’ responses on a six question questionnaire. Kind and Kind (2011)

study of pre-service teacher knowledge of chemical concepts noted that pre-service teachers are

required to teach both within their scientific specialty and outside their scientific specialty (p.

2127). Hill and Gruber (2011) study of teacher certification and qualification in United Stated

public and charter schools found that 57 percent of physics teachers majored in physics while 42

percent majored in another subject.

In Texas, multiple certification options are available to teach sixth grade science, seventh

grade science, eighth grade science, integrated physics and chemistry, or physics (see Table 2.1).

Table 2.1. Types of Certification for each subject

Grade Level or Subject Science certification

including physical

science

Science certification

excluding physical

science

Generalist

or all core

Certification

Sixth Grade Science 15 14 14

Seventh Grade Science 23 23 4

Eighth Grade Science 28 25 4

Integrated Physics and

Chemistry

17 2 0

Physics 18 0 0

*A detailed table of all possible certification can be found in the Appendix B

21

Since 2015, there are 43 different certifications that fulfill requirements to teach sixth grade

science, 51 certifications for seventh grade science, 58 certifications for eighth grade, 19

certifications for integrated physics and chemistry, and 18 certifications for physics (Texas

Education Agency [TEA], 2015). Sixth grade science, seventh grade science, and eighth grade

science teachers can be certified to teach science through a generalist or core subject

certification, which requires an overall passing score of all core subjects and does not require a

specific passing science subject score (TEA, 2015). Also, sixth grade science, seventh grade

science, eighth grade science, and integrated physics and chemistry have certification options

that do not include physical science (TEA, 2015). Harrell (2010) argues that the multiple

pathways to secondary science teacher certification in Texas creates loopholes that allow

teachers to be responsible for a content they are not adequately prepared to teach indicated by her

study of 93 in-service eighth grade science teacher and their transcripts and scores on the 8-12

science TExES diagnostic examination.

22

CHAPTER 3

METHODOLOGY

Research Design

Sampling

Study One

Seven school districts in Dallas- Fort Worth area and two school districts in Houston area

were selected to participate in the study. Two school districts in Dallas- Fort Worth area agreed

to participate and are designated as School District A and School District B. School District A

participated in April and May of 2015. School District A has a student population of 38,600 with

28.3 percent Caucasian, 21.6 percent African American, 40 percent Hispanic or Latino, 6.9

percent Asian, with the remaining population identified as American Indian, Pacific Islander, or

two or more races. Sixth grade is housed in the elementary campuses, middle school houses

seventh and eighth grades, and integrated physics and chemistry and physics is housed in the

high schools. There were 162 potential participants identified as teaching sixth grade, seventh

grade science, eighth grade science, integrated physics and chemistry, or physics. All participants

were contacted three times to participate, once at the beginning of the study, once one week after

the beginning of the study, and once during the final week of the study. Thirty-five participants

responded and 15 participants were included in data analysis. Thirteen participants were

excluded due to an incomplete instrument, five indicated they did not currently teach one of the

science grade levels, and four indicated they either are currently teaching or have taught AP

physics in the last five years. School District B participated in April and May of 2016. School

District B has a student population of 25,500 with 14.3 percent Caucasian, 16.7 percent African

23

American, 56.3 percent Hispanic or Latino, 10 percent Asian, and the remaining population

identifying as American Indian, Pacific Islander, or two or more races. Sixth, seventh, and eighth

grade are housed on the middle school campuses and integrated physics and chemistry and

physics are housed on the high school campuses. A total of 71 potential District B participants

were identified as teaching sixth grade science, seventh grade science, eighth grade science,

integrated physics and chemistry, or physics. All participants were contacted three times to

participate, once at the beginning of the study, once two weeks after the beginning of the study,

and once during the final week of the study. Twenty-seven participants responded and nine

participants were included in data analysis. Nine participants were excluded due to an incomplete

instrument, one indicated he did not currently teach one of the science grade levels, seven

indicated they have been the teacher of record for AP physics in the last five years, and one

indicated he had been the teacher of record for an IB course in the last five years. In total,

responses from 24 participants, 15 from District A plus nine from District B, were included in

data analysis.

Study Two

An optional professional development community is the Texas Regional Collaborative

for Excellence in Science and Mathematics Teaching, referred to as the TRC for the remainder of

this work. The TRC is a collaboration program developed and maintained through The

University of Texas at Austin STEM program. In 2013-2014 school year, the TRC had 30 TRC

Science Collaboratives that were housed primarily at either Texas Educational Region Service

Center offices or universities throughout the state. In order to participate in a Regional Science

24

Collaborative, in-service science teachers in public, charter, or private schools must agree to 100

hours of professional development offered through his or her Collaborative during the 15 month

grant term. Professional development must be approved by the TRC in the individual

Collaborative grant application process, and be aligned to the specific content TEKS identified

by the TRC in consultation with the Texas Education Agency (Texas Regional Collaborative for

Excellence in Science and Mathematics Teaching, 2013). For the 2013-2014 school year, the

TRC focus was physical science, including Newton’s three laws of motion. In 2013-2014 grant

year, there were 753 possible participants identified as teachers of record for sixth grade science,

seventh grade science, eighth grade science, integrated physics and chemistry, or physics

courses. Of the 753 identified participants, 368 participants were included in the study. Of the

participants excluded, 235 participants did not have any pre-test instrument information available

and 150 participants did not complete the pre-test instrument in its entirety.

Instrumentation

Study One

Content Knowledge Instrument One

The Force Concept Inventory (Hestenes, Wells, & Swackhamer, 1992), referred to as the

FCI for the remainder of this study, was administered to identify teacher content knowledge

(Jackson, 2016). The FCI is a vetted instrument to identify student conceptual knowledge of

physical science (Hestenes & Halloun, 1995). Sadler et al. (2013) suggested using similar tests

designed to determine student content knowledge as a more appropriate measure to determine

teacher knowledge than other measures like college course, GPA, degrees, or certifications. The

FCI is used in its entirety. FCI questions that identify conceptual knowledge of Newton’s three

25

laws of motion are extrapolated to identify teacher content knowledge to each law. These

divisions are consistent with Poutot and Blandin (2015) use of the FCI to determine student

misconceptions, with an analysis of the overall concept and not individual scores (see Table 3.1).

Table 3.1. FCI Question Breakdown by Newton’s Laws

Newton’s First Law of Motion 10, 11, 13, 23, 24

Newton’s Second Law of Motion 17, 21, 22, 25, 26, 27, 29

Newton’s Third Law of Motion 4, 15, 16, 28

Demographic Survey One

A demographic survey, referred to as the DS, collected further information for teacher

characteristics comparison. Data was collected on the following characteristics: participant’s sex,

years of service, current teaching position, highest earned degree, undergraduate majors and

minors, type of degree program, and Texas certifications. The DS was modeled after the

National Census survey to convey confidence and accuracy in data collection. The DS is 10

questions in length with multiple choice and free response when appropriate (see Appendix C).

Study Two

Demographic Survey Two

Participants who wish to fully participate in the TRC must complete a participant profile

that identifies the following teacher characteristics: participant’s sex, ethnicity, highest degree

earned, years of service, if the participant has a STEM major, type of certification program, years

26

of participation in the TRC, teaching position for the 2013-2014 school year, grades or subjects

taught by the participant in the 2013-2014 school year, and demographic information about the

student population taught by the participant. The TRC location is also recorded by region. For

this study, the following teacher characteristics were assessed: region, participant’s sex, highest

degree earned, years of service, STEM major, and grades or subjects taught by the participant.

Questions are multiple choice and free response when appropriate (see Appendix D).

Content Knowledge Instrument Two

The TRC requested that Dr. Mary Urquhart of The University of Texas at Dallas, A TRC

Science Collaborative Project Director and physics educator, create a pre/post assessment for

assessing teacher content knowledge of force and motion. Dr. Urquhart recommended the use of

the FCI, but the TRC required the assessment to align to specific middle school physics Texas

Essential Knowledge and Skills [TEKS] identified as low performing on the eighth grade State

of Texas Assessment of Academic Readiness [STAAR] test for the spring 2013 administration

(see Table 3.2).

Table 3.2. TEKS Identified as low performing

Grade Level TEKS

Sixth Grade Science 6.8 A 6.8 C

Eighth Grade Science 8.6 B

For a complete description of the TEKS, see Appendix E

27

Each of the TEKS had been identified by the Texas Education Agency as low performing on the

middle school (eighth grade) science STAAR. Dr. Urquhart created the TRC Physics

Assessment as requested, with the understanding that the two month development time frame did

not allow for validity and reliability testing (Urquhart, M., e-mail, April 4, 2017).

The TRC Physics Assessment is a multiple choice instrument without the option to select

multiple answers to specific questions (see Appendix F for sample questions from the

instrument). The TRC permitted Dr. Urquhart to include short answer explanations of a subset of

multiple choice selections. Only answers to the multiple choice questions were analyzed in this

study. The author created questions in the instrument from her experience with the FCI, the

Force and Motion Conception Evaluation [FMCE] (Thornton & Sokoloff, 1998), and conceptual

questions used in the context of her own Master of Arts in Teaching Conceptual Physics I: Force

and Motion course for think-pair-share or homework assignment (Urquhart, M., e-mail, April 4,

2017). The TRC also requested additional questions that reflected content in the Making Sense

of Science: Force and Motion (Daehler, K. R., Shinohara, M., & Folsom, J., 2011) professional

development course used by most 2013-2014 TRC Science Collaboratives in their Summer

Institutes. Dr. Urquhart vetted the instrument through Master Teachers in the UTeach Dallas

Secondary Science and Mathematics Teacher Preparation program, a local high school physics

teacher, and other UT Dallas physics faculty. At least one content expert affiliated with the TRC

and the TRC staff also vetted the TRC Physics Assessment before administration to the TRC

participants (Urquhart, M, e-mail, April 4, 2017). TRC Physics Assessment questions that

identify conceptual knowledge of physics and Newton’s three laws of motion are extrapolated to

identify teacher content knowledge to each law (see Table 3.3).

28

Table 3.3. TRC Physics Assessment Question Breakdown by Conceptual force

and Newton’s Laws Questions

Conceptual Force

1, 2, 4, 5, 6, 7, 12, 13, 14, 15, 16, 17, 18,

19, 25, 26, 27, 28, 29, 30, 31, 34

Newton’s First Law of Motion 4, 5, 7, 18, 19, 28

Newton’s Second Law of Motion 1, 6, 12, 14, 15, 26, 27, 29

Newton’s Third Law of Motion 13, 16, 17, 25, 30

The TRC administered this test twice, initially as a pre-test before the initiation of the TRC

professional development programs and again as a post-test at closing of the collaborative grant

year. For this study, only pre-test data is analyzed.

Data Analysis

Study One

Participants were awarded one point for each correct answer on the FCI. Overall scores

were averaged to determine individual participant’s scores. Scores were recorded to two

significant figures to correspond with traditional numerical grade point scales. Scores for

Newton’s first, second, and third law were extrapolated and averaged as a subset to determine

individual participants’ scores for each category. Participants’ were also divided into knowledge

groups based on average FCI score. Hestenes and Halloun (1995) divide student scores using the

FCI into three knowledge categories; mastery threshold for scores 85 percent or higher, entry

threshold for scores between 60 percent and 85 percent, and scores below 60 percent outside of

29

these ranges. Using this model, participants who earned an overall average of 85 percent or

higher showed sufficient content knowledge in Newtonian mechanics and the participants mostly

likely have few misconceptions. Participants who earned between 60 percent and 85 percent

showed a general understanding of Newtonian mechanics but the participants mostly likely have

some misconceptions. Participants who earned less than 60 percent have insufficient knowledge

of Newtonian mechanics and most likely have major misconceptions.

Teacher characteristics fall into ten categories: participant’s sex, years of service in

teaching, current school district, current teaching position, highest earned degree, undergraduate

major, undergraduate STEM course work, earned graduate degree, teaching certification type,

and teaching certification subject as identified by the demographic survey one. Participants’

recorded majors and minors were combined to determine undergraduate STEM course work.

Participants’ degree program is excluded due to the wide variety in degree programs offered at

each university. Years of service in teaching are classified as one to five year, six to ten years, or

eleven or more years. Current teaching positions are classified as sixth grade, seventh grade,

eighth grade, and high school. Highest earned degrees are classified as bachelor, masters, and

doctorate. Undergraduate majors are categorized as STEM (science, technology, engineering, or

math), education, or other not listed. Undergraduate STEM course work is categorized as a

major or minor in a STEM field or other. STEM coursework is identified as a field of natural

science, for example veterinarian science, physics, or biology. Teaching certification type is

categorized as traditional or alternative. Teaching certification subject is categorized as general

science, general education, or other not specified. Education coursework is identified as a field of

30

education including science education, for example middle school science education or bilingual

education.

Significance of results is established using a variety of statistical tools. A t-test is applied

for the following teacher characteristics tests versus FCI percentage score to determine a p-value:

participant’s sex, school district, highest earned degree, undergraduate STEM course work, and

teaching certification type. Significance of results is established using an ANOVA test for the

following teacher characteristics tests versus FCI percentage score to determine a p-value: years

of service, current teaching position, undergraduate major, and teaching certification subject (see

Appendix G). A Chi-squared contingency table is applied to teacher characteristics tests versus

knowledge groups to determine a p-value. The null hypothesis in all categories is there is no

relationship between teacher characteristics and FCI score.

An original alpha value of 0.05 is established. However, when calculating large amounts

of statistical tests using the same data, it is more likely to reject the null hypothesis when it is in

fact true (Abdi, 2010). To compensate for this risk, a Holm Bonferroni Sequential Correction

post-hoc statistical test is applied to adjust the p-values to compensate for the large number of

comparisons. The adjusted p-values are then compared against the original alpha to determine

statistical significance. An excel calculator developed by Gaeteno (2013) is used to determine the

adjusted p-value.

Study Two

Participants were awarded one point for each correct answer on the TRC Physics

Assessment. Overall scores were averaged to determine individual participant’s scores. Scores

31

were recorded to two significant figures to correspond with traditional numerical grade point

scales. Scores were extrapolated using only questions about conceptual forces and averages as a

subset to correspond with the makeup of the FCI. Scores for Newton’s first, second, and third

law were extrapolated and averaged as a subset to determine individual participant’s scores for

each category.

Teacher characteristics fall into seven categories: region, participant’s sex, years of

service in teaching, current teaching position, highest earned degree, undergraduate STEM

degree, and certification method as identified by demographic survey two. Region is defined by

the twenty regions established by Texas Education Agency [TEA]. Any TRC Science

Collaborative housed at a college or university was identified by physical location and included

in the appropriate region. Region type is defined as rural, independent, and urban based off

population size provided by Public Education Information Management System [PEIMS] (Public

Education Information Management System, 2017) for the 2013-2014 school year. Rural regions

have less than 100,000 students, independent regions have at least 100,000 but less than 300,000

students, and urban regions have at least 300,000 students. Years of service in teaching are

classified as zero to four years, five to nine years, ten to fourteen years, fifteen to nineteen years,

twenty to twenty four years, twenty five to twenty nine years, or thirty to thirty five years.

Current teaching positions are classified as sixth grade, seventh grade, eighth grade, middle

school science, integrated physics and chemistry, physics, two or more high school subjects, two

or middle school subject, or both middle and high school subjects. Middle school subjects are

defined as sixth grade science, seventh grade science, and eighth grade science. High school

subjects are defined as integrated physics and chemistry and physics. Highest earned degrees are

32

classified as bachelor, masters, and doctorate. Teaching certification type is categorized as

traditional and alternative.

Significance of results is established using a variety of statistical tools. A t-test is applied

for the following teacher characteristic tests versus TRC instrument percentage score to

determine a p-value: participant’s sex, highest earned degree, undergraduate STEM major, and

teaching certification type. Significance of results is established using an ANOVA test for the

following teacher characteristic tests versus TRC instrument percentage score to determine a p-

value: region, years of service, and current teaching position (see Appendix H). The null

hypothesis in all categories is there is no relationship between demographic information and

TRC instrument score. As with Study 1, an original alpha value of 0.05 is established and a

Holm Bonferroni Sequential Correction (Abdi, 2010) is applied to adjust the p-values to

compensate for the large number of comparisons. An excel calculator is used to determine the

adjusted p-value (Gaeteno, 2013).

33

CHAPTER 4

RESULTS

Study One

The overall average of the FCI is 45 percent with a standard deviation of 0.233 (see Table

4.1).

Table 4.1. Descriptive Statistics for FCI Overall Averages

Count Mean

Standard

Deviation

Standard

Error Mode

Confidence

Level (95.0%) Min Max

24 0.450 0.233 0.0476 0.2 0.0985 0.130 1.00

The most common score is 20 percent with a range of 13 percent to 100 percent. The frequency

of scores is skewed towards the right (see Figure 4.1), which shows that more participants scored

below the threshold frequency for general knowledge at 60 percent.

The percentage of participants who scored in the mastery knowledge threshold is four percent,

the percentage of participants who scored in the general knowledge threshold is 29 percent, and

Figure 4.1. FCI distribution for overall averages

0

1

2

3

4

0

0.0

5

0.1

0.1

5

0.2

0.2

5

0.3

0.3

5

0.4

0.4

5

0.5

0.5

5

0.6

0.6

5

0.7

0.7

5

0.8

0.8

5

0.9

0.9

5 1

Fre

qu

en

cy

Average

34

the percentage of participants who scored in the insufficient knowledge threshold is 67 percent

(see Table 4.2).

Table 4.2. FCI Overall Averages by Knowledge Threshold

Threshold

Count

Mastery (85% or greater) 1 (4%)

General (Between 60% and 85%) 7 (29%)

Insufficient (60% or below) 16 (67%)

Numbers in parentheses indicate column percentages

The average for Newton’s first law questions is 51 percent with a standard deviation of

0.22 (see Table 4.3).

Table 4.3. Descriptive Statistics for FCI Newton’s First Law Questions

Count Mean

Standard

Deviation

Standard

Error

Mode

Confidence

Level (95.0%)

Min Max

24 0.508 0.221 0.0450 0.400 0.0931 0.200 1.00

The most common score is 40 percent with a range of 20 percent to 100 percent. The frequency


below the threshold frequency for general knowledge at 60 percent. The percentage of

participants who scored in the mastery knowledge threshold is eight percent, the percentage of

participants who scored in the general knowledge threshold is 33 percent, and the percentage of

participants who scored in the insufficient knowledge threshold is 58 percent (see Table 4.4).

35

Table 4.4. FCI Newton’s First Law Questions by Knowledge Threshold

Knowledge Threshold

Count





The average for Newton’s second law questions is 32 percent with a standard deviation of 0.27

(see Table 4.5).

Table 4.5. Descriptive Statistics for FCI Newton’s Second Law Questions

Count Mean

Standard

Deviation

Standard

Error

Mode

Confidence

Level (95.0%)

Min Max

24 0.321 0.267 0.0546 0.286 0.113 0.00 1.00

The most common score is 29 percent with a range of zero percent to 100 percent. The frequency


below the threshold frequency for general knowledge at 65 percent.

Figure 4.2. FCI distribution for Newton’s First Law Questions

0

2

4

6

8

10

12

0 0.2 0.4 0.6 0.8 1

Fre

qu

en

cy

Average

36

The percentage of participants who scored in the mastery knowledge threshold is eight percent,

the percentage of participants who scored in the general knowledge threshold is four percent, and


(see Table 4.6). More participants scored in the insufficient threshold for Newton’s Second Law

than any other category.

Table 4.6. FCI Newton’s Second Law Questions by Knowledge Threshold

Knowledge Threshold

Count





The average for Newton’s third law questions is 43 percent with a standard deviation of

0.034 (see Table 4.7). The higher standard deviation indicated the scores vary moreso than other

categories. The most common score is 25 percent with a range of zero percent to 100 percent.

Figure 4.3. FCI distribution for Newton’s Second Law Questions

0

5

10

15

0 0.14 0.29 0.43 0.67 0.71 0.86 1

Fre

qu

en

cy

Average

37

Table 4.7. Descriptive Statistics for FCI Newton’s Third Law Questions

Count Mean

Standard

Deviation

Standard

Error

Mode

Confidence

Level (95.0%)

Min Max

24 0.427 0.342 0.0697 0.250 0.144 0.00 1.00

The frequency of scores is skewed towards the right and more evenly distributed (see Figure

4.4), which supports that scores varied more so than other categories.

The percentage of participants who scored in the master knowledge threshold is 17 percent, the

percentage of participants who scored in the general knowledge threshold is eight percent, and


(see Table 4.8). More participants scored in the mastery and general knowledge threshold for

Newton’s third law than any other law.

Figure 4.4. FCI distribution for Newton’s Third Law Questions

0

2

4

6

8

0 0.25 0.5 0.75 1

Fre

qu

en

cy

Average

38

Table 4.8. FCI Newton’s Third Law Questions by Knowledge Threshold

Knowledge Threshold

Count





The following statistical tests proved significant after the Holm Bonferroni Sequential

Correction: Overall FCI vs Grades Taught ANOVA, Newton’s third Law vs Grades Taught

ANOVA, and Newton’s Third Law vs Grades Taught Chi-squared Continencey Table.

A one-way between subjects ANOVA was conducted to compare the effect of grade level

taught on FCI overall average in sixth grade, seventh grade, eighth grade, and high school

conditions. There was a significant effect of grade level taught on FCI overall average at the p

<.05 level and adjusted by Holm-Bonferroni Sequential Correction for the four conditions [F (3,

12) = 11.9, p = 0.00107, p’ = 1.63E-6] (see Table 4.9).

Table 4.9. ANOVA of Grade Level FCI Overall Averages

Grade level taught

Sum of

Squares df

Mean

Square F Significance

Between Groups 0.800 3 0.267 11.9 0.000107

Within Groups 0.447 20 0.0223

Total 1.25 23

39

Post hoc comparisons using the Tukey post-hoc test indicated that the mean score for High

school (M=0.792, SD = 0.14) is significantly different from the mean score for sixth grade

(M=0.253, SD = 0.12), the mean score for high school is also significantly different from the

mean score for seventh grade (M=0.338, SD = 0.13), and is significantly different from the mean

score for eighth grade (M=0.524, SD = 0.186). Eighth grade is significantly different from mean

score for sixth grade. However, there is no significant difference in means between sixth grade

and seventh grade or seventh grade and eighth grade (see Table 4.10).

Table 4.10. Descriptive Statistics of Grade Level FCI Overall Averages

N Mean

Standard

Deviation

Standard

Error

Confidence

Interval 95.0% Min Max

Sixth Grade 5 0.253 0.122 0.0544 0.151 0.133 0.400

Seventh Grade 8 0.338 0.131 0.0465 0.110 0.200 0.600

Eighth Grade 7 0.524 0.186 0.0704 0.172 0.200 0.767

High School 4 0.792 0.140 0.0699 0.222 0.700 1.00

Taken together, grade level taught does correlate with overall force knowledge in reference to

the FCI. Specifically, teachers of high school in this sample outperformed teachers of sixth

grade, seventh grade, or eighth grade. Within sixth, seventh, and eighth grade, teachers of eighth

grade outperform teachers of sixth grade, but there is no significant difference between other

grade levels.

A one-way between subjects ANOVA was conducted to compare the effect of grade level

taught on FCI Newton’s third Law average in sixth grade, seventh grade, eighth grade, and high

40

school conditions. There was a significant effect of grade level taught on FCI third law average

at the p <.05 level and adjusted by Holm-Bonferroni Sequential Correction for the four

conditions [F (3, 20) = 37.2, p = 2.27E-8, p’ = 2.27E-8] (see Table 4.11). Post hoc comparisons

using the Tukey post-hoc test indicated that the mean score for high school (M=1.00, SD = 0.00)

is significantly different from the mean score for sixth grade (M=0.05, SD = 0.11), the mean

Table 4.11. ANOVA of Grade Level Taught for Newton's Third Law Question of the

FCI

Grade Level taught

Sum of

Squares df

Mean


Between Groups 2.28 3 0.759 37.2 2.27E-8


Total 2.68 23

score for high school is also significantly different from the mean score for seventh grade

(M=0.281, SD = 0.16), and is significantly different from the mean score for eighth grade

(M=0.536, SD = 0.172). Eighth grade is significantly different from mean score for sixth grade

and eighth grade is significantly different from the mean score for seventh grade. However, there

is no significant difference in means between sixth grade and seventh grade (see Table 4.12).

Taken together, grade level taught in this sample does correlate with knowledge of Newton’s

third law in reference to the FCI. Specifically, teachers of high school in this study outperformed

teachers of sixth grade, seventh grade, or eighth grade. Within sixth, seventh, and eighth grade,

teachers of eighth grade outperformed teachers of sixth grade and seventh grade, but there is no

significant difference between other grade levels.

41

Table 4.12. Descriptive Statistics of Grade Level Taught for Newton's Third Law

Question of the FCI

N Mean

Standard

Deviation

Standard

Error

Confidence

Interval 95.0% Min Max

Sixth Grade 5 0.0500 0.112 0.0500 0.139 0.000 0.250

Seventh Grade 8 0.281 0.160 0.0566 0.134 0.00 0.500

Eighth Grade 7 0.536 0.173 0.0652 0.160 0.25 0.750

High School 4 1.00 0.00 0.00 0.00 1.00 1.00

A Chi-square test was conducted to determine the effect of grade level taught on

Newton’s third Law threshold knowledge in reference to the FCI. Chi-square results show a

statistically significant difference in threshold knowledge among the four grade levels, χ2 (6) =

1.00E-4, adjusted 0.00177 (see Table 4.13).

Table 4.13. Results of Chi-square Test and Descriptive Statistics for Knowledge

Threshold by Grade level taught for FCI Newton's Third Law

Grade Level Taught

Threshold

Sixth Grade Seventh Grade Eighth Grade High School

Mastery

(x >85%)

0 (0%) 0 (0%) 0 (0%) 1 (25%)

General

(85% > x > 60%)

0 (0%) 0 (0%) 2 (29%) 3 (75%)

Insufficient

(60% > x) 5 (100%) 8 (100%) 5 (71%) 0 (0%)

Note, χ2 = 0.0001*, adjusted =0.001775, df =6. Numbers in parentheses indicate column

percentages. *p < 0.05

42

Teachers of high school in this study are more likely to be at the general to mastery

knowledge threshold, teachers of eighth grade are more likely to be at the insufficient to general

knowledge threshold, and teachers of sixth and seventh grade are more likely to be at the

insufficient knowledge threshold.

The following statistical tests appeared to be significant by the calculated p-values, but

become non-significant after adjustment: Overall vs. Grades Taught Contingency table, Overall

vs. STEM certification ANOVA, Newton’s First Law vs. Years of Service ANOVA, Newton’s

First Law vs STEM coursework t-Test, Newton’s Second Law vs. Participants’ Sex t-Test,

Newton’s Second Law vs Education t-Test, and Newton’s Third law vs STEM coursework t-Test

(see Table 4.14).

Table 4.14. FCI Statistical Test with adjusted p-values > 0.05

Test Calculated p-value Adjusted p-value

Overall vs. Grades Taught Contingency 0.00100 0.0690

Overall vs. STEM Certification ANOVA 0.0341 1.00

Newton’s First Law vs. Years of Service ANOVA 0.0282 1.00

Newton’s First Law vs. STEM course work t-Test 0.0214 1.00

Newton’s Second Law vs Participants’ Sex t-Test 0.0141 0.946

Newton’s Second Law vs Education t-Test 0.0473 1.000

Newton’s Third Law vs STEM coursework t-Test 0.0114 0.774

All other statistical test calculated p-values were non-significant (see Appendix I).

43

Study Two

The overall average of the TRC Physics Assessment is 44 percent with a standard error of

0.008 (see Table 4.15).

The most common score is 44 percent with a range of nine percent to 91 percent. The frequency

of scores is slightly skewed to the right (see Figure 4.5), which shows that more participants

scored below 50 percent.

The average for conceptual force questions of TRC Physics Assessment is 47 percent

with a standard error of 0.008 (see Table 4.16). The most common score is 50 percent with a

range of nine percent to 95 percent. The frequency of scores is centered (see Figure 4.6), which

shows that scores are normally distributed.

Table 4.15. Descriptive Statistics for TRC Physics Assessment Overall

Average N Mean

Standard

Error Mode

Standard

Deviation

Confidence

Level 95.0% Min Max

368 0.444 0.00751 0.440 0.144 0.0148 0.0900 0.910

0

10

20

30

40

50

0

0.0

5

0.1

0.1

5

0.2

0.2

5

0.3

0.3

5

0.4

0.4

5

0.5

0.5

5

0.6

0.6

5

0.7

0.7

5

0.8

0.8

5

0.9

0.9

5 1

Fre

qu

en

cy

Average

Figure 4.5. TRC Physics Assessment distribution for overall averages

44

Table 4.16. Descriptive Statistics for TRC Physics Assessment Conceptual Force

Questions

Average N Mean

Standard

Error

Mode

Standard

Deviation

Confidence

Level (95.0%)

Min Max

368 0.468 0.00824 0.500 0.158 0.0162 0.0900 0.950

The average for Newton’s first law questions of TRC Physics Assessment is 47 percent

with a standard error of 0.011 (see Table 4.17).

Table 4.17. Descriptive Statistics for TRC Physics Assessment Newton's First law

Questions

Average N Mean

Standard

Error Mode

Standard

Deviation

Confidence


368 0.472 0.0118 0.333 0.225 0.0231 0.00 1.00

The most common score is 33 percent with a range of zero percent to 100 percent. The frequency

of scores is skewed to the right (see Figure 4.7), which shows that more participants scored

below 50 percent.

0

10

20

30

40

50

60

0

0.0

4

0.0

8

0.1

2

0.1

6

0.2

0.2

4

0.2

8

0.3

2

0.3

6

0.4

0.4

4

0.4

8

0.5

2

0.5

6

0.6

0.6

4

0.6

8

0.7

2

0.7

6

0.8

0.8

4

0.8

8

0.9

2

0.9

6 1

Fre

qu

en

cy

Average

Figure 4.6. TRC Physics Assessment distribution for Conceptual Force Questions

45

The average for Newton’s second law questions of TRC Physics Assessment is 52

percent with a standard error of 0.011 (see Table 4.18).

Table 4.18. Descriptive Statistics for TRC Physics Assessment Newton's Second Law

Questions

Average N Mean

Standard

Error Mode

Standard

Deviation

Confidence


368 0.517 0.0112 0.500 0.215 0.0220 0.00 1.00

Newton’s second law questions have the highest average of all categories. The most common

score is 50 percent with a range of zero percent to 100 percent. The frequency of scores is

centered (see Figure 4.8), which shows that the scores are normally distributed.

0

50

100

150

0 0.167 0.33 0.5 0.667 0.83 1

Fre

qu

en

cy

Average

Figure 4.7. TRC Physics Assessment distribution for Newton’s First Law

Questions

46

The average for Newton’s third law questions of TRC Physics Assessment is 31 percent

with a standard error of 0.013 (see Table 4.19).

Table 4.19. Descriptive Statistics for TRC Physics Assessment Newton's Third Law

Questions

Average N Mean

Standard

Error Mode

Standard

Deviation

Confidence


368 0.311 0.0133 0.200 0.255 0.0261 0.00 1.00

Newton’s third law questions have the lowest average of all categories. The most common score

is 20 percent with a range of zero percent to 100 percent. The frequency of scores is skewed to

the right (see Figure 4.9), which shows that more participants scored below 50 percent.

0

20

40

60

80

100

0 0.125 0.25 0.375 0.5 0.625 0.75 0.875 1

Fre

qu

en

cy

Average

Figure 4.8. TRC Physics Assessment distribution for Newton’s Second Law Questions

47

The following statistical test proved significant after the Holm Bonferroni Sequential

Correction: Overall vs. Region, Overall vs. Region type, Overall vs. Participants’ Sex, Overall vs

STEM major, Overall vs. Middle School/High School, Overall vs. Eighth Grade/High School,

Overall vs. Grades Taught, Conceptual force questions vs. Region, Conceptual force questions

vs. Participants’ Sex, Conceptual force question vs. STEM major, Conceptual force questions vs.

Middle School/High School, Conceptual force questions vs. Eighth Grade/High School,

Conceptual force questions vs. Grades Taught, Newton’s First Law vs. STEM, Newton’s First

Law vs. Middle School/ High School, Newton’s First Law vs. Grades taught, Newton’s Second

Law vs. Middle School/High School, Newton’s Second Law vs. Eighth Grade/High School,

Newton’s Second Law vs. Grades Taught, Newton’s Third Law vs. Rural School District , and

Newton’s Third Law vs. Participants’ Sex.

A one-way between subjects ANOVA was conducted to compare the effect of region on

TRC Physics Assessment overall average in Region One, Region Two, Region Three, Region

Four, Region Five, Region Six, Region Seven, Region Eight, Region 10, Region 11, Region 12,

Region 13, Region 14, Region 15, Region 16, Region 17, and Region 19 conditions. There was a

0

50

100

150

0 0.2 0.4 0.6 0.8 1

Fre

qu

en

cy

Average

Figure 4.9. TRC Physics Assessment distribution for Newton’s Third Law Questions

48

significant effect of Region on TRC Physics Assessment overall average at the p <.05 level and

adjusted by Holm-Bonferroni Sequential Correction for the seventeen conditions [F (16,351) =

2.77, p = 3.21E-4, p’ = 0.0151] (see Table 4.20).

Table 4.20. ANOVA of Region for TRC Physics Assessment Overall Average

Region

Sum of

Squares df

Mean


Between Groups 0.842147 16 0.052634 2.772607 0.000321429

Within Groups 6.66326 351 0.018984

Total 7.505407 367

Post hoc comparisons using the Tukey post-hoc test indicated that the mean score for Region

five (M=0.677, SD = 0) is significantly different from the mean scores for all other regions.

However, the standard deviation of zero shows a possibility of duplicate answers. Region 10

(M=0.549, SD = 0.19) is significantly different from the mean scores for Region One (M=.412,

SD = 0.10), Region Two (M=0.411, SD = 0.12), Region Three (M=0.412, SD = N/A), Region

Seven (M=0.386, SD =0.14), Region 13 (M=0.382, SD = N/A), and Region 19 (M=0.412, SD =

0.08). However, there is no significant difference in means between any remaining regions (see

Table 4.21). Taken together, region does correlate with overall force knowledge in reference to

the TRC Physics Assessment. Specifically, teachers in Region 10 in this sample have more

physics knowledge than teachers in Region One, Region Two, Region Three, Region Seven,

Region 13, and Region 13. However, there is no significant difference between other regions.

49

Table 4.21. Descriptive Statistics of Regions for TRC Physics Assessment Overall

Averages

N Mean

Standard

Deviation

Standard

Error

Confidence

Interval 95.0%

Min Max

Region One

30 0.412 0.102 0.0187

0.0383 0.235

0.676

Region Two

24 0.411 0.121 0.0248 0.0512 0.147

0.706

Region Three 1 0.412 0 0.412 0.412

Region Four 47 0.476 0.168 0.0246 0.0495 0.147

0.853

Region Five 2 0.676 0 0 0 0.676

0.676

Region Six 32 0.455 0.121 0.0217 0.0436 0.235

0.735

Region Seven 61 0.386 0.144 0.0184 0.0369 0.0882

0.794

Region Eight 15 0.445 0.151 0.0389 0.0835 0.235

0.794

Region 10 31 0.549 0.190 0.0341 0.0696

0.265 0.912

Region 11 7 0.471 0.0720 0.0272 0.0666

0.353 0.559

Region 12 26 0.426 0.117 0.0230 0.0473

0.176

0.676

Region 13 1 0.382 0 0.382

0.382

Region 14 24 0.429 0.130 0.0266 0.0549

0.206 0.676

Region 15 20 0.425 0.0961 0.0215 0.0450 0.294

0.706

Region 16 19 0.489 0.129 0.0297 0.0624 0.324 0.794

Region 17 25 0.459 0.124 0.0247 0.0510

0.235

0.765

Region 19 3 0.412 0.0778 0.0449 0.193

0.353 0.500

A one-way between subjects ANOVA was conducted to compare the effect of region

type on TRC Physics Assessment overall average in rural, independent, and urban conditions.

50

There was a significant effect of Region on TRC Physics Assessment overall average at the p

<.05 level and adjusted by Holm-Bonferroni Sequential Correction for the three conditions [F

(2,365) = 7.08, p = 9.65E-4, p’ = 0.0425] (see Table 4.22).

Table 4.22. ANOVA of Region Type for TRC Physics Assessment Overall Average

Region Type

Sum of

Squares

df Mean Square F Significance

Between Groups 0.280 2 0.140 7.08 9.65E-04


Total 7.51 367

Post hoc comparisons using the Tukey post-hoc test indicated that the mean score for urban

condition (M=0.478, SD = 0.16) is significantly different from the mean scores for independent

condition (M=0.453, SD = 0.13) (see Table 4.23).

Table 4.23. Descriptive Statistics of Region Type for TRC Physics Assessment Overall

Averages

N Mean

Standard

Deviation

Standard

Error

Confidence

Interval 95.0%

Min Max

Rural 106 0.453 0.128 0.0124 0.0246 0.206 0.794

Independent 146 0.413 0.131 0.0109 0.0215 0.0882 0.794

Urban 116 0.478 0.162 0.0150 0.0298 0.147 0.9121

Total 368 0.444 0.144 0.00751 0.0148 0.0900 0.910

51

There is no significant difference in means between any other conditions. Region type has a

limited correlation with physics knowledge in reference to the TRC Physics Assessment overall.

Those teachers who teach in urban regions in this sample have more physics knowledge than

teachers who teach in independent regions. However, there is no significant effect for teachers

who teach in rural regions.

An independent-samples t-test was conducted to compare the TRC Physics Assessment

overall averages in male and female conditions. There was a significant difference in scores for

males (M=0.500, SD =0.16) and females (M=0.430, SD=0.13) conditions after Holm-Bonferroni

correction; t (108) = -3.488, p = 3.53E-4, p’ = 0.0162 (see Table 4.24 and 4.25).

Table 4.24. Descriptive Statistics of Participants’ Sex for TRC Physics Assessment

Overall Average

Sex N Mean

Standard

Deviation Standard Error

Female 289 0.430 0.133 0.00784

Male 79 0.500 0.164 0.0184

Table 4.25. t-Test: Two-Sample Assuming Unequal Variances of Sex for TRC Physics

Assessment Overall

Sex N t df Significance (1-Tailed)

Female 289 -3.49 108 0.000353

Male 79

52

These results suggest that participant’s sex in this sample correlates with physics knowledge in

reference to the TRC Physics Assessment. Specifically, male teachers in this sample have more

physics knowledge than female teachers.

An independent-samples t-test was conducted to compare the TRC Physics Assessment

overall averages in STEM major and non-STEM major conditions. There was a significant

difference in scores for STEM major (M=0.472, SD =0.15) and non-STEM major (M=0.418,

SD=0.14) conditions after Holm-Bonferroni correction, t (348) = -3.599, p = 1.83E-4, p’ =

0.00915 (see Table 4.26 and 4.27).

Table 4.26. Descriptive Statistics of STEM Major for TRC Physics Assessment

Overall

Stem Degree N Mean Standard Deviation Standard Error

Stem Major 191 0.472 0.146 0.0106

Non-Stem Major 162 0.418 0.136 0.0107

Table 4.27. t-Test: Two-Sample Assuming Unequal Variances of STEM Major for

TRC Physics Assessment Overall

Stem Degree N t df

Significance

(1-Tailed)

Stem Major 191 3.60 348 1.83E-04

Non-Stem Major 162

53

STEM degree correlates with physics knowledge in reference to the TRC Physics Assessment

overall averages. STEM majors in this sample have more physics knowledge than non-STEM

majors, although both averages are below 50 percent.

A one-way between subjects ANOVA was conducted to compare the effect of grades

taught on TRC Physics Assessment overall average in middle school science, high school

science, and both middle school and high school science condition. There was a significant effect

of grades taught on TRC Physics Assessment overall average at the p <.05 level and adjusted by

Holm-Bonferroni Sequential Correction for the three conditions [F (2,365) = 19.23, p =1 .15E-8,

p’ = 6.67E-7] (see Table 4.28).

Table 4.28. ANOVA of Grades Taught (MS vs HS) for TRC Physics Assessment Overall

Averages

Grades taught

(MS vs HS) Sum of Squares df

Mean


Between Groups 0.715 2 0.356 19.2 1.15E-08


Total 7.51 367

Post hoc comparisons using the Tukey post-hoc test indicated that the mean score for high school

science condition (M=0.535, SD = 0.17) is significantly different from the mean scores for

middle school science condition (M=0.422, SD = 0.12), and both middle school and high school

science condition (M=0.459, SD=0.15). The mean score for both middle school and high school

science condition is significantly different from the mean scores for middle school science

condition (see Table 4.29).

54

Table 4.29. Descriptive Statistics of Grades Taught (MS vs HS) for TRC Physics

Assessment Overall Averages

Grades

Taught

(MS vs HS) N Mean

Standard

Deviation

Standard

Error

Confidence

Level

(95.0%) Minimum Maximum

MS 279 0.422 0.125 0.00747 0.0147 0.147 0.853

HS 69 0.535 0.172 0.0207 0.0413 0.0882 0.912

Both 20 0.459 0.155 0.0346 0.0723 0.0882 0.794

Taken together, grade level taught in this sample correlates with physics knowledge in reference

to the TRC Physics Assessment overall averages. High school teachers in this sample are

statistically more knowledgeable than other subjects, and combined middle and high school

teachers are statically more knowledgeable than middle school science teachers. This suggests

that teaching at least one high school science course is related to increased teacher physical

science knowledge.


taught on TRC Physics Assessment overall average eighth grade science, high school science,

and both eighth grade and high school science conditions. There was a significant effect of

grades taught on TRC Physics Assessment overall average at the p <.05 level and adjusted by

Holm-Bonferroni Sequential Correction for the three conditions [F (2,198) = 10.07, p = 6.86E-5,

p’ = 0.00357] (see Table 4.30).

55

Table 4.30. ANOVA of Grades Taught (Eighth Grade vs HS) for TRC Physics

Assessment Overall Averages

Grades Taught

(8th

vs HS)

Sum of

Squares df

Mean of

Squares F Significance

Between Groups 0.455 2 0.227 10.1 6.86E-05


Total 4.93 200


science (M=0.535, SD = 0.17) is significantly different from the mean scores for eighth grade

science condition (M=0.433, SD = 0.32), and both eighth grade and high school science

condition (M=0.467, SD=0.17). There is no statistical difference between Eighth grade science

and both eighth grade and high school science conditions (see Table 4.31).

Table 4.31. Descriptive Statistics of Grades Taught (Eighth Grade vs HS) for TRC

Physics Assessment Overall Averages

N Mean

Standard

Deviation

Standard

Error

Confidence

Level

(95.0%) Minimum Maximum

8th 117 0.433 0.132 0.0122 0.0243 0.176 0.765

High 69 0.535 0.172 0.0207 0.0413 0.0882 0.912

Both 15 0.467 0.175 0.0451 0.0967 0.0882 0.794

56

Grade level taught correlates with physics knowledge in reference to the TRC Physics

Assessment overall averages. High school teachers in this sample have more physics knowledge

than eighth grade science teacher, even those eighth grade science teachers who also teach at

least one high school science course. This suggests that teaching only high school is related to an

increase in physics knowledge over teaching eighth grade science.


taught on TRC Physics Assessment overall average in sixth grade, seventh grade, eighth grade,

middle school science (MSS), integrated physics and chemistry (IPC), physics, two or more

middle school science subject, two or more high school science subjects, and both middle school

and high school science subjects. There was a significant effect of grades taught on TRC Physics

Assessment overall average at the p <.05 level and adjusted by Holm-Bonferroni Sequential

Correction for the nine conditions [F (8,359) = 7.35, p = 4.57E-9, p’ = 2.74E-7] (see Table 4.32).

Table 4.32. ANOVA of Grades Taught for TRC Physics Assessment Overall Average

Grades taught

Sum of

Squares

df

Mean

Square

F Significance

Between Groups 1.06 8 0.132 7.35 4.58E-09


Total 7.51 367

Post hoc comparisons using the Tukey post-hoc test indicated that the mean score for physics

(M=0.574, SD = 0.17) is significantly different from the mean scores for sixth grade (M=0.414,

SD = 0.14), seventh grade (M=0.443, SD=0.11), eighth grade (M=0.438, SD = 0.13), middle

school science (M=0.407, SD=0.11), integrated physics and chemistry (M=0.385, SD = 0.17),

57

two or more middle school sciences (M=0.412, SD= 0.13), and both middle school and high

school science (M=0.489, SD=0.15). The mean score for two high school sciences (M=0.534,

SD = 0.13) is significantly different from the mean scores for sixth grade, seventh grade, eighth

grade, middle school science, integrated physics and chemistry, and two or more middle school

sciences. There is no significant difference in means between any other conditions (see Table

4.33).

Table 4.33. Descriptive Statistics of Grades Taught for TRC Physics Assessment

Overall Averages

N Mean

Standard

Deviation

Standard

Error

Confidence


Sixth 45 0.414 0.139 0.0207 0.0417 0.206 0.853

Seventh 23 0.442 0.114 0.0237 0.0491 0.235 0.735

Eighth 84 0.438 0.132 0.0144 0.0287 0.235 0.765

MSS 86 0.407 0.107 0.0115 0.0229 0.176 0.676

IPC 10 0.385 0.170 0.0538 0.122 0.0882 0.676

Physics 39 0.574 0.172 0.0276 0.0559 0.294 0.912

2+ MSS 41 0.412 0.134 0.0210 0.0424 0.147 0.706

2 HSS 20 0.534 0.133 0.0230 0.0621 0.265 0.794

MSS+HSS 20 0.459 0.155 0.0346 0.07234 0.0882 0.794

Grade level taught in this sample correlates with physics knowledge in reference to the TRC

Physics Assessment overall averages. Taken together, physics teachers in this sample are

statistically more knowledgeable than other subjects, including integrated physics and chemistry

58

teachers. Teachers who are responsible for both high school science courses, physics and

integrated physics and chemistry are more knowledgeable than other subjects. There is no

statistical difference between physics only teachers’ knowledge and teachers responsible for

physics and integrated physics and chemistry, suggesting that teaching at least one physics

course is related to an increase in teacher physical science knowledge. However, there is no

statistical difference in knowledge for middle school science teachers or integrated physics and

chemistry teachers.

A one-way between subjects ANOVA was conducted to compare the effect of region on

the conceptual force questions of the TRC Physics Assessment in Region One, Region Two,

Region Three, Region Four, Region Five, Region Six, Region Seven, Region Eight, Region 10,

Region 11, Region 12, Region 13, Region 14, Region 15, Region 16, Region 17, and Region 19

conditions. There was a significant effect of Region on TRC Physics Assessment overall average

at the p <.05 level and adjusted by Holm-Bonferroni Sequential Correction for the seventeen

conditions [F (16,351) = 2.50, p = 0.00121, p’ = 0.0484] (see Table 4.34).

Table 4.34. ANOVA of Region for TRC Physics Assessment Conceptual Force

Questions

Region

Sum of

Squares

df

Mean

Square

F Significance

Between Groups 0.940 16 0.0587 2.503808 0.00121


Total 9.17 367

59

Post hoc comparisons using the Tukey post-hoc test indicated that the mean score for Region

five (M=0.773, SD = 0) is significantly different from the mean scores for all other regions.

However, the standard deviation of zero shows a possibility of duplicate answers. Region 10

(M=0.569, SD = 0.20) is significantly different from the mean scores for Region One (M=0.411,

SD = 0.12), Region Three (M=0.409, SD = N/A), Region Seven (M=0.415, SD =0.16), Region

13 (M=0.409, SD = N/A), and Region 19 (M=0.409, SD = 0.091). There is no significant

difference in means between any remaining regions (see Table 4.35).

Table 4.35. Descriptive Statistics of Region for TRC Physics Assessment Conceptual

Force Questions

N Mean Standard

Deviation

Standard

Error

Confidence

Interval 95.0% Minimum Maximum

Region One 30 0.411 0.115 0.0210 0.0429 0.227 0.773

Region Two 24 0.432 0.145 0.0296 0.0612 0.182 0.818

Region Three 1 0.409 0 0.409 0.409

Region Four 47 0.508 0.181 0.0265 0.0533 0.182 0.864

Region Five 2 0.773 0 0 0 0.773 0.773

Region Six 32 0.476 0.134 0.0237 0.0484 0.182 0.773

Region Seven 61 0.415 0.164 0.0209 0.0419 0.0909 0.864

Region Eight 15 0.461 0.165 0.0425 0.0912 0.227 0.818

Region 10 31 0.569 0.204 0.0366 0.0747 0.227 0.955

Region 11 7 0.494 0.110 0.0414 0.101 0.273 0.591

Region 12 26 0.458 0.144 0.0282 0.0580 0.136 0.773

Region 13 1 0.409 0 0.409 0.409

Region 14 24 0.456 0.136 0.0278 0.0576 0.182 0.773

Region 15 20 0.455 0.112 0.0251 0.0526 0.318 0.773

Region 16 19 0.502 0.136 0.0312 0.0655 0.318 0.864

Region 17 25 0.484 0.139 0.0278 0.0573 0.273 0.773

Region 19 3 0.409 0.0909 0.0525 0.226 0.318 0.500

60

Taken together, region in this sample does correlate with conceptual force knowledge in

reference to the TRC Physics Assessment. Specifically, teachers in Region 10 have more physics

knowledge than teachers in Region One, Region Three, Region Seven, Region 13, and Region

19. However, there is no significant difference between other regions.

An independent-samples t-test was conducted to compare the conceptual force questions

from the TRC Physics Assessment in male and female conditions. There was a significant

difference in scores for male (M=0.531, SD =0.18) and female (M=0.451, SD =0.15) conditions

after Holm-Bonferroni correction, t (107) = -3.58, p = 2.56E-4, p’ = 0.0123 (see Table 4.36 and

4.37).


Conceptual Force Questions

Sex N Mean Standard Deviation Standard Error

Female 289 0.451 0.146 0.00859

Male 79 0.531 0.183 0.0206

Table 4.37. t-Test: Two-Sample Assuming Unequal Variances of Sex for TRC Physics

Assessment Conceptual Force Questions

Sex N t df Significance (1-

Tailed)

Female 289 -3.58 107 0.000256

Male 79

61

Participant’s sex correlates with conceptual physics knowledge in reference to the TRC Physics

Assessment conceptual force questions. Male teachers in this sample have more conceptual

physics knowledge than female teachers.

An independent-samples t-test was conducted to compare the conceptual force questions

from the TRC Physics Assessment in STEM major and non-STEM major conditions. There was

a significant difference in scores for STEM major (M=0.500, SD =0.16) and non-STEM major

(M=0.437, SD =0.15) conditions after Holm-Bonferroni correction, t (345) = 3.75, p = 1.04E-4,

p’ = 0.00531 (see Table 4.38 and 4.39).

Table 4.38. Descriptive Statistics of STEM Degree for TRC Physics Assessment


STEM Degree N Mean Standard Deviation Standard Error

STEM Major 191 0.500 0.159 0.0115

Non-STEM major 162 0.437 0.154 0.0121

Table 4.39. t-Test: Two-Sample Assuming Unequal Variances of STEM Degree for

TRC Physics Assessment Conceptual Force Questions

STEM Degree N t df

Significance

(1-Tailed)

STEM Major 191 3.75 345 0.000104

Non-STEM major 162

62

STEM degree correlates with conceptual physics knowledge in reference to the TRC Physics

Assessment conceptual force questions. Teachers with STEM majors in this sample have more

conceptual physics science knowledge than teachers with non-STEM majors.


taught on conceptual physics questions of the TRC Physics Assessment in middle school science,

high school science, and both middle school and high school science conditions. There was a

significant effect of grades taught on TRC Physics Assessment conceptual physics average at the

p <.05 level and adjusted by Holm-Bonferroni Sequential Correction for the three conditions [F

(2,365) = 18.3, p = 2.67E-8, p’ = 1.52E-6] (see Table 4.40).

Table 4.40. ANOVA of Grades Taught (MS vs Hs) for TRC Physics Assessment


Grades taught

MS vs HS

Sum of

Squares

df

Mean

Square

F Significance

Between Groups 0.836 2 0.418 18.3 2.67E-08


Total 9.17 367




science condition (M=0.486, SD=0.15). The mean score for both middle school and high school

63

science condition is significantly different from the mean scores for middle school science

condition (see Table 4.41).



Grades

Taught

(MS vs HS)

N Mean Standard

Deviation

Standard

Error

Confidence

Level

(95.0%)

Minimum Maximum

MS 279 0.443 0.141 0.00845 0.0166 0.0909 0.864

HS 69 0.565 0.186 0.0224 0.0448 0.0909 0.955

Both 20 0.486 0.152 0.0340 0.0711 0.136 0.773

Taken together, grade level taught in this sample correlates with conceptual physics knowledge

in reference to the TRC Physics Assessment overall averages. High school teachers in this

sample statistically demonstrated more physics knowledge in the areas assessed than middle

school science teachers or combined middle school and high school science teachers. Combined

middle and high school teachers showed more physics knowledge in the areas assessed than

middle school science teachers. This suggests that teaching at least one high school science

course correlates with increased teacher conceptual physics knowledge.


taught on the conceptual force questions of the TRC Physics Assessment overall average eighth

grade science, high school science, and both eighth grade and high school science conditions.

There was a significant effect of grades taught on TRC Physics Assessment conceptual force

64

questions average at the p <.05 level and adjusted by Holm-Bonferroni Sequential Correction for

the three conditions [F (2,198 ) = 11.3, p = 2.26E-5, p’ = 0.00120] (see Table 4.42).

Table 4.42. ANOVA of Grades Taught (Eighth vs HS) for TRC Physics Assessment


Grades Taught

(8th vs HS)

Sum of

Squares

df

Mean of

Squares

F Significance

Between Groups 0.605 2 0.303 11.3 2.26E-05


Total 5.91 200




condition (M=0.488, SD=0.16) (see Table 4.43).

Table 4.43. Descriptive Statistics of Grades Taught (Eighth vs HS) for TRC Physics


N Mean

Standard

Deviation

Standard

Error

Confidence

Level (95.0%)

Minimum Maximum

8th 117 0.447 0.149 0.0137 0.0272 0.0909 0.818

High 69 0.565 0.186 0.0224 0.0448 0.0909 0.955

Both 15 0.488 0.164 0.0424 0.0910 0.136 0.773

65

Grade level taught correlates with conceptual physics knowledge in reference to the TRC

Physics Assessment overall averages. High school teachers in this sample have more physics

content knowledge in the areas assessed than do eighth grade science teachers and science

teachers who teach both eighth grade science and high school science. This suggests that

teaching only high school science courses correlated with greater conceptual physics content

knowledge in comparison with teaching eighth grade science.


taught on the conceptual physics questions of the TRC Physics Assessment in sixth grade,

seventh grade, eighth grade, middle school science (MSS), integrated physics and chemistry

(IPC), physics, two or more middle school science subject, two or more high school science

subjects, and both middle school and high school science subjects. There was a significant effect

of grades taught on TRC Physics Assessment conceptual physics question average at the p <.05

level and adjusted by Holm-Bonferroni Sequential Correction for the nine conditions [F (8,359)=

7.07, p = 1.11E-8, p’ = 6.57E-7] (see Table 4.44).

Table 4.44. ANOVA of Grades Taught for TRC Physics Assessment Conceptual

Force Questions

Grades taught

Sum of

Squares

df

Mean

Square

F Significance

Between Groups 1.25 8 0.156 7.07 1.11E-08


Total 9.17 367

66






school science (M=0.486, SD=0.15). The mean score for two high school sciences (M=0.552,

SD = 0.16) is significantly different from the mean scores for sixth grade, middle school science,

integrated physics and chemistry, and two or more middle school sciences. There is no

significant difference in means between any other conditions (see Table 4.45).



N Mean Standard

Deviation

Standard

Error

Confidence


Sixth 45 0.443 0.152 0.0226 0.0456 0.227 0.864

Seventh 23 0.458 0.140 0.0293 0.0607 0.227 0.773

Eighth 84 0.456 0.142 0.0155 0.0309 0.227 0.818

MSS 86 0.434 0.125 0.0135 0.0269 0.136 0.773

IPC 10 0.400 0.184 0.0582 0.132 0.0909 0.773

Physics 39 0.614 0.177 0.0284 0.0575 0.318 0.955

2+ MSS 41 0.424 0.161 0.0251 0.0507 0.0909 0.773

2 HSS 20 0.552 0.161 0.0360 0.0754 0.227 0.864

MSS+HSS 20 0.486 0.152 0.0340 0.0711 0.136 0.773

67

Grade level taught correlates with conceptual physics knowledge in reference to the TRC

Physics Assessment conceptual physics question averages. Taken together, Physics teachers in

this sample demonstrated better understanding in the area assessed than teachers of other

subjects, including integrated physics and chemistry and teachers responsible for both middle

school science and high school science courses. Teachers who are responsible for physics and

integrated physics and chemistry demonstrated better understanding in the area assessed than

other subjects except for seventh grade and eighth grade. However, there is no statistical

difference in conceptual physics knowledge for middle school science teachers or integrated

physics and chemistry teachers. This suggests that teaching at least one physics course in a high

school only setting is related to increased conceptual knowledge of physics.

An independent-samples t-test was conducted to compare Newton’s first law questions

from the TRC Physics Assessment in STEM major and non-STEM major conditions. There was

a significant difference in scores for STEM major (M = 0.507, SD = 0.23) and non-STEM major

(M = 0.433, SD = 0.22) conditions after Holm-Bonferroni correction, t (346) = 3.087,

p=0.00109, p’=0.0448 (see Table 4.46 and 4.47).

Table 4.46. Descriptive Statistics of STEM Degree for TRC Physics Assessment

Newton's First Law Questions

STEM Degree N Mean Standard

Deviation Standard Error

STEM Major 191 0.505 0.229 0.0166

Non-STEM major 162 0.433 0.219 0.0172

68

Table 4.47. t-Test: Two-Sample Assuming Unequal Variances of STEM Degree for TRC

Physics Assessment Newton's First Law Questions

STEM Degree N t df Significance (1-Tailed)

STEM Major 191 3.09 346 0.00109

Non-STEM major 162

STEM degree correlates with understanding of Newton’s first law in reference to the TRC

Physics Assessment Newton’s first law questions. Teachers who were STEM majors in this

sample demonstrated greater understanding of Newton’s first law than teachers who were non-

STEM majors.


taught on Newton’s first law questions of the TRC Physics Assessment in middle school science,


significant effect of grades taught on TRC Physics Assessment Newton’s first law questions

average at the p <.05 level and adjusted by Holm-Bonferroni Sequential Correction for the three

conditions [F (2,365) = 8.633, p = 2.17E-4, p’ = 0.0106] (see Table 4.48). Post hoc comparisons

using the Tukey post-hoc test indicated that the mean score for high school science condition

(M=0.570, SD = 0.25) is significantly different from the mean scores for middle school science

condition (M=0.447, SD = 0.21), and both middle school and high school science condition

(M=0.483, SD=0.26). There is no significant difference in mean scores for middle school science

condition and both middle school and high school science condition (see Table 4.49).

69

Table 4.48. ANOVA of Grades Taught (MS vs HS) for TRC Physics Assessment

Newton's First Law Questions

Grades taught (MS

vs HS)

Sum of Squares df

Mean

Square

F Significance

Between Groups 0.843 2 0.421 8.63 0.000217


Total 18.7 367


Assessment Newton's First Law Questions

Grades taught

(MS vs HS)

N Mean

Standard

Deviation

Standard

Error

Confidence

Level (95.0%)

Min Max

MS 279 0.447 0.210 0.0126 0.0249 0.00 1.00

HS 69 0.570 0.250 0.0300 0.0600 0.167 1.00

Both 20 0.483 0.259 0.0579 0.121 0.00 1.00

Taken together, grade level taught in this sample correlates with understanding of Newton’s first

law in reference to the TRC Physics Assessment Newton’s first law questions average. High

school teachers demonstrated a better understanding of Newton’s first law than teachers of other

grade levels.


taught on Newton’s first law questions of the TRC Physics Assessment in sixth grade, seventh

70

grade, eighth grade, middle school science (MSS), integrated physics and chemistry (IPC),

physics, two or more middle school science subject, two or more high school science subjects,

and both middle school and high school science subjects. There was a significant effect of grades

taught on TRC Physics Assessment Newton’s first law questions average at the p <.05 level and

adjusted by Holm-Bonferroni Sequential Correction for the nine conditions [F (8,359) = 3.35, p

= 0.00103, p’ = 0.0433] (see Table 4.50).

Table 4.50. ANOVA of Grades Taught for TRC Physics Assessment Newton’s First Law

Questions

Grades taught

Sum of

Squares


Between Groups 1.30 8 0.162 3.35 0.00103


Total 18.7 367



SD = 0.22), seventh grade (M=0.413, SD=0.21), middle school science (M=0.436, SD=0.20),

integrated physics and chemistry (M=0.433, SD = 0.22), and two or more middle school sciences

(M=0.423, SD= 0.18). There is no significant difference in means between any other conditions

(see Table 4.51).Grade level taught correlates with understanding on Newton’s first law in

reference to the TRC Physics Assessment Newton’s first law question averages in this sample.

Taken together, physics teachers in this sample have statistically better understanding of

71


Newton’s First Law Questions

N Mean

Standard

Deviation

Standard

Error

Confidence

Level (95.0%)

Min Max

Sixth 45 0.444 0.219 0.0327 0.0658 0.167 1.00

Seventh 23 0.413 0.212 0.0443 0.0919 0.167 0.833

Eighth 84 0.480 0.219 0.0239 0.0474 0.167 1.00

MSS 86 0.436 0.203 0.0219 0.0436 0.00 1.00

IPC 10 0.433 0.225 0.0711 0.161 0.167 0.833

Physics 39 0.620 0.256 0.0410 0.0831 0.167 1.00

2+ MSS 41 0.423 0.198 0.0309 0.0624 0.00 0.833

2 HSS 20 0.542 0.229 0.0511 0.107 0.167 0.833

MSS+HSS 20 0.483 0.259 0.0579 0.121 0.00 1.00

Newton’s first law than integrated physic and chemistry teachers, sixth grade science teacher,

seventh grade science teacher, and teacher who identified themselves as responsible for middle

school science or two or more middle school science courses. However, there is no statistical

difference in understanding of Newton’s first law for teachers of both middle and high school

science or teachers of both high school science courses. This suggests that teaching only high

school science correlates with an increase in teacher understanding of Newton’s first Law in very

specific conditions.

72


taught on Newton’s first law questions of the TRC Physics Assessment in middle school science,


significant effect of grades taught on TRC Physics Assessment Newton’s second law questions

average at the p <.05 level and adjusted by Holm-Bonferroni Sequential Correction for the three

conditions [F (2,365) = 13.5, p = 2.11E-6, p’ = 1.16E-4] (see Table 4.52).

Table 4.52. ANOVA of Grades Taught (MS vs HS) for TRC Physics Assessment

Newton's Second Law Questions

Grades taught

(MS vs HS)

Sum of

Squares

df

Mean

Square

F Significance

Between Groups 1.17 2 0.584 13.5 2.11E-06


Total 16.9 367




science condition (M=0.538, SD=0.23) There is significantly different mean score for both

middle school and high school condition compared to the middle school condition (see Table

4.53).

73


Assessment Newton's Second Law Questions

Grades taught

(MS vs HS)

N Mean

Standard

Deviation

Standard

Error

Confidence

Level (95.0%)

Min Max

MS 27

9

0.487 0.204 0.0122 0.0240 0.00 1.00

HS 69 0.632 0.216 0.0260 0.0520 0.125 1.00

Both 20 0.538 0.233 0.0522 0.109 0.00 0.875

Taken together, grade level taught correlates with understanding of Newton’s second law in

reference to the TRC Physics Assessment Newton’s second Law questions average. High school

teachers in this sample statistically understand Newton’s second law better than other subjects

and teachers of both high school and middle school science statistically understand Newton’s

second law better than middle school science. This suggests that teaching at least one high school

course correlates with an increase in teacher understanding of Newton’s second law.


taught on the Newton’s second Law questions of the TRC Physics Assessment overall average

eighth grade science, high school science, and both eighth grade and high school science

conditions. There was a significant effect of grades taught on TRC Physics Assessment

Newton’s second law questions average at the p <.05 level and adjusted by Holm-Bonferroni

Sequential Correction for the three conditions [F(2,198) = 11.4, p = 2.12E-5, p’ = 0.00112] (see

Table 4.54).

74

Table 4.54. ANOVA of Grades Taught (Eighth vs HS) for TRC Physics Assessment


Grades Taught

(8th vs HS)

Sum of

Squares

df

Mean of

Squares

F Significance

Between Groups 1.04 2 0.521 11.4 2.12E-05


Total 10.1 200




condition (M=0.55, SD=0.21). The mean score for both eighth grade and high school science

condition is significantly different from the mean score for eighth grade condition (see Table

4.55).

Table 4.55. Descriptive Statistics of Grades Taught (Eighth vs HS) for TRC Physics

Assessment Newton's Second Law Questions

N Mean

Standard

Deviation

Standard

Error

Confidence Level

(95.0%)

Min Max

8th 117 0.478 0.213 0.0197 0.0390 0.00 1.00

High 69 0.632 0.216 0.0260 0.0520 0.125 1.00

Both 15 0.550 0.210 0.0543 0.116 0.00 0.875

75

Taken together, grade level taught does correlate with understanding of Newton’s second law in

reference to the TRC Physics Assessment Newton’s second law questions averages. High school

teachers in this sample have a better understanding of Newton’s second law than eighth grade

science teacher, including those high school science teachers who also teach at least eighth grade

science course. This suggests that teaching at least one high school science course correlates with

an increase in teacher understanding of Newton’s second law over teaching eighth grade science

only.


taught on Newton’s second Law questions of the TRC Physics Assessment in sixth grade,

seventh grade, eighth grade, middle school science (MSS), integrated physics and chemistry

(IPC), physics, two or more middle school science subject, two or more high school science

subjects, and both middle school and high school science subjects. There was a significant effect

of grades taught on TRC Physics Assessment Newton’s second law questions average at the p

<.05 level and adjusted by Holm-Bonferroni Sequential Correction for the nine conditions [F

(8,359) = 5.59, p = 1.10E-6, p’ = 6.160E-5] (see Table 4.56).

Table 4.56. ANOVA of Grades Taught for TRC Physics Assessment Newton's Second

Law Questions

Grades taught

Sum of

Squares


Between Groups 1.87 8 0.234 5.59 1.10E-06


Total 16.9 367

76






school science (M=0.538, SD=0.23). The mean score for two high school science (M=0.606, SD

= 0.22) is significantly different from the mean score for integrated physics and chemistry and

two or more middle school sciences. There is no significant difference in means between any

other conditions (see Table 4.57).



N Mean

Standard

Deviation

Standard

Error

Confidence

Level (95.0%)

Min Max

Sixth 45 0.486 0.225 0.0335 0.0676 0.00 0.875

Seventh 23 0.516 0.218 0.0454 0.0941 0.125 0.875

Eighth 84 0.494 0.206 0.0225 0.0447 0.00 1.00

MSS 86 0.493 0.177 0.0191 0.0380 0.125 0.875

IPC 10 0.425 0.147 0.0464 0.105 0.125 0.625

Physics 39 0.699 0.198 0.0317 0.0642 0.250 1.00

2+ MSS 41 0.448 0.222 0.0346 0.0700 0.00 0.875

2 HSS 20 0.606 0.216 0.0482 0.101 0.250 1.00

MSS+HSS 20 0.538 0.233 0.0522 0.110 0.00 0.875

77

Grade level taught correlates with understanding on Newton’s second law in reference to the

TRC Physics Assessment Newton’s second law questions averages. Taken together, physics

teachers in this sample have a statistically better understanding of Newton’s second law than

sixth grade, seventh grade, eighth grade, integrated physics and chemistry, and teachers

responsible for two or more subject courses. Teachers who teach integrated physic and chemistry

and physics have a statistically better understanding of Newton’s second law than teachers who

only teach integrated physics and chemistry and teacher who teach more than two middle school

sciences. This suggests teaching only physics correlates with an increase in teacher

understanding of Newton’s second law while teaching at least one physics course in high school

has a limited impact.

A one-way between subjects ANOVA was conducted to compare the effect of rural

region on Newton’s third Law questions of the TRC Physics Assessment in Region Three,

Region Five, Region Eight, Region 14, Region 15, Region 16, and Region 17 conditions. There

was a significant effect of grades taught on TRC Physics Assessment Newton’s third law

questions average at the p <.05 level and adjusted by Holm-Bonferroni Sequential Correction for

the nine conditions [F (6, 99) = 4.394, p = 5.60E-4, p’ = 0.0252] (see Table 4.58). Post hoc

comparisons using the Tukey post-hoc test indicated that the mean score for Region Five

(M=1.000, SD = 0.00) is significantly different from the mean scores for all other regions.

However, a standard deviation of zero suggests a duplication of scores. There is no significant

difference in means between any other conditions (see Table 4.59).

78

Table 4.58. ANOVA of Rural Regions for TRC Physics Assessment Newton's Third

Law Questions

Rural Regions

Sum of

Squares

df

Mean

Square

F Significance

Between Groups 1.15 6 0.191 4.39 5.60E-04


Total 5.45 105

Table 4.59. Descriptive Statistics of Rural Regions for TRC Physics Assessment Newton's

Third Law Questions

N Mean

Standard

Deviation

Standard

Error

Confidence

Level (95.0%)

Min Max

Region Three 1 0.2 0 0.20 0.20

Region Five 2 1 0 0 0 1.00 1.00

Region Eight 15 0.333 0.289 0.0747 0.160 0.00 1.00

Region 14 24 0.267 0.152 0.0311 0.0643 0.00 0.60

Region 15 20 0.25 0.193 0.0432 0.0905 0.00 0.60

Region 16 19 0.316 0.234 0.0537 0.113 0.00 0.80

Region 17 25 0.248 0.194 0.0388 0.0800 0.00 0.60

An independent-samples t-test was conducted to compare the Newton’s third law

questions from the TRC Physics Assessment in male and female conditions. There was a

significant difference in scores for male (M=0.403, SD =0.30) and female (M=0.287, SD =0.24)

79

conditions after Holm-Bonferroni correction, t (105) = -3.17, p = 0.00100, p’= 0.0431 (see Table

4.60 and 4.61).


Newton’s Third Law Questions

Sex N Mean Standard Deviation Standard Error

Female 289 0.287 0.235 0.0138

Male 79 0.403 0.301 0.0339

Table 4.61. t-Test: Two-Sample Assuming Unequal Variances of Sex for TRC

Physics Assessment Newton’s Third Law Questions

Sex N t df

Significance (1-

Tailed)

Female 289 -3.17 105 0.00100

Male 79

In this sample, participant’s sex correlates with understanding of Newton’s third law in reference

to the TRC Physics Assessment Newton’s third law questions. Male teachers have more

understanding of Newton’s third law than female teachers although both scored below 50

percent.

The following statistical tests appeared to be significant by the calculated p-values, but become

non-significant after adjustment: Overall vs Urban Regions, Overall vs Education, Force

Conceptual Only vs Region, Force Conceptual Only vs Rural/Urban, Force Conceptual Only vs

80

Urban Regions, Newton’s first Law vs Participants’ Sex, Newton’s first Law vs Eighth

Grade/High School, Newton’s second Law vs Region, Newton’s second Law vs Rural/Urban,

Newton’s second Law vs Urban Regions, Newton’s Second Law vs Participants’ Sex, Newton’s

Second Law vs STEM Major, Newton’s Third Law vs Region, Newton’s Third Law vs

Rural/Urban, Newton’s Third Law vs Region, Newton’s Third Law vs Grades Taught, and

Newton’s Third Law vs Middle/High School (see Table 4.62). All other tests are statistically

non-significant (see Appendix J). For a complete list of all statistical test results, see Appendix

K and Appendix L.

Table 4.62. TRC Physics Assessment Statistical Tests with adjusted p-values > 0.05

Test Calculated p-value Adjusted p-value

Overall vs. Urban Regions 0.0199 0.577

Overall vs Education 0.0106 0.349

Force Conceptual Only vs Rural/Urban 0.00909 0.300

Force Conceptual Only vs Urban Regions 0.0109 0.339

Newton’s first Law vs Participants’ Sex 0.0223 0.602

Newton’s first Law vs Eighth Grade/High School 0.00827 0.289

Newton’s Second Law vs Region 0.0215 0.601

Newton’s Second Law vs Rural/Urban 0.0439 1.00

81

Newton’s Second Law vs Urban Regions 0.00825 0.289

Newton’s Second Law vs Participants’ Sex 0.00729 0.270

Newton’s Second Law vs STEM Major 0.00484 0.184

Newton’s Third Law vs Region 0.00144 0.0560

Newton’s Third Law vs Rural/Urban 0.0437 1.000

Newton’s Third law vs Grades Taught 0.0158 0.474

Newton’s Third Law vs Middle School/High

School

0.00798 0.287

82

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

Summary of results

The statistical tests for both the FCI and the TRC Physics Assessment have averages

below mastery level, showing the majority of the teachers have misconceptions Newtonian

mechanics (see Figure 5.1).

Figure 5.1. Results Flow Chart

83

Newton’s Third law was found to be the lowest scoring section for both the FCI and TRC, with

the most frequent score of 25 percent for the FCI and 20 percent for the TRC Physics

Assessment. For both studies, grades taught were consistently statistically significant. Teachers

of physics out-performed teachers of other subjects except with regard to the TRC Physics

Assessment third law questions. Grades taught was significant only for overall averages and the

Newton’s third law questions of the FCI. However, for the FCI, the low participant number

meant teachers of physics and teachers of integrated physics and chemistry could not be

distinguished, which is less comparative to the TRC Physics Assessment.

Several teacher characteristics were significant for the TRC Physics Assessment but not

significant for the FCI: region, region type, participant’s sex, and STEM major. Although there

was no significant difference between school districts, participant’s sex, or STEM major for the

FCI, both school districts were part of Region 10, an urban region that had higher overall scores

on the TRC than other regions. Males scored higher than females on the TRC, which is

consistent with current research on gender in STEM fields as explained by Weinburgh (1995)

meta-analysis of gender attitudes in science, but also has a large difference in group size (male =

79; female =289), which could skew the results. The TRC Physics Assessment for third law

question was significant when comparing rural regions. However, this comparative is potentially

misleading. The region that scored significantly higher than other regions had a standard

deviation of zero, which may suggest duplicate responses and therefore not be representative of

the region as a whole.

84

Conclusion

Based on the findings of this study, the majority of Texas science teachers responsible for

teaching physical science have not mastered at least a portion of the physical science content

they are teaching. These findings appear state-wide and cross sex of teacher, location, years of

service, STEM education, and certification. Teachers in this study had particular difficulty with

questions on Newton’s third law of motion.

In this study, teachers of physics demonstrated a better understanding than teachers of

other science courses, but on average, did not reach the mastery level of conceptual knowledge

in Newtonian mechanics according to the FCI and TRC Physics Assessment. The lack of

knowledge corresponds to the Neuschatz and McFarling (2000) findings on the low number of

physics teachers who majored in physics. Without a large number of teachers with physics

degrees in the teacher population, there is a higher likelihood that misconceptions will be

embedded in the curriculum presented to the students (Burgoon et al., 2009).

Middle school teachers were the lowest performing group, suggesting this teacher

population has the most misconceptions. This corresponds with the finding of the Harrell (2010)

study on eighth grade science teachers in Texas, suggesting that the loophole in certification for

middle school science is allowing physical science to be taught by unprepared teachers. A

prevalence of misconceptions among middle school teachers is particularly concerning because

according to the TEKS, sixth grade science introduces force and motion in terms of Newton’s

three laws of motion and eighth grade science continues these concepts which are then tested on

the state assessment (see Appendix M). If these teachers have insufficient knowledge of physical

85

science content, then their students are likely to have major misconceptions that will have to be

addressed in physics before students are able to be successful (Berg & Brouwer, 1991).

Equally concerning is the impact of years of service. There is no indication in this study

that years of service impact overall knowledge of physical science. This suggests that teachers

are continuing to teach physical science with insufficient knowledge of the content. The students

of these teachers continue to build misconceptions year after year, as long as the teacher is active

in his or her career.

Limitations

It is important to note that Study One using the FCI has a small sample size (n=24) which

makes findings suggestive but not conclusive. A possible influencing factor is that teacher

participation in Study One for School Districts A and B was strictly voluntary without provided

incentives. In Study Two, the TRC Physics Assessment did not undergo reliability and validity

testing before implementation due to time constraints in its creation, although it assessed similar

concepts to the FCI. The TRC demographic survey identified participants as either earning a

degree with a STEM major or not, but did not give specific information as to the undergraduate

major. The TRC demographic survey also did not collect information about current teacher

certificates held by each participant. The participants in the TRC are not necessarily

representative of the entire Texas teacher population. The TRC requires 100 hours of

professional development over the school year, which deters some teachers from participating.

The TRC also offers incentives for joining, such as stipends, classroom supplies and resources,

free technology instruments, or scholarship money for graduate coursework. These incentives

86

create a self-selection bias in the sample, particularly with regards to motivation and amount of

prior professional development.

Recommendations

Based on the findings and conclusions in this study, the following recommendations are

offered.

1. Middle school science teacher certification should be limited to a science certification

that includes physical science.

2. Physical science curriculum presented should address specific facets on Newton’s third

law in order to correct teacher misconceptions.

3. Physics teachers can serve as mentors for physical science content knowledge, as they are

the most likely to understand it.

4. Teachers of record for middle school science, integrated physics and chemistry, and

physics should have specialized professional development in Newton’s laws from a

physics education expert to correct teacher misconceptions regardless of years of service.

Future Research

This study was able to show that the majority of science teachers do not understand at

least some of the physical science content they are teaching. However, this study is unable to

indicate what specific misconceptions regarding Newton’s laws of motion this teacher

population has and how best to combat them. Future research into why teachers chose specific

incorrect answers would be insightful for building a professional development to correct teacher

87

knowledge. This study focused on physical science, but similar trends may appear in other

subjects, specifically chemistry and earth science. Some research has suggested similar trends

(Kind, 2014), but these should be explored more thoroughly. Middle school science has the most

certification routes that do not require physical science knowledge, especially in sixth grade. A

study between specific middle school certifications and teacher knowledge needs to researched,

but is beyond the scope of this study.

88

APPENDIX A

TEXAS ESSENTIAL KNOWLEDGE AND SKILLS [TEKS] RELATED TO FORCE

AND MOTION IN MIDDLE SCHOOL AND HIGH SCHOOL

Sixth Grade Science

Grade 6 Strand- 6.4 C Force, motion, and energy.

Energy occurs in two types, potential and kinetic, and can take several forms. Thermal energy

can be transferred by conduction, convection, or radiation. It can also be changed from one

form to another. Students will investigate the relationship between force and motion using a

variety of means, including calculations and measurements.

Knowledge and Skills 6.8 Force, motion and energy

The student knows force and motion are related to potential and kinetic energy. The student is

expected to:

(A) compare and contrast potential and kinetic energy;

(B) identify and describe the changes in position, direction, and speed of an object when acted

upon by unbalanced forces;

(C) calculate average speed using distance and time measurements;

(D) measure and graph changes in motion; and

(E) investigate how inclined planes and pulleys can be used to change the amount of force to

move an object.

Seventh Grade Science

Grade 7 Strand- (C) Force, motion, and energy.

Force, motion, and energy are observed in living systems and the environment in several ways.

Interactions between muscular and skeletal systems allow the body to apply forces and

transform energy both internally and externally. Force and motion can also describe the

direction and growth of seedlings, turgor pressure, and geotropism. Catastrophic events of

weather systems such as hurricanes, floods, and tornadoes can shape and restructure the

environment through the force and motion evident in them. Weathering, erosion, and

deposition occur in environments due to the forces of gravity, wind, ice, and water.

89

Knowledge and Skills 7.7 Force, motion, and energy

Seventh Grade Science Continued

The student knows that there is a relationship among force, motion, and energy. The student is

expected to:

(A) contrast situations where work is done with different amounts of force to situations where

no work is done such as moving a box with a ramp and without a ramp, or standing still;

(C) demonstrate and illustrate forces that affect motion in everyday life such as emergence of

seedlings, turgor pressure, and geotropism.

Eighth Grade Science

Grade 8 Strand- (C) Force, motion, and energy.

Students experiment with the relationship between forces and motion through the study of

Newton's three laws. Students learn how these forces relate to geologic processes and

astronomical phenomena. In addition, students recognize that these laws are evident in

everyday objects and activities. Mathematics is used to calculate speed using distance and time

measurements.

Knowledge and Skills 8.6 Force, motion, and energy.

The student knows that there is a relationship between force, motion, and energy. The student

is expected to:

(A) demonstrate and calculate how unbalanced forces change the speed or direction of an

object's motion;

(B) differentiate between speed, velocity, and acceleration; and

(C) investigate and describe applications of Newton's law of inertia, law of force and

acceleration, and law of action-reaction such as in vehicle restraints, sports activities,

amusement park rides, Earth's tectonic activities, and rocket launches.

Integrated Physics and Chemistry

Knowledge and Skills IPC.4 Science Concepts

The student knows concepts of force and motion evident in everyday life. The student is

expected to:

(A) describe and calculate an object's motion in terms of position, displacement, speed, and

acceleration;

90

Integrated Physics and Chemistry Continues

(B) measure and graph distance and speed as a function of time using moving toys;

(C) investigate how an object's motion changes only when a net force is applied, including

activities and equipment such as toy cars, vehicle restraints, sports activities, and

classroom objects;

(D) assess the relationship between force, mass, and acceleration, noting the relationship is

independent of the nature of the force, using equipment such as dynamic carts, moving

toys, vehicles, and falling objects;

(E) apply the concept of conservation of momentum using action and reaction forces such as

students on skateboards;

(F) describe the gravitational attraction between objects of different masses at different

distances, including satellites; and

(G) examine electrical force as a universal force between any two charged objects and

compare the relative strength of the electrical force and gravitational force.

Physics

Knowledge and Skill Physics (4) Science concepts.

The student knows and applies the laws governing motion in a variety of situations. The

student is expected to:

(A) generate and interpret graphs and charts describing different types of motion, including

the use of real-time technology such as motion detectors or photogates;

(B) describe and analyze motion in one dimension using equations with the concepts of

distance, displacement, speed, average velocity, instantaneous velocity, and acceleration;

(C) analyze and describe accelerated motion in two dimensions using equations, including

projectile and circular examples;

(D) calculate the effect of forces on objects, including the law of inertia, the relationship

between force and acceleration, and the nature of force pairs between objects;

(E) develop and interpret free-body force diagrams; and

(F) identify and describe motion relative to different frames of reference.

Knowledge and Skills Physics (5) Science concepts.

The student knows the nature of forces in the physical world. The student is expected to:

(B) describe and calculate how the magnitude of the gravitational force between two objects

depends on their masses and the distance between their centers;

(C) describe and calculate how the magnitude of the electrical force between two objects

depends on their charges and the distance between them;

(D) identify examples of electric and magnetic forces in everyday life;

(H) describe evidence for and effects of the strong and weak nuclear forces in nature.

91

APPENDIX B

CERTIFICATION REQUIREMENTS FOR SCIENCE BY GRADE LEVEL OR

SUBJECT

Sixth Grade Science


including physical science


excluding physical science

Generalist or all core

Certification

Grades 6-12 or Grades 6-8--

Physical Science

Grades 6-12 or Grades 6-8—

Biology

Bilingual Generalist: Early

Childhood-Grade 6


Physics


Chemistry

Bilingual Generalist: Grades

4-8


Science


Earth Science

Core Subjects: Early

Childhood-Grade 6


Science, Composite


Life/Earth Science

Core Subjects: Grades 4-8

Junior High School or High

School--Physical Science


School--Biology

Elementary--General


School--Physics


School—Chemistry

Elementary--General (Grades

1-6


School—Science


School--Earth Science

Elementary--General (Grades

1-8)


School--Science, Composite


School--Life/Earth Science

Elementary Early Childhood

Education (Prekindergarten-

Grade 6)

Master Science Teacher

(Grades 4-8)


School--Life/Earth Middle-

School Science

Elementary Self-Contained

(Grades 1-8)

92






Certification

Mathematics/Science: Grades

4-8

Secondary Biology (Grades 6-

12)

English as a Second

Language Generalist: Early

Childhood-Grade 6

Science: Grades 4-8 Secondary Chemistry (Grades

6-12)

English as a Second

Language Generalist: Grades

4-8

Secondary Physical Science

(Grades 6-12)

Secondary Earth Science

(Grades 6-12)

Generalist: Early Childhood-

Grade 6

Secondary Physics (Grades

6-12

Secondary Life/Earth Science

(Grades 6-12)

Generalist: Grades 4-8

Secondary Science (Grades

6-12)

Secondary or all-level teacher

certificate plus 18 semester

credit hours in any

combination of sciences

Prekindergarten-Grade 6--

General

Secondary Science,

Composite (Grades 6-12)

Seventh Grade and Eighth Grade Science (Eighth grade only are marked with an *)






Certification

Elementary Physical Science Chemistry: Grades 7-12 Bilingual Generalist:

Grades 4-8

Elementary Physics *Chemistry: Grades 8-12 Core Subjects: Grades 4-8

Elementary Physical Science

(Grades 1-8)

Elementary Biology English as a Second

Language Generalist: Grades

4-8

Elementary Physics (Grades

1-8)

Elementary Chemistry Generalist: Grades 4-8

93






Certification


Physical Science

Elementary Earth Science


Physics

Elementary Life/Earth

Middle-School Science


Science

Elementary Biology (Grades

1-8)


Science, Composite

Elementary Chemistry

(Grades 1-8)


School--Physical Science

Elementary Earth Science

(Grades 1-8)


School—Physics

Elementary Life/Earth

Middle-School Science

(Grades 1-8)


School--Science


Chemistry


School--Science, Composite


Biology


(Grades 4-8)


Earth Science

*Master Science Teacher

(Grades 8-12)


Life/Earth Middle-School

Science

Mathematics/Science: Grades

4-8


School--Biology

Mathematics/Physical

Science/Engineering: Grades

6-12


School--Chemistry

94






Certification

*Mathematics/Physical


8-12

Life Science: Grades 7-12

Physical Science: Grades 6-

12

*Life Science: Grades 8-12

*Physical Science: Grades 8-

12


School--Earth Science

Physics/Mathematics:

Grades 7-12


School--Life/Earth Middle-

School Science

*Physics/Mathematics:

Grades 8-12 (Grade 8 only)

Secondary Chemistry (Grades

6-12)

Science: Grades 4-8 Secondary Earth Science

(Grades 6- Secondary

Life/Earth Science (Grades 6-

12)12)

*Science: Grades 8-12 Elementary teacher certificate

plus 18 semester credit hours

in any combination of sciences

Science: Grades 7-12 Elementary teacher certificate


in any combination of sciences



credit hours in any

combination of sciences.


(Grades 6-12)


6-12)

95






Certification


6-12)



credit hours in any

combination of sciences.

Secondary Science,


Integrated Physics and Chemistry






Certification


Physical Science

Junior High School (Grades

9-10 only) or High School--

Chemistry, if issued prior to

September 1, 1976

Not available for this course


Science


Science, Composite



Physical Science



Physics, if issued prior to

September 1, 1976

Secondary or All-Level

classroom teaching certificate

dated between September 1,

1966, and September 1, 1976,


in a combination of sciences

completed prior to September

1, 1976


9-10 only) or High School—

Science

96






Certification



Science, Composite


(Grades 8-12)



6-12



8-12


12


12

Science: Grades 7-12



(Grades 6-12)


6-12)

Secondary Science,


97

Physics






Certification


Physics

Not available for this course Not available for this course


Science


Science, Composite



Physics.



Science



Science, Composite


(Grades 8-12)



6-12



8-12


12

98






Certification


12

Physics/Mathematics: Grades

7-12

Physics/Mathematics: Grades

8-12




6-12)


6-12)

Secondary Science,


99

APPENDIX C

DEMOGRAPHIC SURVEY FOR USES WITH THE FCI

31. What is Your years of teaching service?

Only include years in which a full academic year is completed as defined by the state of

Texas

32. What is Your current teaching position?

For those in multiple levels, consider the position in which you spend the majority (>50%)

of Your teaching time.

Sixth Grade Science



Integrated Physics and Chemistry (IPC)

Physics

33. What is Your current teaching School District?

Carrollton Farmers Branch ISD Denton ISD

Garland ISD Grand Prairie ISD

Hurst Euless Bedford ISD Mansfield ISD

McKinney ISD

Richardson ISD

34.What is Your highest degree completed?

Bachelors

Masters

Doctorate

34. What is Your completed Master(s)?

100

34. What is Your completed Doctorate(s)?

35. What is Your undergraduate degree?

For those who earned more than on undergraduate degree, please select the most recent.

Bachelor of Science

Bachelor of Arts

Bachelor of Fine Arts

Bachelor of Business Administration

36. Please briefly describe Your undergraduate major(s) for Bachelor of Science.

37. Please briefly describe Your undergraduate minor(s) for Bachelor of Science.

36. Please briefly describe Your undergraduate major(s) for Bachelor of Arts.

37. Please briefly describe Your undergraduate minor(s) for Bachelor of Arts.

36. Please briefly describe Your undergraduate major(s) for Bachelor of Fine Arts.

37. Please briefly describe Your undergraduate minor(s) for Bachelor of Fine Arts.

36. Please briefly describe Your undergraduate major(s) for Bachelor of Business

Administration.

37. Please briefly describe Your undergraduate minor(s) for Bachelor of Business

Administration.

38. What was Your certification program?

Traditional

Alternative

101

39. Which of the following Texas Teaching Certificates do you currently hold in

validation?

Please do not consider any certifications that have expired

Chemistry 712

Chemistry 812

Core Subjects EC6

Core Subjects 48

Generalist EC6

Generalists 48

Life Science 712

Mathematics/ Physical Science/

Engineering 612

Mathematics/ Physical Science/

Engineering 812

Mathematics/ Science 48

Physics 812

Physical Science 612

Physics/ Mathematics 712

Physics/ Mathematics 812

Science (composite) 48



Other Not Listed

40. What is Your sex?

Male

Female

102

APPENDIX D

2016-2017 PARTICIPATION PROFILE PORTAL FOR THE TEXAS REGIONAL

COLLABORATIVES FOR EXCELLENCE IN SCIENCE AND MATHEMATICS

TEACHING

105

APPENDIX E

TEXAS ESSENTIAL KNOWLEDGE AND SKILLS (TEKS) FOR FORCE AND

MOTION IDENTIFIED AS LOW PERFORMING

Assessed on 2012-2013 Science State of Texas Assessments of Academic Readiness (STAAR)

Sixth Grade Science

TEKS Number of Questions Average

6.8 A 1 0.44

6.8 C 1 0.25


TEKS Number of Questions Average*

8.6 A 3 0.71 (0.64, 0.63, 0.87)

8.6 B 1 0.75

8.6 C 3 0.66 (0.80, 0.54, 0.64)

*Averages in parenthesis are for individual questions

See Appendix A for TEKS descriptions

106

APPENDIX F

SAMPLE QUESTIONS FROM THE TRC PHYSICS ASSESSMENT

Copyright 2013

Dr. Mary Urquhart of The University of Texas at Dallas and

Texas Regional Collaborative for Excellence in Science and Mathematics Teaching at

The University of Texas at Austin

All Rights Reserved

107

1) A teacher tosses a ball straight up in a demonstration. The ball rises, then falls back

down to the teacher's hand. At the ball's highest point:

I) The velocity of the ball is equal to zero

II) The ball has a non-zero velocity

III) The acceleration of the ball is equal to zero

IV) The ball has a non-zero acceleration

Which combination is true?

A) I and III

B) I and IV

C) II and III

D) II and IV

108

6) During a baseball game, a bat hits a ball thrown by the pitcher. At the moment of

contact between the ball and the bat:

A) The bat exerts a greater force on the ball because it is swung by the batter to hit the ball.

B) The bat exerts a greater force on the ball because it has more mass than does the ball.

C) The ball exerts a greater force on the bat because the ball is thrown towards the bat.

D) The ball exerts a greater force on the bat because it has less mass than the bat.

E) The ball exerts a force equal to the force the bat exerts on the ball during the collision.

“Explain Your Answer” Short answer requested for this question.

7) During a baseball game, a bat hits a ball thrown by the pitcher. After the hit of the

baseball by the bat, which of the following forces are acting on the ball?

I) The force of gravity

II) The force of the hit

III) The force of air resistance

A) Gravity is the only force that applies

B) Only the force of the hit applies

C) Both the force of gravity and the force of air resistance apply

D) Both the force of the hit and the force of gravity apply

E) All three forces apply

109

9) A rollercoaster car at an amusement park is pulled up to the top of a high hill (not

shown) and then allowed to coast without additional input of energy. A segment of the

rollercoaster has the profile above. Which point in the depicted segment has the maximum

kinetic energy?

A) 1

B) 2

C) 3

D) 4

E) 5

110

12) A spacecraft travels between Earth and the Moon. Halfway through its trip the

spacecraft:

A) Requires a constant force from the rocket engine to continue its motion towards the Moon.

B) Requires a rocket to change its motion from the curved path caused by the Earth's gravity.

C) Requires a rocket to change its motion from the straight path it takes after leaving the

atmosphere.

D) Cannot use its rocket engine to accelerate because it cannot push against the Earth or its

atmosphere.

13) Two teams of students play a tug of war game on a playground. Team 2 has a total

mass 60 kg greater than the mass of Team 1. Team 1 gives the rope a sharp tug. Which is

true of the forces on the rope exerted by Team 1 and Team 2?

A) Team 1 is pulling on the rope, and therefore exerts a greater force on the rope than Team

2.

B) Team 2 has more mass than Team 1, and therefore exerts a greater force on the rope than

Team 1.

C) The force exerted on the rope by Team 2 is equal to the force exerted on the rope by

Team 1.

D) There is too little information provided in this question to determine the relative forces on

the rope.

“Explain Your Answer” Short answer requested for this question.

111

Image from online edition of the 1996 USGS publication “This Dynamic Earth”

16) The Indian Plate is in the process of colliding with the Eurasian Plate. Multiple forces

are involved in this collision, which is producing the Himalayas. Both plates put a colliding

resistive force, FCR, on each other. The magnitude of the force FCR the Indian Plate exerts

on the Eurasian Plate is:

A) Greater than the magnitude of the force FCR exerted by the Eurasian Plate because the

Indian Plate is colliding into the Eurasian Plate.

B) Greater than the magnitude of the force FCR exerted by the Eurasian Plate because the

Indian plate is moving faster than the Eurasian Plate.

C) Less than the magnitude of the force FCR exerted by the Eurasian Plate because the Indian

Plate is thinner than the Eurasian Plate.

D) Less than the magnitude of the force FCR exerted by the Eurasian Plate because the

Eurasian Plate is more massive than the Indian Plate.

E) Equal to the magnitude of the force FCR exerted by the Eurasian Plate because the two

plates are pushing against each other.

112

28) A book sits on a table at rest. In this situation, determine if the following statement is

true or false based on the reasoning given.

Newton's first law of motion (law of inertia) applies because the force of gravity on the book and

the force of the table on the book are balanced.

A) True

B) False

For the complete instrument, please contact the author at [email protected]

113

APPENDIX G

LIST OF ALL STATISTICAL TESTS FOR THE FCI

Overall Average

Participants’ Sex t-Test Participants’ Sex Chi-Squared Contingency Table

Years of Service ANOVA Years of Service Chi-Squared Contingency Table

School District t-Test School District Chi-Squared Contingency Table

Teaching Position ANOVA Teaching Position Chi-Squared Contingency Table

Highest Degree Earned t-Test* Highest Degree Earned Chi-Squared Contingency Table

Undergraduate Degree ANOVA Undergraduate Degree Chi-Squared Contingency Table

Undergraduate STEM Degree t-

Test

Undergraduate STEM Degree Chi-Squared Contingency

Table

Certification Type t-Test Certification Type Chi-Squared Contingency Table

STEM Certification ANOVA STEM Certification Chi-Squared Contingency Table

*No participants indicated they earned a PhD, which required a t-Test over ANOVA

114









Test


Table




Newton’s Second Law Questions




115

Newton’s Second Law Questions Continued





Test


Table










116

Newton’s Third Law Questions Continued



Test


Table




117

APPENDIX H

LIST OF ALL STATISTICAL TESTS FOR THE TRC PHYSICS ASSESSMENT

Overall Average

Region ANOVA Rural Regions ANOVA

Urban Regions ANOVA Region Type ANOVA

Participants’ Sex t-Test Highest Degree Earned ANOVA

Years of Experience ANOVA STEM Major t-Test

Certification t-Test Grades Taught (MS vs HS) ANOVA

Grades Taught (8th

vs HS) ANOVA Grades Taught ANOVA

Conceptual Physics Questions






118

Conceptual Physics Questions Continued

Grades Taught (8th


Newton’s First Law






Grades Taught (8th


Newton’s Second Law





119

Newton’s Second Law Continued


Grades Taught (8th


Newton’s Third Law






Grades Taught (8th


120

APPENDIX I

FCI STATISTICAL TESTS p > 0.05

Test p-value Test p-value

Overall vs Participants’ Sex t-

Test

0.151141808

Overall vs Participants’ Sex

Contingency

0.771

Overall vs Years of Service

ANOVA

0.581854271

Overall vs Years of Service

Contingency

0.278

Overall vs School District t-

Test

0.487328399

Overall vs School District

Contingency

1.000

Overall vs Education t-Test 0.299807658

Overall vs Education

Contingency

0.305

Overall vs Education Major

ANOVA

0.805378922

Overall vs Education Major

Contingency

0.716

Overall vs STEM Coursework

t-Test

0.092618697

Overall vs STEM Coursework

Contingency

0.640

Overall vs Certification Type

t-Test

0.274805211

Overall vs Certification Type

Contingency

0.405

121


Overall vs STEM

Certification Contingency

0.589

Newton’s first Law vs

Participants’ Sex Contingency

0.091

Newton’s First Law vs

Participants’ Sex t-Test

0.298878768

Newton’s First Law vs School

District Contingency

1.000

Newton’s First Law vs School

District t-Test

0.26943558

Newton’s First Law vs Grades

Taught Contingency

0.378

Newton’s First Law vs Grades

Taught ANOVA

0.958246644


Education Contingency

0.249


Education t-Test

0.090926522


Education Major Contingency

1.000


Education Major ANOVA

0.788685491


Certification Type Contingency

0.091


Certification Type t-Test

0.197944699

Newton’s First Law vs STEM


0.516


Certification ANOVA

0.340694511


Coursework Contingency

0.093

122


Newton’s First Law vs Years

of Service Contingency

0.055

Newton’s Second Law vs Years


0.345

Newton’s Second Law vs

Years of Service ANOVA

0.208465927


School District Contingency

0.464


School District t-Test

0.487328399


Grades Taught Contingency

0.481


Grades Taught ANOVA

0.064162169



1.000


Education Major

0.264894005


STEM coursework Contingency

1.000


STEM coursework t-Test

0.262538626 Newton’s Second Law vs

Certification Type Contingency

0.249




STEM Certification

Contingency

0.775


STEM Certification ANOVA



1.000

123




0.249 Newton’s Third Law vs


1.000

Newton’s Third Law vs

Participants’ Sex t-Test

0.340337002 Newton’s Third Law vs Years


0.572

Newton’s Third Law vs Years

of Service ANOVA

0.553938166 Newton’s Third Law vs School

District Contingency

0.355


School District t-Test



0.805


Education t-Test



0.620


Education Major ANOVA



0.625



0.249486278 Newton’s Third Law vs STEM


0.538


STEM Certification ANOVA

0.093144364 Newton’s Third Law vs STEM

Coursework Contingency

0.093

124

APPENDIX J

TRC PHYSICS ASSESSMENT STATISTICAL TESTS p > 0.05


Overall vs. Rural Region

ANOVA

0.134071

Overall vs. Years of Service

ANOVA

0.55326

Overall vs. Certification Type

t-Test

0.198837

Conceptual Force Questions vs.

Rural Region ANOVA

0.080875


vs. Education ANOVA

0.056842

Conceptual Force Questions vs.

Years of Service ANOVA

0.640938


vs. Certification Type t-Test

0.351631

Newton’s First Law Questions vs.

Region ANOVA

0.080607


vs. Region Type ANOVA

0.069392


Rural Region ANOVA

0.140535


vs. Urban Region ANOVA

0.091649


Education ANOVA

0.055521


vs. Years of Service ANOVA

0.847927


Certification t-Test

0.227304

125



Questions vs. Rural Region

0.964052


vs. Education ANOVA

0.195153


Questions vs. Years of Service

0.121158


vs. Certification t-Test

0.454098


vs. Urban Region ANOVA

0.073973

Newton’s Third Law Questions vs.

Education ANOVA

0.37308


vs. Years of Service ANOVA

0.548742


STEM Degree

0.180255


vs. Certification Type t-Test

0.18709


Grades Taught (8 vs. HS) ANOVA

0.090729

126

APPENDIX K

STATISTICAL OUTCOMES FOR ALL TESTS

FCI Overall Statistical Test

Participants’ Sex

t-Test: Two-Sample Assuming Unequal Variances

Female Male

Mean 0.416666667 0.516666667

Variance 0.060740741 0.04031746

Observations 16 8

Hypothesized Mean

Difference 0

df 17

t Stat

-

1.063831061

P(T<=t) one-tail 0.151141808

t Critical one-tail 3.965126263

P(T<=t) two-tail 0.302283616

t Critical two-tail 4.285828337

Chi squared r X c Contingency Table

Female Male Total

60% or less 11 5 16

Between 60% and 85% 4 3 7

85% or greater 1 0 1

Totals 16 8 24

Expected: Contingency Table

Female Male

60% or less 10.7 5.33

Between 60% and 85% 4.67 2.33

85% or greater 0.667 0.333

The given table has a probability of 0.2

The sum of the probabilities of “unusual” tables, p= 0.771

127


Years of Service

ANOVA

Anova: Single Factor

SUMMARY

Groups Count Sum Average Variance

Years 1-5 8 4 0.5 0.0495238

Years 6-10 10 3.9 0.39 0.0493951

Years 11 or

greater 6 2.9 0.4833333 0.0785556

ANOVA

Source of

Variation SS df MS F P-value F crit

Between

Groups 0.062667 2 0.0313333 0.5557432 0.5818543 11.155744

Within Groups 1.184 21 0.0563810

Total 1.246667 23


1-5 Years 6-10 Years 11 or greater Total

60% or less 5 8 5 18

Between 60% and 85% 3 2 0 5

85% or greater 0 0 1 1

Totals 8 10 6 24


1-5 Years 6-10 Years 11 or greater

60% or less 6.00 7.50 4.50

Between 60% and 85% 1.67 2.08 1.25

85% or greater 0.333 0.417 0.250



128


School District


School

District A

School

District B

Mean 0.451851852 0.448888889

Variance 0.035030864 0.069026455

Observations 9 15

Hypothesized Mean

Difference 0

df 21

t Stat 0.032149039

P(T<=t) one-tail 0.487328399


P(T<=t) two-tail 0.974656798



School District A School District

B

Total

60% or less 7 11 18



Totals 9 15 24


School District A School District B

60% or less 6.75 11.2

Between 60% and 85% 1.88 3.12

85% or greater 0.375 0.625



129


Grades Taught


SUMMARY


6th Grade 5 1.266667 0.2533333 0.0147778

7th Grade 8 2.7 0.3375 0.0172817

8th Grade 7 3.666667 0.5238095 0.0347090

High School 4 3.166667 0.7916667 0.0195370

ANOVA

Source of


Between Groups 0.799718 3 0.2665728 11.928569 0.0001066 9.1955382

Within Groups 0.446948 20 0.0223474

Total 1.246667 23


6th

Grade 7th

Grade 8th

Grade High School Total

60% or less 5 8 5 0 18

Between 60% and 85% 0 0 2 3 5

85% or greater 0 0 0 1 1

Totals 5 8 7 4 24


6th

Grade 7th

Grade 8th

Grade High School

60% or less 3.75 6.00 5.25 3.00

Between 60% and 85% 1.04 1.67 1.46 0.833

85% or greater 0.208 0.333 0.292 0.167


The sum of the probabilities of “unusual” tables find p< 0.001, p= 0.001

130


Education


Bachelors Master

Mean 0.428888889 0.485185185

Variance 0.047597884 0.070308642

Observations 15 9

Hypothesized Mean

Difference 0

df 14

t Stat

-

0.537124031

P(T<=t) one-tail 0.299807658


P(T<=t) two-tail 0.599615316



Bachelors Masters Total

60% or less 11 7 18



Totals 15 9 24


Bachelors Masters

60% or less 11.2 6.75

Between 60% and 85% 3.112 1.88

85% or greater 0.625 0.375



131


Education Major


SUMMARY


STEM Major 8 3.9 0.4875 0.03712302

Education

Major 6 2.766666667 0.46111111 0.05618519

Other Major 10 4.133333333 0.41333333 0.07560494

ANOVA

Source of


Between Groups 0.025435185 2 0.01271759

0.2186886

3

0.805378

9 11.155744

Within Groups 1.221231481 21 0.05815388

Total 1.246666667 23


STEM Major Education

Major

Other Major Total

60% or less 6 4 8 18

Between 60% and 85% 2 2 1 5


Totals 8 6 10 24


STEM Major Education Major Other Major

60% or less 6.00 4.50 7.50

Between 60% and 85% 1.67 1.25 2.08

85% or greater 0.333 0.250 0.417



132


STEM coursework


STEM Other

Mean 0.513888889 0.386111111

Variance 0.061910774 0.042516835

Observations 12 12

Hypothesized Mean

Difference 0

df 21

t Stat 1.369740465

P(T<=t) one-tail 0.092618697


P(T<=t) two-tail 0.185237395



STEM Other Total

60% or less 8 10 18



Totals 12 12 24


STEM Other

60% or less 9 9

Between 60% and 85% 2.50 2.50

85% or greater 0.5 0.5



133


Certification Type


Alternative Traditional

Mean 0.495833333 0.427083333

Variance 0.076329365 0.045810185

Observations 8 16

Hypothesized Mean

Difference 0

df 11

t Stat 0.617286026

P(T<=t) one-tail 0.274805211


P(T<=t) two-tail 0.549610422


r X c Contingency Table

Alternative Traditional Total

60% or less 5 13 18



Totals 8 16 24



60% or less 6.00 12.0

Between 60% and 85% 1.67 3.33

85% or greater 0.333 0.667



134


STEM Certification


SUMMARY


Science Generalist 15 7.6666667 0.51111111 0.05645503

Generalist 6 1.4666667 0.24444444 0.00918519

Other Not Specified 3 1.6666667 0.55555556 0.03370370

ANOVA

Source of


Between Groups 0.34296296 2 0.1714815 3.9848361 0.0341110 11.155744

Within Groups 0.90370370 21 0.0430335

Total 1.24666667 23


Science

Generalist

Generalist Other Not

Specified

Total

60% or less 10 6 2 18

Between 60% and 85% 4 0 1 5


Totals 15 6 3 24


Science

Generalist


Specified

60% or less 11.2 4.5 2.25

Between 60% and 85% 3.12 1.25 0.625

85% or greater 0.625 0.250 0.125



135

FCI Newton’s First Law Questions Statistical Tests

Participants’ Sex


Female Male

Mean 0.4875 0.55

Variance 0.031833333 0.088571429

Observations 16 8

Hypothesized Mean

Difference 0

df 9

t Stat

-

0.546879451

P(T<=t) one-tail 0.298878768


P(T<=t) two-tail 0.597757537



Female Male Total

60% or less 15 5 20



Totals 16 8 24


Female Male

60% or less 13.3 6.67

Between 60% and 85% 1.33 0.667

85% or greater 1.33 0.667



136


Years of Service

ANOVA


SUMMARY


Years 1-5 8 3.2 0.4 0.0228571

Years 6-10 10 4.8 0.48 0.0195556

Years 11 or

greater 6 4.2 0.7 0.092

ANOVA

Source of


Between

Groups

0.322333

3 2 0.1611667 4.2518844 0.0281572 11.155744

Within Groups 0.796 21 0.0379048

Total 1.118333 23



60% or less 8 9 3 20

Between 60% and 85% 0 1 1 2


Totals 8 10 6 24



60% or less 6.67 8.33 5.00

Between 60% and 85% 0.667 0.833 0.500

85% or greater 0.667 0.833 0.500



137


School District


School District A

School District

B

Mean 0.466666667 0.533333333

Variance 0.08 0.032380952

Observations 9 15

Hypothesized Mean

Difference 0

df 11

t Stat -0.634270329

P(T<=t) one-tail 0.269435558


P(T<=t) two-tail 0.538871116



School District A School District B Total

60% or less 7 13 20



Totals 9 15 24



60% or less 7.5 12.5

Between 60% and 85% 0.75 1.25

85% or greater 0.75 1.25



138


Grades Taught


SUMMARY


6th 5 2.4 0.48 0.012

7th 8 4.2 0.525 0.045

8th 7 3.4 0.4857143 0.07809524

High School 4 2.2 0.55 0.09

ANOVA

Source of


Between

Groups 0.0167619 3 0.0055873 0.1014424 0.9582466 9.1955382

Within

Groups 1.1015714 20 0.0550786

Total 1.1183333 23


6th

Grade 7th

Grade 8th


60% or less 5 6 6 3 20

Between 60% and 85% 0 2 0 0 2

85% or greater 0 0 1 1 2

Totals 5 8 7 4 24


6th

Grade 7th

Grade 8th

Grade High School

60% or less 4.17 6.67 5.83 3.33

Between 60% and 85% 0.417 0.667 0.583 0.333

85% or greater 0.417 0.667 0.583 0.333


The sum of the probabilities of “unusual” tables p= 0.378

139


FCI Education


Bachelor Masters

Mean 0.453333333 0.6

Variance 0.02552381 0.08

Observations 15 9

Hypothesized Mean

Difference 0

df 11

t Stat -1.42519299

P(T<=t) one-tail 0.090926522


P(T<=t) two-tail 0.181853045




60% or less 14 6 20



Totals 15 9 24


Bachelors Masters

60% or less 12.5 7.5

Between 60% and 85% 1.25 0.75

85% or greater 1.25 0.75



140


Education Major


SUMMARY


STEM Major 8 4.4 0.55 0.0657143

Education

Major 6 2.8 0.4666667 0.0106667

Other Major 10 5 0.5 0.0644444

ANOVA

Source of


Between

Groups 0.025 2 0.0125 0.24009146 0.7886855 11.155744

Within Groups 1.0933333 21 0.05206350

Total 1.1183333 23


STEM

Major

Education

Major

Other Major Total

60% or less 6 6 8 20

Between 60% and 85% 1 0 1 2


Totals 8 6 10 24



60% or less 6.67 5.00 8.33

Between 60% and 85% 0.667 0.500 0.833

85% or greater 0.667 0.500 0.833



141


STEM coursework


STEM Other

Mean 0.6 0.416666667

Variance 0.065454545 0.017878788

Observations 12 12

Hypothesized Mean

Difference 0

df 16

t Stat 2.2

P(T<=t) one-tail 0.0214232


P(T<=t) two-tail 0.0428464



STEM Other Total

60% or less 8 12 20

Between 60% and

85%

2 0 2


Totals 12 12 24


STEM Other

60% or less 10.0 10.0

Between 60% and 85% 1.00 1.00

85% or greater 1.00 1.00



142


Certification Type



Mean 0.575 0.475

Variance 0.085 0.031333333

Observations 8 16

Hypothesized Mean

Difference 0

df 9

t Stat 0.891460592

P(T<=t) one-tail 0.197944699


P(T<=t) two-tail 0.395889399




60% or less 5 15 20

Between 60% and

85%

2 0 2


Totals 8 16 24



60% or less 6.67 13.3

Between 60% and 85% 0.667 1.33

85% or greater 0.667 1.33



143


STEM Certification


SUMMARY


Science Generalist 15 7.6 0.50666667 0.05638095

Generalist 6 2.6 0.43333333 0.00666667

Other Not Specified 3 2 0.66666667 0.09333333

ANOVA

Source of


Between

Groups 0.109 2 0.0545 1.1339167 0.3406945 11.155744

Within Groups 1.009333 21 0.0480635

Total 1.118333 23


Science

Generalist


Specified

Total

60% or less 12 6 2 20

Between 60% and 85% 2 0 0 2


Totals 15 6 3 24


Science

Generalist


Specified

60% or less 12.5 5.00 2.50

Between 60% and 85% 1.25 0.500 0.250

85% or greater 1.25 0.500 0.250



144

FCI Newton’s Second Law Questions Statistical Tests

Participants’ Sex


Female Male

Mean 0.241071429 0.482142857

Variance 0.067261905 0.046282799

Observations 16 8

Hypothesized Mean

Difference 0

df 16

t Stat

-

2.412014845

P(T<=t) one-tail 0.01411776


P(T<=t) two-tail 0.02823552



Female Male Total

60% or less 15 6 21

Between 60% and

85%

0 1 1


Totals 16 8 24


Female Male

60% or less 14.0 7.00

Between 60% and 85% 0.667 0.333

85% or greater 1.33 0.667



145


Years of Service


SUMMARY


Years 1-5 8 2.857142857 0.357142857 0.104956268

Years 6-10 10 2.142857143 0.214285714 0.023809524

Years 11 or

greater 6 2.714285714 0.452380952 0.093197279

ANOVA

Source of


Between

Groups 0.2278912 2 0.113945578 1.69110577 0.2084659 11.15574

Within

Groups 1.4149660 21 0.06737933

Total 1.6428571 23



60% or less 6 10 5 21

Between 60% and 85% 1 0 0 1


Totals 8 10 6 24



60% or less 7.00 8.75 5.25

Between 60% and

85%

0.333 0.417 0.250

85% or greater 0.667 0.833 0.500



146


School District


School

District A

School

District B

Mean 0.451851852 0.448888889

Variance 0.035030864 0.069026455

Observations 9 15

Hypothesized Mean

Difference 0

df 21

t Stat 0.032149039

P(T<=t) one-tail 0.487328399


P(T<=t) two-tail 0.974656798




60% or less 7 14 21



Totals 9 15 24



60% or less 7.88 13.1

Between 60% and 85% 0.375 0.625

85% or greater 0.750 1.25



147


Grades Taught


SUMMARY


6th 5 0.5714286 0.114286 0.0142857

7th 8 2 0.25 0.0218659

8th 7 3.285714 0.4693878 0.0932945

High School 4 1.857143 0.4642857 0.127551

ANOVA

Source of


Between

Groups 0.4902332 3 0.1634111 2.8354622 0.0641622 9.1955382

Within Groups 1.1526239 20 0.0576312

Total 1.6428571 23


6th

Grade

7th

Grade

8th

Grade

High

School

Total

60% or less 5 8 5 3 21

Between 60% and 85% 0 0 1 0 1

85% or greater 0 0 1 1 2

Totals 5 8 7 4 24


6th

Grade 7th

Grade 8th

Grade High School

60% or less 4.38 7.00 6.12 3.50

Between 60%

and 85%

0.208 0.333 0.292 0.167

85% or greater 0.417 0.667 0.583 0.333


The sum of the probabilities of “unusual” tables , p= 0.481

148


Education


Bachelor Masters

Mean 0.247619048 0.444444444

Variance 0.059669582 0.073696145

Observations 15 9

Hypothesized Mean

Difference 0

df 15

t Stat

-

1.784429961

P(T<=t) one-tail 0.047294771


P(T<=t) two-tail 0.094589542




60% or less 13 8 21



Totals 15 9 24


Bachelors Masters

60% or less 13.1 7.88

Between 60% and 85% 0.625 0.375

85% or greater 1.25 0.750



149


Education Major


SUMMARY


STEM

Major 8 3.4285714 0.4285714 0.0524781

Education

Major 6 1.1428571 0.1904762 0.0217687

Other Major 10 3.1428571 0.3142857 0.1079365

ANOVA

Source of


Between

Groups 0.1952381 2 0.0976190 1.4161184 0.2648940 3.4668001

Within Groups 1.447619 21 0.068934

Total 1.6428571 23


STEM

Major

Education

Major

Other Major Total

60% or less 7 6 8 21

Between 60% and 85% 0 0 1 1


Totals 8 6 10 24



60% or less 7.00 5.25 8.75

Between 60% and 85% 0.333 0.250 0.417

85% or greater 0.667 0.500 0.833



150


STEM coursework


STEM Other

Mean 0.357142857 0.285714286

Variance 0.06864564 0.077922078

Observations 12 12

Hypothesized Mean

Difference 0

df 21

t Stat 0.646313793

P(T<=t) one-tail 0.262538626


P(T<=t) two-tail 0.525077253



STEM Other Total

60% or less 11 10 21

Between 60% and

85%

0 1 1


Totals 12 12 24


STEM Other

60% or less 10.5 10.5

Between 60% and 85% 0.500 0.500

85% or greater 1.00 1.00



151


Certification Type



Mean 0.446428571 0.258928571

Variance 0.07835277 0.060459184

Observations 8 16

Hypothesized Mean

Difference 0

df 12

t Stat 1.609409736

P(T<=t) one-tail 0.066751148


P(T<=t) two-tail 0.133502296




60% or less 6 15 21



Totals 8 16 24



60% or less 7.00 14.0

Between 60% and 85% 0.333 0.667

85% or greater 0.667 1.33



152


STEM Certification


SUMMARY


Science Generalist 15 5 0.3333333 0.0573372

Generalist 6 2.2857143 0.3809524 0.1197279

Other Not Specified 3 0.4285714 0.1428571 0.061224

ANOVA

Source of


Between

Groups 0.119048 2 0.05952381 0.8203125 0.453914731 11.15574397

Within Groups 1.523810 21 0.07256236

Total 1.64285714 23


Science

Generalist


Specified

Total

60% or less 13 5 3 21

Between 60% and 85% 1 0 0 1


Totals 15 6 3 24


Science Generalist Generalist Other Not

Specified

60% or less 13.1 5.25 2.62

Between 60% and 85% 0.625 0.250 0.125

85% or greater 1.25 0.500 0.250



153

FCI Newton’s Third Law Questions Statistical Tests

Participants’ Sex


Female Male

Mean 0.40625 0.46875

Variance 0.123958333 0.114955357

Observations 16 8

Hypothesized Mean

Difference 0

df 14

t Stat

-

0.420260642

P(T<=t) one-tail 0.340337002


P(T<=t) two-tail 0.680674005



Female Male Total

60% or less 12 6 18



Totals 16 8 24


Female Male

60% or less 12.0 6.00

Between 60% and 85% 1.33 0.667

85% or greater 2.67 1.33



154


Years of Service


SUMMARY


Years 1-5 8 4.25 0.53125 0.07924107

Years 6-10 10 4 0.4 0.12777778

Years 11 or

greater 6 2 0.33333333 0.16666667

ANOVA

Source of


Between

Groups 0.146875 2 0.073438 0.6076339 0.5539382 11.1557440

Within

Groups 2.5380208 21 0.12085814

Total 2.6848958 23



60% or less 5 8 5 18

Between 60% and 85% 2 0 0 2


Totals 8 10 6 24



60% or less 6.00 7.50 4.50

Between 60% and 85% 0.667 0.833 0.500

85% or greater 1.33 1.67 1.00



155


School District


School

District A

School

District B

Mean 0.444444444 0.416666667

Variance 0.027777778 0.175595238

Observations 9 15

Hypothesized Mean

Difference 0

df 19

t Stat 0.228387727

P(T<=t) one-tail 0.410892438


P(T<=t) two-tail 0.821784877




60% or less 8 10 18

Between 60% and

85%

1 1 2


Totals 9 15 24



60% or less 6.75 11.2

Between 60% and 85% 0.750 1.25

85% or greater 1.50 2.50



156


Grades Taught


SUMMARY


6th 5 0.25 0.05 0.0125

7th 8 2.25 0.28125 0.0256696

8th 7 3.75 0.5357143 0.0297619

High School 4 4 1 0

ANOVA

Source of


Between

Groups 2.2766369 3 0.7588790 37.176356 2.26978E-08 9.195538

Within

Groups 0.4082589 20 0.0204129

Total 2.6848958 23


6th

Grade 7th

Grade 8th


60% or less 5 8 5 0 18

Between 60% and 85% 0 0 2 0 2

85% or greater 0 0 0 4 4

Totals 5 8 7 4 24


6th

Grade 7th

Grade 8th

Grade High School

60% or less 3.75 6.00 5.25 3.00

Between 60% and 85% 0.417 0.667 0.583 0.333

85% or greater 0.833 1.33 1.17 0.667


The sum of the probabilities of “unusual” tables find p< 0.001, p= 0.000025

157


Education


Bachelor Masters

Mean 0.366666667 0.527777778

Variance 0.114880952 0.116319444

Observations 15 9

Hypothesized Mean

Difference 0

df 16

t Stat

-

1.122974682

P(T<=t) one-tail 0.139008454


P(T<=t) two-tail 0.278016908


r X c Contingency Table


60% or less 12 6 18



Totals 15 9 24


Bachelors Masters

60% or less 11.2 6.75

Between 60% and 85% 1.25 0.750

85% or greater 2.50 1.50



158


Education Major


SUMMARY


STEM Major 8 5 0.625 0.0714286

Education

Major 6 2.5 0.4166667 0.1416667

Other Major 10 2.75 0.275 0.1034722

ANOVA

Source of


Between

Groups 0.5453125 2 0.272656 2.6761198 0.0921974 3.4668001

Within

Groups 2.1395833 21 0.1018849

Total 2.6848958 23


STEM Major Education Major Other Major Total

60% or less 5 4 9 18

Between 60% and 85% 1 1 0 2


Totals 8 6 10 24



60% or less 6.00 4.50 7.50

Between 60% and 85% 0.667 0.500 0.833

85% or greater 1.33 1.00 1.67



159


STEM coursework


STEM Other

Mean 0.583333333 0.270833333

Variance 0.128787879 0.062026515

Observations 12 12

Hypothesized Mean

Difference 0

df 19

t Stat 2.47819273

P(T<=t) one-tail 0.01138085


P(T<=t) two-tail 0.0227617



STEM Other Total

60% or less 7 11 18



Totals 12 12 24


STEM Other

60% or less 9.00 9.00

Between 60% and 85% 1.00 1.00

85% or greater 2.00 2.00



160


Certification Type



Mean 0.5 0.390625

Variance 0.142857143 0.108072917

Observations 8 16

Hypothesized Mean

Difference 0

df 12

t Stat 0.697183754

P(T<=t) one-tail 0.249486278


P(T<=t) two-tail 0.498972556




60% or less 6 12 18



Totals 8 16 24



60% or less 6.00 12.0

Between 60% and 85% 0.667 1.33

85% or greater 1.33 2.67



161


STEM Certification


SUMMARY


Science

Generalist 15 7.75 0.5166667 0.1113095

Generalist 6 1 0.1666667 0.0166667

Other Not

Specified 3 1.5 0.5 0.25

ANOVA

Source of


Between

Groups 0.5432292 2 0.2716144 2.6633025 0.093144364 11.15574397

Within

Groups 2.1416667 21 0.1019841

Total 2.6848958 23


Science

Generalist


Specified

Total

60% or less 10 6 2 18

Between 60% and 85% 2 0 0 2


Totals 15 6 3 24


Science Generalist Generalist Other Not Specified

60% or less 11.2 4.5 2.25

Between 60% and 85% 1.25 0.500 0.250

85% or greater 2.50 1.00 0.500



162

TRC Physics Assessment Overall Statistical Tests

Region


SUMMARY


1 30 12.35294 0.411765 0.0105

2 24 9.852941 0.410539 0.014704

3 1 0.411765 0.411765 #DIV/0!

4 47 22.35294 0.475594 0.028371

5 2 1.352941 0.676471 0

6 32 14.55882 0.454963 0.014621

7 61 23.55882 0.38621 0.020746

8 15 6.676471 0.445098 0.022722

10 31 17.02941 0.549336 0.036008

11 7 3.294118 0.470588 0.00519

12 26 11.08824 0.426471 0.01372

13 1 0.382353 0.382353 #DIV/0!

14 24 10.29412 0.428922 0.016919

15 20 8.5 0.425 0.00924

16 19 9.294118 0.489164 0.016745

17 25 11.47059 0.458824 0.015283

19 3 1.235294 0.411765 0.006055

ANOVA

Source of


Between

Groups 0.842147 16 0.052634 2.772607 0.000321 1.672385

Within Groups 6.66326 351 0.018984

Total 7.505407 367

163


Rural Vs Urban

Anova: Single

Factor

SUMMARY


Rural 106 48 0.45283 0.016274

Independent 146 60.29412 0.412973 0.017216

Urban 116 55.41176 0.477688 0.026262

ANOVA

Source of


Between Groups 0.280187 2 0.140094 7.077185 0.000965 3.020455

Within Groups 7.225219 365 0.019795

Total 7.505407 367

Urban


SUMMARY


1 30 12.35294 0.411765 0.0105

4 47 22.35294 0.475594 0.028371

10 31 17.02941 0.549336 0.036008

11 7 3.294118 0.470588 0.00519

13 1 0.382353 0.382353 #DIV/0!

ANOVA

Source of


Between

Groups 0.29916 4 0.07479 3.051031 0.019883 2.453458

Within Groups 2.720945 111 0.024513

Total 3.020105 115

164


Rural


SUMMARY


3 1 0.411765 0.411765 #DIV/0!

5 2 1.352941 0.676471 0

8 15 6.676471 0.445098 0.022722

14 24 10.29412 0.428922 0.016919

15 20 8.5 0.425 0.00924

16 19 9.294118 0.489164 0.016745

17 25 11.47059 0.458824 0.015283

ANOVA

Source of


Between

Groups 0.157803 6 0.026301 1.678777 0.134071 2.191549

Within Groups 1.550984 99 0.015667

Total 1.708788 105

Participants’ Sex


Female Male

Mean 0.42988 0.499627699

Variance 0.017771 0.026738836

Observations 289 79

Hypothesized Mean Difference 0

df 108

t Stat -3.48758

P(T<=t) one-tail 0.000353


P(T<=t) two-tail 0.000706


165


Education


SUMMARY


BA/BS 257 110.5 0.429961 0.020419

MA/MS 101 48.17647 0.476995 0.019163

PhD/EdD 4 2.088235 0.522059 0.022131

ANOVA

Source of


Between

Groups 0.184975 2 0.092488 4.60524 0.010597 3.02087

Within Groups 7.209851 359 0.020083

Total 7.394827 361

STEM Major


Stem

Major

Non-Stem

Major

Mean 0.472128 0.417938

Variance 0.02146 0.01852

Observations 191 162

Hypothesized Mean

Difference 0

df 348

t Stat 3.599317





166


Years of Service


SUMMARY


0-4 96 41.82353 0.435662 0.022133

5-9 133 61.11765 0.459531 0.018685

10-14 55 23.32353 0.424064 0.02325

15-19 36 16.5 0.458333 0.019149

20-24 22 10.23529 0.465241 0.018507

25-29 15 6.058824 0.403922 0.014393

30-34 9 4.029412 0.447712 0.035203

ANOVA

Source of


Between

Groups 0.101223 6 0.01687 0.822163 0.55326 2.123852

Within Groups 7.366541 359 0.02052

Total 7.467764 365

Certification Type


Traditional Alternative

Mean 0.439356 0.452159

Variance 0.020034 0.021042


Hypothesized Mean

Difference 0

df 334

t Stat -0.84687





167


Middle School vs High School


SUMMARY


MS 279 117.6176 0.421569 0.015571

HS 69 36.91176 0.534953 0.029521

Both 20 9.176471 0.458824 0.023894

ANOVA

Source of


Between

Groups 0.715311 2 0.357655 19.22568 1.15E-08 3.020455

Within Groups 6.790096 365 0.018603

Total 7.505407 367

8th

Grade Vs High School

Anova: Single

Factor

SUMMARY


8th 117 50.61765 0.432629 0.017555

High 69 36.91176 0.534953 0.029521

Both 15 7 0.466667 0.030507

ANOVA

Source of


Between Groups 0.454651 2 0.227325 10.06734 6.86E-05 3.041518

Within Groups 4.470937 198 0.02258

Total 4.925588 200

168


Grades Taught

Anova: Single

Factor

SUMMARY


6th 45 18.64706 0.414379 0.019299

7th 23 10.20588 0.443734 0.01289

8th 84 36.82353 0.438375 0.01746

MSS 86 35.02941 0.407319 0.011368

IPC 10 3.852941 0.385294 0.028922

Physics 39 22.38235 0.573906 0.029725

2+ MSS 41 16.91176 0.412482 0.018057

2 HSS 20 10.67647 0.533824 0.017599

MSS+HSS 20 9.176471 0.458824 0.023894

ANOVA

Source of

Variation SS df MS F

P-

value F crit

Between Groups 1.056693 8 0.132087 7.353266 4.57E-09 1.964217

Within Groups 6.448713 359 0.017963

Total 7.505407 367

169

TRC Physics Assessment Conceptual Force Questions Statistical Tests

Region


SUMMARY


Region 1 30 12.31818 0.410606 0.013178

Region 2 24 10.36364 0.431818 0.02102

Region 3 1 0.409091 0.409091 #DIV/0!

Region 4 47 23.86364 0.507737 0.032907

Region 5 2 1.545455 0.772727 0

Region 6 32 15.22727 0.475852 0.017993

Region 7 61 25.31818 0.415052 0.026755

Region 8 15 6.909091 0.460606 0.027115

Region 10 31 17.63636 0.568915 0.041442

Region 11 7 3.454545 0.493506 0.012003

Region 12 26 11.90909 0.458042 0.020648

Region 13 1 0.409091 0.409091 #DIV/0!

Region 14 24 10.95455 0.456439 0.018591

Region 15 20 9.090909 0.454545 0.012614

Region 16 19 9.545455 0.502392 0.018474

Region 17 25 12.09091 0.483636 0.019263

Region 19 3 1.227273 0.409091 0.008264

ANOVA

Source of


Between

Groups 0.939568 16 0.058723 2.503808 0.001209 1.672385

Within Groups 8.232167 351 0.023453

Total 9.171735 367

170


Rural vs. Urban


SUMMARY


Rural 106 50.54545 0.476844 0.019608

Independent 146 64.04545 0.438667 0.022559

Urban 116 57.68182 0.497257 0.031379

ANOVA

Source of


Between

Groups 0.233195 2 0.116598 4.761195 0.009094 3.020455

Within Groups 8.938539 365 0.024489

Total 9.171735 367

Urban


SUMMARY


Region 1 30 12.31818 0.410606 0.013178

Region 4 47 23.86364 0.507737 0.032907

Region 10 31 17.63636 0.568915 0.041442

Region 11 7 3.454545 0.493506 0.012003

Region 13 1 0.409091 0.409091 #DIV/0!

ANOVA

Source of


Between

Groups 0.397466 4 0.099366 3.434791 0.010948 2.453458

Within Groups 3.211165 111 0.028929

Total 3.608631 115

171


Rural


SUMMARY


Region 3 1 0.409091 0.409091 #DIV/0!

Region 5 2 1.545455 0.772727 0

Region 8 15 6.909091 0.460606 0.027115

Region 14 24 10.95455 0.456439 0.018591

Region 15 20 9.090909 0.454545 0.012614

Region 16 19 9.545455 0.502392 0.018474

Region 17 25 12.09091 0.483636 0.019263

ANOVA

Source of


Between

Groups 0.217131 6 0.036189 1.945268 0.080875 2.191549

Within Groups 1.841734 99 0.018603

Total 2.058865 105

Participants’ Sex


Female Male

Mean 0.450928 0.53107

Variance 0.021344 0.033669

Observations 289 79

Hypothesized Mean

Difference 0

df 107

t Stat -3.58389





172


Education


SUMMARY


BA/BS 257 116.8636 0.454722 0.024721

MA/MS 101 50 0.49505 0.024335

PhD/EdD 4 2.181818 0.545455 0.035813

ANOVA

Source of


Between

Groups 0.142825 2 0.071413 2.890505 0.056842 3.02087

Within Groups 8.869418 359 0.024706

Total 9.012243 361

STEM Major


Stem Major

Non-Stem

Major

Mean 0.499524036 0.436868687

Variance 0.025358624 0.023747498


Hypothesized Mean Difference 0

df 345

t Stat 3.748679018

P(T<=t) one-tail 0.000104185


P(T<=t) two-tail 0.00020837


173


Years of Experience


SUMMARY


0-4 96 43.59091 0.454072 0.026903

5-9 133 64.77273 0.487013 0.02478

10-14 55 24.86364 0.452066 0.028805

15-19 36 17.22727 0.478535 0.019774

20-24 22 10.5 0.477273 0.023564

25-29 15 6.5 0.433333 0.015604

30-34 9 4.045455 0.449495 0.032771

ANOVA

Source of


Between

Groups 0.107539 6 0.017923 0.711006 0.640938 2.123852

Within Groups 9.049717 359 0.025208

Total 9.157256 365

Certification Type



Mean 0.465368 0.471807

Variance 0.023035 0.027731


Hypothesized Mean

Difference 0

df 321

t Stat -0.38126





174


High School vs. Middle School


SUMMARY


MS 279 123.5455 0.442815 0.019914

HS 69 39 0.565217 0.034728

Both 20 9.727273 0.486364 0.023075

ANOVA

Source of


P-

value F crit

Between

Groups 0.835834 2 0.417917 18.29913

2.67E-

08 3.020455

Within Groups 8.335901 365 0.022838

Total 9.171735 367

8th

Grade vs. High School

Anova: Single

Factor

SUMMARY


8th 117 52.31818 0.447164 0.022085

High 69 39 0.565217 0.034728

Both 15 7.318182 0.487879 0.026997

ANOVA

Source of Variation SS df MS F P-value F crit

Between Groups 0.605026 2 0.302513 11.29876 2.26E-05 3.041518

Within Groups 5.301249 198 0.026774

Total 5.906274 200

175


Grades Taught


SUMMARY


6th 45 19.95455 0.443434 0.023024

7th 23 10.54545 0.458498 0.019706

8th 84 38.31818 0.456169 0.020235

MSS 86 37.36364 0.434461 0.015732

IPC 10 4 0.4 0.033884

Physics 39 23.95455 0.614219 0.031413

2+ MSS 41 17.36364 0.423503 0.025768

2 HSS 20 11.04545 0.552273 0.025941

MSS+HSS 20 9.727273 0.486364 0.023075

ANOVA

Source of


P-

value F crit

Between

Groups 1.247745 8 0.155968 7.066208

1.11E-

08 1.964217

Within Groups 7.92399 359 0.022072

Total 9.171735 367

176

TRC Physics Assessment Newton’s First Law Questions Statistical Tests

Region

Anova: Single

Factor

SUMMARY


1 30 12.5 0.416667 0.041667

2 24 10.5 0.4375 0.050272

3 1 0.5 0.5 #DIV/0!

4 47 25.83333 0.549645 0.059076

5 2 1.666667 0.833333 0

6 32 14.16667 0.442708 0.035142

7 61 26 0.42623 0.055115

8 15 7 0.466667 0.024603

10 31 16.33333 0.526882 0.07055

11 7 3.333333 0.47619 0.031746

12 26 12.33333 0.474359 0.045983

13 1 0.166667 0.166667 #DIV/0!

14 24 10.5 0.4375 0.052687

15 20 8.666667 0.433333 0.036257

16 19 10.5 0.552632 0.080409

17 25 12.5 0.5 0.032407

19 3 1.166667 0.388889 0.009259

ANOVA

Source of Variation SS df MS F P-value F crit

Between Groups 1.230539 16 0.076909 1.549319 0.080607 1.672385

Within Groups 17.42375 351 0.04964

Total 18.65429 367

177


Region Type


SUMMARY


Rural 106 51.33333 0.484277 0.04684

Independent 146 64.16667 0.439498 0.046697

Urban 116 58.16667 0.501437 0.05821

ANOVA

Source of


Between

Groups 0.270725 2 0.135362 2.687578 0.069392 3.020455

Within Groups 18.38356 365 0.050366

Total 18.65429 367

Urban


SUMMARY


1 30 12.5 0.416667 0.041667

4 47 25.83333 0.549645 0.059076

10 31 16.33333 0.526882 0.07055

11 7 3.333333 0.47619 0.031746

13 1 0.166667 0.166667 #DIV/0!

ANOVA

Source of


Between

Groups 0.461414 4 0.115353 2.054334 0.091649 2.453458

Within Groups 6.232791 111 0.056151

Total 6.694205 115

178


Rural


SUMMARY


3 1 0.5 0.5 #DIV/0!

5 2 1.666667 0.833333 0

8 15 7 0.466667 0.024603

14 24 10.5 0.4375 0.052687

15 20 8.666667 0.433333 0.036257

16 19 10.5 0.552632 0.080409

17 25 12.5 0.5 0.032407

ANOVA

Source of


Between

Groups 0.447954 6 0.074659 1.653416 0.140535 2.191549

Within Groups 4.470285 99 0.045154

Total 4.918239 105

Participants’ Sex


Female Male

Mean 0.458478 0.521097

Variance 0.047074 0.062227

Observations 289 79

Hypothesized Mean

Difference 0

df 112

t Stat -2.03103





179


Education

Anova: Single

Factor

SUMMARY


BA/BS 257 116.1667 0.45201 0.047818

MA/MS 101 52 0.514851 0.052277

PhD/EdD 4 2 0.5 0.12963

ANOVA

Source of


Between Groups 0.289947 2 0.144974 2.914398 0.055521 3.02087

Within Groups 17.85807 359 0.049744

Total 18.14802 361

STEM Degree


Stem Major

Non-Stem

Major

Mean 0.506980803 0.433128

Variance 0.052582591 0.048123


Hypothesized Mean

Difference 0

df 346

t Stat 3.086998627

P(T<=t) one-tail 0.001092561


P(T<=t) two-tail 0.002185122


180


Years of Service

Anova: Single

Factor

SUMMARY


0-4 96 43.16667 0.449653 0.049778

5-9 133 65 0.488722 0.054375

10-14 55 26.83333 0.487879 0.051291

15-19 36 16.83333 0.467593 0.053682

20-24 22 10.33333 0.469697 0.044012

25-29 15 6.333333 0.422222 0.02328

30-34 9 4.333333 0.481481 0.08642

ANOVA

Source of


Between Groups 0.137773 6 0.022962 0.445676 0.847927 2.123852

Within Groups 18.49649 359 0.051522

Total 18.63426 365

Certification



Mean 0.464286 0.482068

Variance 0.050951 0.050809


Hypothesized Mean

Difference 0

df 339

t Stat -0.74862





181



Anova: Single

Factor

SUMMARY


MS 279 124.6667 0.446834 0.044226

HS 69 39.33333 0.570048 0.062423

Both 20 9.666667 0.483333 0.066959

ANOVA

Source of


Between Groups 0.842595 2 0.421298 8.633299 0.000217 3.020455

Within Groups 17.81169 365 0.048799

Total 18.65429 367

8th

Grade vs High School

Anova: Single

Factor

SUMMARY


8th 117 53.83333 0.460114 0.04533

High School 69 39.33333 0.570048 0.062423

Both 15 7.166667 0.477778 0.086772

ANOVA

Source of


Between Groups 0.531971 2 0.265985 4.913758 0.008265 3.041518

Within Groups 10.71789 198 0.054131

Total 11.24986 200

182


Grades Taught

Anova: Single

Factor

SUMMARY


6th 45 20 0.444444 0.04798

7th 23 9.5 0.413043 0.045125

8th 84 40.33333 0.480159 0.047794

MSS 86 37.5 0.436047 0.041287

IPC 10 4.333333 0.433333 0.050617

Physics 39 24.16667 0.619658 0.065714

2+ MSS 41 17.33333 0.422764 0.039024

2 HSS 20 10.83333 0.541667 0.052266

MSS+HSS 20 9.666667 0.483333 0.066959

ANOVA

Source of


Between Groups 1.295165 8 0.161896 3.348125 0.001031048 1.964217

Within Groups 17.35912 359 0.048354

Total 18.65429 367

183

TRC Physics Assessment Newton’s Second Law Questions Statistical Tests

Region


SUMMARY


1 30 13.375 0.445833 0.030909

2 24 11 0.458333 0.030797

3 1 0.5 0.5 #DIV/0!

4 47 24.875 0.529255 0.042943

5 2 1.25 0.625 0

6 32 17.875 0.558594 0.044339

7 61 26.75 0.438525 0.054491

8 15 7.375 0.491667 0.09256

10 31 19.875 0.641129 0.059106

11 7 4.125 0.589286 0.03497

12 26 13.25 0.509615 0.024904

13 1 0.375 0.375 #DIV/0!

14 24 13.25 0.552083 0.047441

15 20 10.75 0.5375 0.033059

16 19 10.125 0.532895 0.032712

17 25 14 0.56 0.041823

19 3 1.625 0.541667 0.067708

ANOVA

Source of


Between

Groups 1.332433 16 0.083277 1.877013 0.021468 1.672385

Within Groups 15.57276 351 0.044367

Total 16.90519 367

184


Rural vs Urban


SUMMARY


Rural 106 57.25 0.540094 0.044508

Independent 146 70.5 0.482877 0.044532

Urban 116 62.625 0.539871 0.047717

ANOVA

Source of


Between

Groups 0.287175 2 0.143587 3.153768 0.043857 3.020455

Within Groups 16.61801 365 0.045529

Total 16.90519 367

Urban


SUMMARY


1 30 13.375 0.445833 0.030909

4 47 24.875 0.529255 0.042943

10 31 19.875 0.641129 0.059106

11 7 4.125 0.589286 0.03497

13 1 0.375 0.375 #DIV/0!

ANOVA

Source of


Between

Groups 0.632713 4 0.158178 3.616613 0.008248 2.453458

Within Groups 4.85476 111 0.043737

Total 5.487473 115

185


Rural


SUMMARY


3 1 0.5 0.5 #DIV/0!

5 2 1.25 0.625 0

8 15 7.375 0.491667 0.09256

14 24 13.25 0.552083 0.047441

15 20 10.75 0.5375 0.033059

16 19 10.125 0.532895 0.032712

17 25 14 0.56 0.041823

ANOVA

Source of


Between

Groups 0.065679 6 0.010947 0.235196 0.964052 2.191549

Within Groups 4.60767 99 0.046542

Total 4.673349 105

Participants’ Sex


Female Male

Mean 0.502595 0.571203

Variance 0.04459 0.048351

Observations 289 79

Hypothesized Mean

Difference 0

df 120

t Stat -2.47836





186


Education


SUMMARY


BA/BS 257 130.125 0.506323 0.048605

MA/MS 101 54.375 0.538366 0.041482

PhD/EdD 4 2.625 0.65625 0.045573

ANOVA

Source of


Between

Groups 0.152966 2 0.076483 1.641429 0.195153 3.02087

Within Groups 16.72777 359 0.046595

Total 16.88074 361

STEM Degree


Stem

Major

Non-Stem

Major

Mean 0.545157 0.486111

Variance 0.048032 0.042702


Hypothesized Mean

Difference 0

df 347

t Stat 2.601699





187


Years of Service


SUMMARY


0-4 96 48.5 0.505208 0.057538

5-9 133 70.625 0.531015 0.038448

10-14 55 25.625 0.465909 0.046559

15-19 36 20.75 0.576389 0.037748

20-24 22 12.75 0.579545 0.038014

25-29 15 6.875 0.458333 0.05506

30-34 9 4.25 0.472222 0.061632

ANOVA

Source of


Between

Groups 0.465682 6 0.077614 1.694961 0.121158 2.123852

Within Groups 16.4389 359 0.045791

Total 16.90459 365

Certification Type



Mean 0.518452 0.515823

Variance 0.04429 0.048713


Hypothesized Mean

Difference 0

df 329

t Stat 0.115403





188


Middle School vs. High School


SUMMARY


Middle 279 136 0.487455 0.041434

High 69 43.625 0.632246 0.046822

Both 20 10.75 0.5375 0.054441

ANOVA

Source of


Between

Groups 1.168344 2 0.584172 13.54927 2.11E-06 3.020455

Within Groups 15.73685 365 0.043115

Total 16.90519 367

8th

Grade vs. High school


SUMMARY


8th 117 55.875 0.477564 0.045424

High 69 43.625 0.632246 0.046822

Both 15 8.25 0.55 0.044196

ANOVA

Source of


Between

Groups 1.041637 2 0.520819 11.36725 2.12E-05 3.041518

Within Groups 9.071858 198 0.045817

Total 10.1135 200

189


Grades Taught


SUMMARY


6th 45 21.875 0.486111 0.050584

7th 23 11.875 0.516304 0.047307

8th 84 41.5 0.494048 0.042509

MSS 86 42.375 0.492733 0.03138

IPC 10 4.25 0.425 0.021528

Physics 39 27.25 0.698718 0.039242

2+ MSS 41 18.375 0.448171 0.0492

2 HSS 20 12.125 0.60625 0.046505

MSS+HSS 20 10.75 0.5375 0.054441

ANOVA

Source of


P-

value F crit

Between

Groups 1.872234 8 0.234029 5.588822

1.1E-

06 1.964217

Within Groups 15.03295 359 0.041875

Total 16.90519 367

190

TRC Physics Assessment Newton’s Third Law Questions Statistical Tests

Region


SUMMARY


1 30 7.6 0.253333 0.038437

2 24 6.4 0.266667 0.051014

3 1 0.2 0.2 #DIV/0!

4 47 17 0.361702 0.108936

5 2 2 1 0

6 32 8.8 0.275 0.043226

7 61 18.4 0.301639 0.050164

8 15 5 0.333333 0.08381

10 31 14.4 0.464516 0.105032

11 7 2.2 0.314286 0.038095

12 26 8 0.307692 0.083938

13 1 0.6 0.6 #DIV/0!

14 24 6.4 0.266667 0.023188

15 20 5 0.25 0.037368

16 19 6 0.315789 0.054737

17 25 6.2 0.248 0.0376

19 3 0.4 0.133333 0.013333

ANOVA

Source of


Between

Groups 2.414168 16 0.150886 2.468127 0.001436 1.672385

Within Groups 21.4579 351 0.061134

Total 23.87207 367

191


Region Type


SUMMARY


Rural 106 30.8 0.290566 0.05191

Independent 146 42 0.287671 0.053502

Urban 116 41.8 0.360345 0.089196

ANOVA

Source of


Between

Groups 0.406105 2 0.203052 3.158367 0.043659 3.020455

Within Groups 23.46596 365 0.06429

Total 23.87207 367

Urban


SUMMARY


1 30 7.6 0.253333 0.038437

4 47 17 0.361702 0.108936

10 31 14.4 0.464516 0.105032

11 7 2.2 0.314286 0.038095

13 1 0.6 0.6 #DIV/0!

ANOVA

Source of


Between

Groups 0.752317 4 0.188079 2.196338 0.073973 2.453458

Within Groups 9.50527 111 0.085633

Total 10.25759 115

192


Rural


SUMMARY


3 1 0.2 0.2 #DIV/0!

5 2 2 1 0

8 15 5 0.333333 0.08381

14 24 6.4 0.266667 0.023188

15 20 5 0.25 0.037368

16 19 6 0.315789 0.054737

17 25 6.2 0.248 0.0376

ANOVA

Source of


Between

Groups 1.146236 6 0.191039 4.393924 0.00056 2.191549

Within Groups 4.30433 99 0.043478

Total 5.450566 105

Participants’ Sex


Female Male

Mean 0.286505 0.402532

Variance 0.055408 0.090763

Observations 289 79

Hypothesized Mean

Difference 0

df 105

t Stat -3.16887





193


Education


SUMMARY


BA/BS 257 76.4 0.297276 0.059094

MA/MS 101 33.6 0.332673 0.074622

PhD/EdD 4 1.6 0.4 0.026667

ANOVA

Source of


Between

Groups 0.124867 2 0.062433 0.988675 0.37308 3.02087

Within Groups 22.67027 359 0.063148

Total 22.79514 361

STEM Degree


Stem

Major

Non-Stem

Major

Mean 0.327749 0.302469

Variance 0.064015 0.069186


Hypothesized Mean

Difference 0

df 337

t Stat 0.91564





194


Years of Service


SUMMARY


0-4 96 29.8 0.310417 0.071469

5-9 133 44.8 0.336842 0.079011

10-14 55 18 0.327273 0.053872

15-19 36 10.2 0.283333 0.037429

20-24 22 5.4 0.245455 0.060693

25-29 15 4 0.266667 0.032381

30-34 9 2 0.222222 0.044444

ANOVA

Source of


Between

Groups 0.325522 6 0.054254 0.82805 0.548742 2.123852

Within Groups 23.52158 359 0.06552

Total 23.8471 365

Certification Type



Mean 0.300952 0.325316

Variance 0.057511 0.075151


Hypothesized Mean

Difference 0

df 312

t Stat -0.88995





195




SUMMARY


Middle 279 81.4 0.291756 0.060903

High 69 27.4 0.397101 0.074697

Both 20 5.8 0.29 0.065158

ANOVA

Source of


Between

Groups 0.623606 2 0.311803 4.895292 0.00798 3.020455

Within Groups 23.24846 365 0.063694

Total 23.87207 367

8th

Grade vs High School


SUMMARY


8th 117 36.2 0.309402 0.069997

High 69 27.4 0.397101 0.074697

Both 15 4.6 0.306667 0.079238

ANOVA

Source of


Between

Groups 0.351091 2 0.175545 2.429199 0.090729 3.041518

Within Groups 14.30841 198 0.072265

Total 14.6595 200

196


Grades Taught


SUMMARY


6th 45 13 0.288889 0.059192

7th 23 8.6 0.373913 0.055652

8th 84 25.4 0.302381 0.066982

MSS 86 22 0.255814 0.053319

IPC 10 2.4 0.24 0.096

Physics 39 17 0.435897 0.07552

2+ MSS 41 12.4 0.302439 0.068244

2 HSS 20 8 0.4 0.054737

MSS+HSS 20 5.8 0.29 0.065158

ANOVA

Source of


Between

Groups 1.210156 8 0.15127 2.396346 0.015787 1.964217

Within Groups 22.66191 359 0.063125

Total 23.87207 367

197

APPENDIX L

HOLM-BONFERRIONI CORRECTION FOR P-VALUES AS CALCULATED BY

EXCEL (Gaetano, 2013)

FCI Statistical Tests

p p' rank outcome

2.27E-08 0.000001634 1 SIG

0.000025 0.001775000 2 SIG

0.00010654 0.0074582900 3 SIG

0.001 0.0690000000 4 NON SIG

0.01138085 0.7738978000 5 NON SIG

0.01411776 0.9458899200 6 NON SIG

0.0214232 1.000 7 NON SIG

0.028157162 1.000 8 NON SIG

0.034111043 1.000 9 NON SIG

0.047294771 1.000 10 NON SIG

0.055 1.000 11 NON SIG

0.064162169 1.000 12 NON SIG

0.066751148 1.000 13 NON SIG

0.090926522 1.000 14 NON SIG

0.091 1.000 15 NON SIG

0.091 1.000 16 NON SIG

0.092197371 1.000 17 NON SIG

198

p p' rank outcome

0.092618697 1.000 18 NON SIG

0.093 1.000 19 NON SIG

0.093 1.000 20 NON SIG

0.093144364 1.000 21 NON SIG

0.139008454 1.000 22 NON SIG

0.151141808 1.000 23 NON SIG

0.197944699 1.000 24 NON SIG

0.208465927 1.000 25 NON SIG

0.249 1.000 26 NON SIG

0.249 1.000 27 NON SIG

0.249 1.000 28 NON SIG

0.249486278 1.000 29 NON SIG

0.262538626 1.000 30 NON SIG

0.264894005 1.000 31 NON SIG

0.269435558 1.000 32 NON SIG

0.274805211 1.000 33 NON SIG

0.278 1.000 34 NON SIG

0.298878768 1.000 35 NON SIG

0.299807658 1.000 36 NON SIG

0.305 1.000 37 NON SIG

0.340694511 1.000 38 NON SIG

199

p p' rank outcome

0.345 1.000 39 NON SIG

0.355 1.000 40 NON SIG

0.378 1.000 41 NON SIG

0.405 1.000 42 NON SIG

0.410892438 1.000 43 NON SIG

0.420260642 1.000 44 NON SIG

0.453914731 1.000 45 NON SIG

0.464 1.000 46 NON SIG

0.481 1.000 47 NON SIG

0.487328399 1.000 48 NON SIG

0.487328399 1.000 49 NON SIG

0.516 1.000 50 NON SIG

0.538 1.000 51 NON SIG

0.553938166 1.000 52 NON SIG

0.572 1.000 53 NON SIG

0.581854271 1.000 54 NON SIG

0.589 1.000 55 NON SIG

0.62 1.000 56 NON SIG

0.625 1.000 57 NON SIG

0.64 1.000 58 NON SIG

0.716 1.000 59 NON SIG

200

p p' rank outcome

0.771 1.000 60 NON SIG

0.775 1.000 61 NON SIG

0.788685491 1.000 62 NON SIG

0.805 1.000 63 NON SIG

0.805378922 1.000 64 NON SIG

0.958246644 1.000 65 NON SIG

1 1.000 66 NON SIG

1 1.000 67 NON SIG

1 1.000 68 NON SIG

1 1.000 69 NON SIG

1 1.000 70 NON SIG

1 1.000 71 NON SIG

1 1.0000000000 72 NON SIG

201

TRC Physics Assessment Statistical Tests

p p' rank outcome

4.57E-09 0.0000002742 1 SIG

1.11288E-08 0.0000006566 2 SIG

1.15E-08 0.0000006670 3 SIG

2.66957E-08 0.0000015217 4 SIG

1.10E-06 0.0000616000 5 SIG

2.11E-06 0.0001160500 6 SIG

2.12E-05 0.0011448000 7 SIG

2.256E-05 0.0011958242 8 SIG

6.86E-05 0.0035672000 9 SIG

0.000104185 0.0053134384 10 SIG

0.000183 0.0091500000 11 SIG

0.000217 0.0106330000 12 SIG

0.000255653 0.0122713455 13 SIG

0.000321 0.0150870000 14 SIG

0.000353 0.0162380000 15 SIG

0.00056 0.0252000000 16 SIG

0.000965 0.0424600000 17 SIG

0.001003 0.0431290000 18 SIG

0.001031048 0.0433040160 19 SIG

0.001092561 0.0447950010 20 SIG

202

p p' rank outcome

0.001208752 0.0483500712 21 SIG

0.001436 0.0560040000 22 NON SIG

0.004837 0.1838060000 23 NON SIG

0.007294 0.2698780000 24 NON SIG

0.00798 0.2872800000 25 NON SIG

0.008248 0.2886800000 26 NON SIG

0.008265 0.2886800000 27 NON SIG

0.009093937 0.3000999303 28 NON SIG

0.010597 0.3391040000 29 NON SIG

0.010947573 0.3393747526 30 NON SIG

0.015787 0.4736100000 31 NON SIG

0.019883 0.5766070000 32 NON SIG

0.021468 0.6011040000 33 NON SIG

0.02231 0.6023700000 34 NON SIG

0.043659 1.000 35 NON SIG

0.043857 1.000 36 NON SIG

0.055521 1.000 37 NON SIG

0.056842047 1.000 38 NON SIG

0.069392 1.000 39 NON SIG

0.073973 1.000 40 NON SIG

0.080607 1.000 41 NON SIG

203

p p' rank outcome

0.080874702 1.000 42 NON SIG

0.090729 1.000 43 NON SIG

0.091649 1.000 44 NON SIG

0.121158 1.000 45 NON SIG

0.134071 1.000 46 NON SIG

0.140535 1.000 47 NON SIG

0.180255 1.000 48 NON SIG

0.18709 1.000 49 NON SIG

0.195153 1.000 50 NON SIG

0.198837 1.000 51 NON SIG

0.227304 1.000 52 NON SIG

0.351630592 1.000 53 NON SIG

0.37308 1.000 54 NON SIG

0.454098 1.000 55 NON SIG

0.548742 1.000 56 NON SIG

0.55326 1.000 57 NON SIG

0.640937712 1.000 58 NON SIG

0.847927 1.000 59 NON SIG

0.964052 1.0000000000 60 NON SIG

204

APPENDIX M

TEXAS ESSENTIAL KNOWLEDGE AND SKILL FOR FORCE AND MOTION

ASSESSED ON 2013-2014 SCIENCE STATE OF TEXAS ASSESSMENTS OF

ACADEMIC READINESS

Sixth Grade Science


6.8 C 1 0.43

6.8 D 1 0.62



7.7 A 1 0.72


TEKS Number of Questions Average*

8.6 A 4 0.73 (0.33, 0.62, 0.63, 0.86)

8.6 B 1 0.54

8.6 C 2 0.75 (0.63, 0.86)

*Averages in parenthesis are for individual questions

205

REFERENCES

Abdi, H., (2010). Holm’s Sequential Bonferroni Procedure. In N. Salkind (Ed.), Encyclopedia of

research design. (pp 1-8). Thousand Oaks, CA: Sage.

Abell, S. K., (2007). Research on science teacher knowledge. In S.K. Abell & N. G. Lederman

(Eds.), Handbook of research on science education. (pp 1105-1150). Hillsdale, NJ:

Lawrence Erlbaum.

Allen, M., & Coole, H. (2012). Experimenter confirmation bias and the correction of science

misconceptions. Journal of Science Teacher Education, 23(4), 387-405.

American Association for the Advancement of Science. (1993). Benchmarks for science literacy,

Project 2061. New York: Oxford University Press.

American Association of Physics Teachers. (1988). The role, education, and qualifications of the

high school physics teacher. College Park: AAPT Committee on Special Projects for

High School Physics.

Arzi, H. J., & White, R. T. (2007). Change in teachers’ knowledge of subject matter: A 17- year

longitudinal study. Science Education, 92(2), 221-251.

Asikainen, M. A., & Hirvonen, P. E. (2014). Probing pre- and in-service physics teachers’

knowledge using the double-slit thought experiment. Science & Education, 23(9), 1811-

1833.

Berg, T., & Brouwer, W. (1991). Teacher awareness of student alternate conceptions about

rotational motion and gravity. Journal of Research in Science Teaching, 28(1), 3-18.

Burgoon, J. N., Heddle, M. L., & Duran, E. (2009). Re-examining the similarities between

teacher and student conceptions about physical science. Journal of Science Education,

21(1), 859-872.

Cochran, K., & Jones, L. (1998). The subject matter knowledge of preservice science teachers. In

B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education (pp. 707–

718). Dordrecht, The Netherlands: Kluwer Academic Publishers.

Deng, Z. (2007). Knowing the subject matter of a secondary-school science subject. Journal of

Curriculum Studies, 39(5), 503-505.

Daehler, K. R., Shinohara, M., & Folsom, J. (2011). Making sense of science, Force and motion

for teachers of grades 6-8. San Francisco, CA: WestEd.

206

Etkina, E. (2010). Pedagogical content knowledge and preparation of high school physics

teachers. Physical Review Special Topics- Physics Education Research, 6(2), 020110-1.

Galili, I., & Lehavi, Y. (2006) Definitions of physical concepts: A study of physics teachers’

knowledge and views. International Journal of Science Education, 28(5), 521-541.

Gaetano J. (2013). Holm-Bonferroni sequential correction: An EXCEL calculator (1.2)

[Microsoft Excel workbook].

Ginns, I. S., & Watters, J. J. (1995). An analysis of scientific understandings of preservice

elementary teacher education students. Journal of Research in Science Teaching, 32(2),

205-222.

Gönen, S. (2008). A study on student teachers’ misconceptions and scientifically acceptable

conceptions about mass and gravity. Journal of Science Education & Technology, 17(1),

70-81.

Harrell, P. E. (2010). Teaching an integrated science curriculum: Linking teacher knowledge and

teaching assignments. Issues in Teacher Education, 19(1), 145-165.

Hashweh, M. Z. (1987). Effects of subject-matter knowledge in the teaching of biology and

physics. Teaching & Teacher Education, 3(2), 109-120.

Hestenes, D. & Halloun, I. (1995) Interpreting the force concept inventory: A response to March

1995 critique by Huffman and Heller. The Physics Teacher, 33, 502-506.

Hestenes, D., Wells, M., & Swackhamer, G. (1992). Force concept inventory. The Physics

Teacher, 30, 141-158.

Hill, H. C., Rowan, B., & Ball D. L. (2005). Effects of teachers’ mathematical knowledge for

teaching on student achievement. American Educational Research Journal, 42(2), 371-

406.

Hill, J.G., & Gruber, K. J. (2011). Education and certification qualifications of departmentalized

public high school-level teachers of cores subjects: Evidence from the 2007-08 schools

and staffing survey. U.S. Department of Education. Institute of Education Sciences,

National Center for Education Statistics.

Jackson, J. (2016, October). Evaluation Instruments. Retrieved from

http://modeling.asu.edu/R&E/Research.html.

Kikas, E. (2004). Teachers’ conceptions and misconceptions concerning three natural

phenomena. Journal of Research in Science Teaching, 41(5), 435-448.

207

Kind, V. (2014). A Degree is Not Enough: A quantitative study of aspects of pre-service

teachers’ chemistry content knowledge. International Journal of Science Education,

36(8), 1313-1345.

Kind, V., & Kind P. M. (2011). Beginning to teach chemistry: How personal and academic

characteristics of pre-service science teachers compare with their understandings of basic

chemical ideas. International Journal of Science Education, 33(15), 2123-2158.

Kruger, C., Palacio, D., & Summers, M. (1990). A survey of primary school teachers’

conceptions of force and motion. Educational Research, 32, 83-95.

Kruger, C., Palacio, D., & Summers, M. (1992). Surveys of English primary teachers’

conceptions of force, energy, and materials. Science Education, 76(4), 339-351.

Kruger, C. J., Summers, M. K., & Palacio, D. J. (1990). An investigation of some English

primary school teachers’ understanding of the concepts of force and gravity. British

Educational Research Journal, 16 (4), 383-397.

Mäntylä, T., & Nousiainen (2014). Consolidating pre-service physics teachers’ subject matter

knowledge using didactical reconstructions. Science & Education, 23(8), 1583-1604.

National Research Council. (2005). How students learn: Science in the classroom. Washington,

DC: National Academy Press.

Next Generation Science Standards. (2013). Next Generation Science Standards: For States, By

States MS. Forces and Interactions. Retrieved from http://www.nextgenscience.org/topic-

arrangement/msforces-and-interactions.

Neuschatz, M., & McFarling, M. (2000). Background and professional qualifications of high-

school physics teacher. The Physics Teacher, 38(2), 98-104.

Ohle, A., Boone, W. J., & Fischer, H. E. (2015). Investigating the impact of teachers’ physics ck

on students outcomes. International Journal of Science and Mathematics Education,

13(6), 1211-1233.

Panizzon, D., Westwell, M., & Elliott, K. (2010). Exploring the profile of teachers of secondary

science: What are the emerging issues for future workforce planning? Teaching Science,

56(4), 18-24.

Poutot, G. & Blandin, B. (2015) Exploration of students’ misconception in mechanics using the

FCI. American Journal of Educational Research, 3(2), 116-120.

208

Public Education Information Management System (2017). Student enrollment reports statewide

region totals, 2013-2014. Retrieved from

https://rptsvr1.tea.texas.gov/adhocrpt/adste.html.

Ralya, L. L., & Ralya L. L. (1938). Some misconceptions in science held by prospective

elementary teachers. Science Education, 22(5), 244-251.

Sadler, P. M., Sonnert, G., Coyle, H. P., Cook-Smith, N., & Miller, J. L. (2013). The influence of

teachers’ knowledge on student learning in middle school physical science classrooms.

American Educational Research Journal, 50(5), 1020-1049.

Shapiro, J.R. & Williams, A.M. (2012). The role of stereotype threats in undermining girls’ and

women’s performance and interest in STEM fields. Sex Roles, 66(3), 175-183.

Snyder, T.D., de Brey, C., and Dillow, S.A. (2016). Digest of Education Statistics 2015 (NCES

2016-014). National Center for Education Statistics, Institute of Education Sciences, U.S.

Department of Education. Washington, DC.

Texas Education Agency (2015). Texas Essential Knowledge and Skills. 19 TAC Chapter 231.

Requirements for Public School Personnel Assignments. Retrieved from

http://ritter.tea.state.tx.us/sbecrules/tac/chapter231/index.html.

Texas Education Agency (2010). 19 TAC Chapter 112. Texas Essential Knowledge and Skills for

Science. Retrieved from http://ritter.tea.state.tx.us/rules/tac/chapter112/.

Texas Regional Collaborative for Excellence in Science and Mathematics Teaching (2013).

Texas Regional Collaborative Mathematics and Science RFA 2013-2014 Grants. Austin,

TX: Author.

Thornton, R. K., & Sokoloff, D. R. (1998). Assessing student learning of Newton’s laws: The

force and motion conceptual evaluation and the evaluation of active learning laboratory

and lecture curricula, American Journal of Physics, 66(4), 338-352.

Trumper, R. (1991). A longitudinal study of physics students’ conceptions of force in pre-service

training for high school teachers. European Journal of Teacher Education, 22(2), 247-

256.

Urquhart, M. (2017, April, 4). Email communication with K Busby.

Wayne, J. A., & Youngs, P. (2003). Teacher characteristics and student achievement gains: A

review. Review of Educational Research, 73(1), 89.

209

Weinburgh, M. (1995), Gender differences in student attitudes toward science: A meta-analysis

of the literature from 1970 to 1991. Journal of Research in Science Teaching, 32, 387–

398.

Yip, D. Y., Chung, C. M., & Mak, S. Y. (1998). The subject matter knowledge in physics related

topics of Hong Kong junior secondary science teachers. Journal of Science Education

and Technology, 7(4), 319-328.

210

BIOGRAPHICAL SKETCH

Karin Therese Burk Busby was born in Fort Worth, Texas and raised in Carrollton, Texas. After

completing her schoolwork at Ursuline Academy of Dallas in Dallas, Texas in 2000, Karin

attended Austin College in Sherman, Texas. She received a Bachelor of Arts with a major in

communication arts from Austin College in May 2004. Upon graduation, she attended Dallas

Christian College in Farmers Branch, Texas for teacher preparation. She began teaching 5th

grade

in the fall of 2004. She taught 5th

grade for two years. In September 2006, she entered the

Graduate School of Southern Methodist University where she studied science and gifted

education. She married her husband, David Busby, in July 2007. She received a Master of

Education in science and gifted studies in May 2009. She graduated with honors. She delivered

her first child, Michael David, in January 2011. He was joined by his sister, Anastasia Claire in

September 2012. In July 2013, she entered the Graduate School of The University of Texas at

Dallas where she studied science education. She joined the Texas Regional Collaboratives for

Excellence in Science and Mathematics Teaching in May 2013. While attending graduate school,

she welcomed her third child, a son, Alexander Charles in December 2015. At time of

publication, Karin is expecting twin boys in the spring of 2017.

CURRICULUM VITAE

Karin Burk Busby

Education The University of

Texas at Dallas

Master in the Art of

Teaching

May 2017

Science Education

Southern Methodist

University

Master of Education

May 2009

Gifted and Science

Education

Austin College

Bachelor of Arts

May 2004

Teaching

Experience

Carrollton Farmers Branch Independent School District, Creekview

High School

Physics/ IPC (2015- Present)

IPC Curriculum Writer (2016- Present)

Richardson Independent School District, Lake Highlands High School

Physics/ Principles of Technology Teacher (2013-2015)

Physics Team Lead(2013-2014)

ESL IPC Teacher (2014-2015)

Carrollton- Farmers Branch Independent School District, DeWitt

Perry Middle School

6th

, 7th

, & 8th

Grade Science/ESL/ G-T Teacher (2006-2013)

6th

Grade Team Lead (2007-2008)

Science Department Chair-Teacher leader (2009-2012)

Middle School Science Curriculum Writer (2005-2007)

Carrollton- Farmers Branch Independent School District, Central

Elementary

5th

Grade Self Contained ESL / Gifted-Talented Teacher

(2004-2006)

Presentations

TRC Annual Meeting – June 16-18, 2015

Presenter- Just Graph It: Teaching Motion Graphs Conceptually.

Presented 5E lesson creating and evaluating kinematic graphs

conceptual.

Presenter- Is it Science or Math? Strategies to teach mathematical

concepts in the science classroom

Presented and demonstrated four methods to teaching mathematical

skills in the science classroom. Included significant figures,

balancing equations, and formula manipulation.

Presenter- Google it All.

Presented and demonstrated multiple uses for google apps for

education in the classroom

STAT CAST - Nov. 2014

Presenter- Graphing and You: Teaching Motion Graphs

Conceptually. Presented 5E lesson creating and evaluating

kinematic graphs conceptual.



Presentations Dallas/Fort Worth Metroplex MiniCAST- Feb 1, 2014

Presenter- Graphing and You: Teaching Motion Graphs

Conceptually.

Presenter- Don’t Flip out- 5 ways to Flip your Classroom.

Presented and demonstrated 5 ways to integrate technology into the

classroom. Include a variety of methods



World Council for Gifted and Talented Children 17th

Biennial World

Conference 2007 Aug. 5-10, 2007

Presenter- Building Vocabulary.

Presented action research over building science vocabulary for ELL

Gifted Students as part of SMU.

Honors/

Affiliations

Pi Lambda Theta Member

SMU Since 2009

Texas Regional Collaborative for Excellence in Science and

Mathematics

UT Dallas 2013-2016

by karin burk busby

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