using felder’s index of learning styles in the classroom kay c dee, glen a. livesay department of...

Using Felder’s Index of Learning Styles

in the Classroom

Kay C Dee, Glen A. Livesay

Department of Biomedical EngineeringTulane University, New Orleans, LA 70118 USA

Who, What, Why?

We are not here to tell you “how you should teach.”

The Dark Side of Teaching Well

You’llNEVER gettenure!!

Of course, this varies with institution and priorities.

“You prep for classes your way, Harris, I’ll prep for classes my way.”

Overview

Broad Questions:

What are some of the different ways that students take in and process information?

Which learning styles are favored by:• many students?• the teaching style of many professors?

What can we do to reach a full spectrum of learning styles?

Outline

I What is Felder’s Index of Learning Styles (ILS)? Where did it come from?

II What has the ILS told us about learning styles so far?

III Let’s be fair - are there concerns or critiques associated with the ILS?

IV How can we use learning style information to make informed teaching style choices?

V Does using ILS information in the classroom actually make a difference?

Learning Styles

• perception, acquisition, processing, and retention of information

• both cognitive and affective behaviors

• individuality

• maximal learning when instruction capitalizes on an individual’s learning style preferences

- the matching hypothesis

There are several definitions of “learning style.”

Generally, these definitions include aspects of:

Felder’s Index of Learning Styles

• Relatively short questionnaire

• Specifically formulated with engineering students in mind

• Does not require professional scoring and interpretation

• Collected data/publications available [1]

• Dimensions well-suited for discussions of teaching as well as learning

Active Reflective

Sensor IntuitorVisual Verbal

Sequential Global

Index of Learning Styles: Overview

ILS Domains

VisualVisual Verbal Verbal

• Pictures

• Diagrams

• Flow charts

• Plots

• Spoken words

• Written words

• Formulas

“Show me the systems you’re talking about.”

“Explain what’s going on inside the systems.”

ILS Domains

ActiveActive Reflective Reflective

• Tends to process information while doing something active

• Likes group work

• May start tasks prematurely

• Tends to process information introspectively

• Likes independent work

• May never get around to starting tasks

“Let’s just try it out.” “Let’s make sure we’ve thought this through.”

ILS Domains

SensorSensor Intuitor Intuitor

• Focuses on sensory input - what is seen, heard, touched, etc.

• Prefers concrete information: facts and data

• Focuses on ideas, possibilities, theories

• Prefers more abstract information: theory and models

“How does this class relate to the real world?”

“All we did were plug-and-chug assignments.”

ILS Domains

SequentialSequential Global Global

• Can function with partial understanding

• Makes steady progress

• Good at detailed analysis

• Needs to see the big picture

• May start slow and then make conceptual leaps

• Good at creative synthesis

“I need to focus on one part of the project and get it done - then I can move onward.”

“I need to see how this all fits together before I can start the project.”

What We’re NOT Saying

We don’t mean to “put people in boxes.”

What We’re NOT Saying

We don’t mean to “put people in boxes.”

Most people, however, have somepreferences (mild, moderate, or strong).

Everyone learns both actively and reflectively, both visually and verbally, etc.

Origins of ILS Domains

Sensor - Intuitor

• Carl Jung’s theory of psychological types:sensing and intuition modes of perception

• Myers-Briggs Type Indicator:sensors and intuitors as problem solvers

• Kolb’s experiential learning model:concrete experience and abstract conceptualization

Origins of ILS Domains

Active - Reflective

• Myers-Briggs Type Indicator: extrovert and introvert

• Kolb’s experiential learning model: active experimentation and reflective observation

Kolb’s Cycle

ConcreteExperience

Makingsomething new

ReflectiveObservation

Internalizingexperience

AbstractConceptualization

Developingconcepts

ActiveExperimentation

Doing it

Doing

Feeling

Watching

Thinking

Kolb’s Learning Model

ConcreteExperience




Diverger

AssimilatorConverger

Accommodator

Kolb’s and Felder’s Models

ConcreteExperience


Diverger


Accommodator

ReflectiveActive

Kolb’s and Felder’s Models

Diverger


Accommodator

ReflectiveActive

Sensor

Intuitor

Active Reflective


Sequential Global

Index of Learning Styles: Overview

Faculty Learning Styles

73

38 36

27

0

10

20

30

40

50

60

70

80

90

100

Visual Active Sensor Global

Preferred Learning Style

Perc

en

t of

Pop

ula

tion

n=568 [2]

(national)83

2517

75 n=12(Tulane BMEN)

Learning Styles - Tulane

0

10

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30

40

50

60

70

80

90

100



Perc

en

t of

Pop

ula

tion

83

2517

75

n=12(BMEN faculty)

88

6260

52

n=255 [3]

(ENGR students)

Learning Styles of Other Engineers

Tulane, Engr (n=255) [3]

0

10

20

30

40

50

60

70

80

90

100



Perc

en

t of

Pop

ula

tion

88

62 60

52

80

69

86

U Western Ontario, Engr (n=858) [4]

U Michigan, Chem Engr (n=143) [5]

Ryerson Univ, Elec Engr (n=87) [6]

69 67

53

59 57

72

3328 28

Learning Styles and Gender

0

10

20

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40

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100



Perc

en

t of

Pop

ula

tion

89

72

58

35

Males, Engr(n = 692)

69

59 61

25

Females, Engr(n = 135)

University of Western Ontario [7]

Learning Styles and Gender


Females, Engr(n = 135, 16.3%)

61

48

0

10

20

30

40

50

60

70

80

90

100

Visual Active Sensor GlobalPreferred Learning Style

Perc

en

t of

Pop

ula

tion

89

35

69

25

9184

72

59 56

78

5861

50

67

University of Western Ontario


Females, Engr(n = 63, 32.8%)

Tulane University

Index of Learning Styles: Critiques

Concerns which have been noted regarding the use of the Index of Learning Styles:

• Doesn’t predict academic performance. [8]

• The matching hypothesis - just a hypothesis - is difficult to prove. [9,10]

• Lacks statistical validation. [8]

• Bunch’a hooey. [11]

Predicting Academic Performance

We found little or no correlation between SAT score and cumulative GPA at the end of the sophomore year.

R2 = 0.16

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

800 900 1000 1100 1200 1300 1400 1500 1600

SAT score

GP

A

Tulane sophomores in Statics, all disciplines, n=98 [3]

StrongMod

MildMild

ModStrong

1500+ (4)

1400-1499 (21)

1300-1399 (40)

1200-1299 (24)

1100-1199 (5)

Below 1100 (4)

0.00

0.10

0.20

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0.40

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0.60

SAT Score

SensorIntuitor

Pe

rce

nt

of

Po

pu

lati

on

Intuitors Outperformed Sensors on SAT

Tulane sophomores, Statics group, n=98 [3]

ILS = Academic Performance? No.

Some concerns regarding the ILS appear to arise from a misapplication of the inventory:

• It was not developed to enable predictions of academic performance.

• It was not developed as a selection tool to determine ‘who should be an engineer’.

Activities or tests which engage only one learning style may not illustrate the true potential or abilities of a group of students.

Testing the Matching Hypothesis

B = f (P, E)Behavior-person-environment paradigmleads to the idea of optimizing the instructional environment for optimal learning.

Testing the matching hypothesis is difficult -there are many learning style schemes to test, not all easily comparable to each other.

Meta-analyses [9,10] have claimed that a majority of published studies support the matching hypothesis.

Statistical Validation

Validity(Accuracy)

item total correlation (ITC)

coefficient

Reliability(Precision)

Statistical Analysis

SPSS was used to:

• Calculate reliability coefficients for each learning style domain.

- a larger value implies a more internally consistent construct.

• Perform item and factor analyses to determine which items were most strongly correlated with each other and how many factors were present within each domain.

- removing poorly correlated items increases

Active-Reflective

Sensor-Intuitor

Visual-Verbal

Sequential-Global

ILS Domain

0.564 0.718 0.596 0.544

n=248 n=246 n=242 n=244

Reliability () of ILS Domains

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Alp

ha

attitude[12]

achievement

Reliability () of Core ILS Domains

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Active-Reflective

Sensor-Intuitor

Visual-Verbal

Sequential-Global

ILS Domain

Alp

ha

0.582 0.744 0.679 0.622

n=249 n=247 n=248 n=248

achievement

attitude[12]

Measures of Reliability

is commonly used for estimating reliability (mean of split halves). Challenges:

- low number of questions (11 per domain)- mutually exclusive (dichotomous) questions- no ‘right’ answer to questions

Test-retest reliability is what is estimating: to what degree will people obtain the same ILS scores if they take the test again? Challenges:

- requires multiple administrations - if too long between, people may change- if too short between, people may remember

test

Test-Retests Are Correlated Over Time

0

0.3

0.4

0.5

0.6

0.7

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0.9

Four(n=24)

Seven(n=40)

Twelve(n=26)

Sixteen(n=24)

Months Between Test - Retest

Corr

ela

tion

Coeffi

cie

nt

Betw

een

Test

- R

ete

st

Active-ReflectiveSensor-IntuitorVisual-VerbalSequential-Global

§ § §

Population includessame students

§NOT significant (p>0.05)

0

2

4

6

8

10

12

14

1009050 60 70 800 10 20 30 40

Specific Answers Correlated Over Time

% Students Repeating Original Answerson a Given Question in Retest

Nu

mb

er

of

Qu

esti

on

s*

Test-Retest Data(16 month interval, n=24)*Out of 44 questions.

More ‘Repeatable’ Questions

Test-Retest Data(16 month interval, n=24)

37) I am more likely to be considered: a) outgoing b) reserved

41) The idea of doing homework in groups, with one grade for the entire group: a) appeals to me b) does not appeal to me

43) I tend to pictures places I have been: a) easily and fairly b) with difficulty and without accurately much detail

Greater than 90% of students answered test-retest identically on these questions

Less ‘Repeatable’ Questions

Test-Retest Data(16 month interval, n=24)

50% or less of students answered test-retest identically on these questions

17) When I start a homework problem, I am more likely to: a) start working on the b) try to fully understand the solution immediately problem first

36) When I am learning a new subject, I prefer to: a) stay focused on the b) try to make connections between

subject, learning as that subject and related subjects much about it as I can

44) When solving problems in a group, I would be more likely to: a) think of steps in b) think of possible consequences or the solution process applications in a range of areas

16) When I’m analyzing a story or a novel: a) I think of the incidents b) I know the themes and must and put them together go back to find the incidents

Validation Study Summary

The ILS satisfies general guidelines for reliability across all domains.

• between 0.54 and 0.72 with all questions. increased in all domains with “core” questions,

especially visual/verbal, sequential/global.

Test-retest scores in all domains were significantly correlated over various intervals.

• correlation was highest at shortest interval, and generally reduced with longer intervals.

Recommendations

We believe Felder’s ILS to be a useful, appropriate, statistically-acceptable tool for characterizing learning preferences and discussing teaching methods.

There is (as always) some room for improvement. We encourage others to test new questions, work on statistical validation - especially when the ILS is administered to large numbers of students at one time - and share their findings.

Additional Comments on Validity

The nature of the ILS - to force choices for a set of individual questions - necessarily spreads out responses.

- Increases in variance are directly related to lower values for .

Guidelines for statistical validity developed for tests of achievement (e.g., minimum of 0.7) should not be blindly applied to the ILS.

“An instrument is valid if it measures what it is intended to measure”.

Dimensions of Teaching and Learning [13]


CorrespondingTeaching Style

VisualVerbal Input Presentation

VisualVerbal

ActiveReflective Processing

StudentParticipation

ActivePassive

SensorIntuitor Perception Content

ConcreteAbstract

SequentialGlobal Understanding Perspective

SequentialGlobal

The “Traditional” Lecture Format

The traditional engineering lecture format (teaching style) tends to be (almost exclusively):

INTUITIVE

SEQUENTIAL

PASSIVE

VERBAL

Learning Styles and “Traditional” Lectures

The traditional lecture format does match some students’ preferred learning styles, however, the majority of students tend to prefer:

VISUAL, ACTIVE, and SENSING approaches

In fact, the teaching style utilized in the traditional lecture does not necessarily match the preferred learning styles of professors!

Teach to a Student’s Style, or Against? [14]

The matching hypothesis: teaching to a student’s learning style provides the best opportunity for learning.

- a student functioning in their preferred modes is focused on learning and not on overcoming a barrier.

However, should we teach to the strengths of the student, or work to help them develop in their areas of weakness (less preferred modes)?

- students will need to be able to function in different modalities at different times, e.g. both actively and reflectively, both visually and verbally, etc.

Teach to Many Preferred Styles [14]

With the diversity of learning styles in the classroom, do we teach to a single, preferred learning style? If so, which one?

- teaching to a single, preferred learning style (or using a single style to teach) will benefit those FEW students who prefer that chosen style.

The best solution is likely to utilize a variety of instructional styles and modes of delivery in a course.

- enable ALL of the students to function in their preferred modes some of the time, and also encourage

development in less-preferred modes.

Teaching Styles - Reaching Styles

Good news:Traditional lecturing does address several categories of learning styles.

VERBAL, (REFLECTIVE), SEQUENTIAL, and INTUITOR

Better news:Engaging multiple learning styles does NOT require complete restructuring of a course, or eliminating traditional lectures.

Still better news:Teaching methods that address styles short-changed by traditional methods (e.g. VISUAL, ACTIVE, GLOBAL, and SENSOR) often accommodate multiple styles.

For example: [15]

• Motivate theoretical material with prior presentation of phenomena that the theory will help explain, and problems the theory will be used to solve (SENSOR, GLOBAL).

• Balance concrete information (SENSOR) with conceptual information (INTUITOR) in all courses.



• Complement oral and written explanation of concepts (VERBAL) with extensive use of sketches, plots, etc. and physical demonstrations where possible (VISUAL).

• Illustrate abstract concepts with at least some numerical examples (SENSOR), in addition to traditional algebraic examples (INTUITOR).

• Use physical analogies and demonstrations to improve students’ grasp of magnitudes of calculated quantities (SENSOR, GLOBAL).

• Demonstrate the logical flow of individual course topics (SEQUENTIAL), and also highlight connections to other material in the course and other courses, in other disciplines, and in everyday experience (GLOBAL).


• Provide time in class for students to think about material being presented (REFLECTIVE) and for active participation (ACTIVE).

- pause during lecture to allow time for thinking and formulation of questions (reflective).

- assign 1-minute papers, where students write the most important point of the lecture and the most pressing

unanswered question (active and reflective).- assign brief, group problem-solving exercises where students work with neighbors (active and reflective).

• Encourage or mandate cooperation on homework, or through team projects (ACTIVE).


Teach the Cycle!Concrete

Experience




(Kolb’s Cycle, that is.)

Meta-Active Learning

What types of reasons might professors give for not using these ideas (for example: active learning exercises) in their courses?

2 minutes, Go!

Fears - Active Learning

TIME-CONSUMINGLOSE CONTROL OF THE CLASS

UNPREDICTABILITY

“How will I cover the syllabus?”

“What do I want to cover?”

“What do I want the students to be able to do?”

TOO MUCH EFFORT

Active Learning: Benefits

• Students cannot be ‘passive vessels’ - they must be engaged with the material

• Clearly informs instructor what studentsunderstand and what they don’t

• Shifts focus from professor to material(“sage on the stage” to “guide on the side”)

• Increases and personalizes student-professor interactions

Active Learning: Potential Drawbacks

• Students cannot be ‘passive vessels’ - they must be engaged with the material

• Clearly informs instructor what studentsunderstand and what they don’t

• Shifts focus from professor to material

The Whole Enchilada - DOES IT WORK?

A longitudinal study of chemical engineering students has shown that courses designed to accommodate a spectrum of learning styles:

• increased students’ confidence in their academic preparation [17]

• raised overall academic performance [16] (even in subsequent courses taught “traditionally” by other instructors [17])

• increased student retention [16]

• increased graduation rate [17]

Data from Tulane BMEN

We have modified several junior-level courses to address the sensing and active learning preferences of our students.

New lab components were made possible through a National Science Foundation “Course, Curriculum and Laboratory Improvement” grant.

A team of biomedical and psychology faculty designed an assessment questionnaire to be used as part of the evaluation plan.

TUBA Model

Tulane University Biomedical Assessment (TUBA) model

Five constructs:1. My perception of what happened in the course2. Laboratory or “laboratory-like” experiences3. How my skills and abilities were enhanced in

the course4. My assessment of the course5. The instructor

Administered to 134 students in Spring 2001, 113 students in Fall 2001, and 77 students in Spring 2002.

TUBA Model

53 “fill-in-the-blank” questions using the scale1. strongly disagree2. disagree3. neutral or undecided4. agree5. strongly agree

Example:1. This course included a number of “hands-on” projects or exercises. _____

Statistical validation for TUBA model was conducted using data from the three administrations. [18]

Assessing the Impact

Three courses were assessed in Spring 2001 and Spring 2002.

Longitudinal results were assessed by performing independent t-tests on each item.

The number of items which demonstrated significant (p < 0.05) improvement were summed and reported.

No items showed ‘negative improvement’ from Spring 2001 to Spring 2002.

Results

BMEN 340 - Spring 2001 and Spring 2002

ConstructItems

ImprovedTotal Items

Perception of course 5 8

Laboratory-like experiences

5 6

Skill and ability enhancement

5 16

Assessment of course 3 11

Instructor 5 12

What Happened?

BMEN 340 incorporated no hands-on activities in 2001 but added three laboratories in 2002, teaching students a new skill set (cell culture experiments).

Students in 2002 expressed higher ratings of their:• teamwork skills• interest in conducting research or working in

the area• confidence in their abilities• instructor’s knowledge of the material

QUIZ






Wrap-up and Summary


Active Reflective


Sequential Global

* Similar to stages of Kolb’s cycle.

*

*

Wrap-up and Summary


Students tend, in general to prefer visual, active, and sensing learning styles.

Not all populations of students (or faculty) are the same.

Wrap-up and Summary


Yes.

BUT: We believe Felder’s ILS to be a useful, appropriate, statistically-acceptable tool for characterizing learning preferences and discussing teaching methods. Nothing more, nothing less.

Wrap-up and Summary


There are many ways.

Start small. Try an approach more than once before giving up. Tell students what you are doing and why.

If you try only two things:• show pictures or models (visual)• provide short times to think and short times

to interact (reflective / active)

Wrap-up and Summary


Yes.

Thank you.

Acknowledgements

We thank:

• The Rose-Hulman Institute of Technology, for the opportunity to present this material.

• The students who participated in our studies, for their time and good will.

• Rich Felder, for mentoring and inspiration.

• The National Science Foundation for support provided by awards DUE-0088333, BES-9983931, and BES-0093969.

Time Into Lecture When Information Was Presented(minutes)

100

70

20

Perc

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0 10 50

Lecture

Lecture withactive breaks

R. Brent, R. Felder, J. Stice, National Effective Teaching Institute, 1998.

Active Learning and Info Retention

using felder’s index of learning styles in the classroom kay c dee, glen a. livesay department of...

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