Journal of Science Education and Technology, Vol. 5, No. 3, 1996

Predicting Academic Success in Electronics

B a r r y M . L u n t 1,2

This study was motivated by the desire to help potential electronics students answer the questions, 1) which program of study should I consider?; 2) how do I know if I'll be suc- cessful in that program? This study focused on: 1) identifying the best variables for pre- dicting academic success in electronics, 2) determining if abstract learning preference is an effective discriminator between the three main types of electronics programs, and 3) finding a model for predicting success in each electronic program. The results give validity to the commonly held opinion that a student's success in math and science in high school is a good predictor of their success in the three programs of electronics. The results also show that abstract learning preference is a valid discriminator between students in each of the three programs of electronics. Implications are provided.

KEY WORDS: Academic success; electronics; engineering technology.

INTRODUCTION

The choosing of a career is a very difficult de- cision for most students. In seeking to make such an important decision, many students avail themselves of help from peers and experienced adults, often in- cluding teachers and counselors. When called upon to counsel students, these experienced adults usually use their own experience or perspective as their only guide. But personal experience and perspective are inherently biased, and are not founded in research.

The field of electronics is likewise lacking in re- search-based information for career counseling. Elec- tronics is divided into three main programs at the college level: 1) Electronics Technology (ET), a 2- year, associate degree; 2) Electronics Engineering Technology (EET), a 4-year, baccalaureate degree; and 3) Electrical (or Electronic) Engineering (EE), also a 4-year, baccalaureate degree. Students wishing to major in electronics often seek to know which of these three programs would be best for them to enter. This paper gives the results of one effort at defining

1Department of Manufacturing and Engineering Technology, Brigham Young University, Provo, Utah.

2Correspondence should be addressed to Barry M. Lunt, 265 CTB, Brigham Young University, Provo, Utah 84602.

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a substantive difference between these programs and how this difference can be used to determine the best program for students wishing to major in electronics.

Program Differences

One of the basic differences between the three programs of electronics is the level of abstraction in the curriculum (Carlson, 1980; Carr, 1980; Cheshier, 1985; Kenyon, 1985; Ritz et al., 1985). ET programs present the material at a very concrete level, and generally all courses include labs for hands-on ex- perience with the concepts and their applications. EET programs are less concrete and more abstract in their presentation of material, but each course still usually includes lab experience. The most ab- stract of the three programs is EE, the course ma- terial is presented in an abstract way, with strong emphasis on mathematical models. Typically only about half of the courses include labs.

Table I (Ritz et al., 1985) depicts this relation- ship and places the three programs on a scale with educational programs for all members of a techni- cal/scientific team. As shown in this table, the em- phas is on abs t r ac t ions inc reases , go ing f r o m technician to engineering technologist to engineer.

1059-0145/96/0900-0235509.50/0 �9 1996 Plenum Publishing Corporation

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Table I. The Engineering Team: Relationship Between Positions, Education, and Nature of Work a

Position on team Craftsperson Technician Technologist Engineer Scientist t'

Education and training <

Nature of work <

Emphasis on mechanical skills < >Emphasis on abstractions >

Production, maintenance, operation < . . . . >Supervision, design, research >

aRitz et al., 1985 bDirection of arrows indicate increased emphasis.

Table II. Top 12 Predictor Variables

Rank Predictor Variable

1 2 3 4 5 6 7 8 9

10 11 12

High school GPA ACT-Math High school Science GPA General Aptitude Test Battery (GATB): Intelligence Group Embedded Figures Test High school Math GPA SAT--Math Cooperative School and College Ability Test: Quantitative Computer languages learned previously Meyers-Briggs Test Inventory: Introvert/Extrovert High school Rank ACT--Composite

Based on this difference, it appears that one way for students to decide which electronics program to study would be to determine the level of abstraction they prefer in their education and training.

RESEARCH QUESTIONS

This research was guided by the following re- search questions: (a) What are the best predictor variables for predicting academic success in elec- tronics? (b) Is abstract learning preference an effec- tive discriminator between students in the three main types of electronics programs? and (c) What is the best multiple-regression model that can be de- rived for predicting success in each of the three types of electronics programs?

METHODS

Before any surveys were conducted, existing research was studied in a meta-analysis of predictor variables shown to be successful in predicting aca- demic success in scientific and technical programs.

This meta-analysis found 236 predictor variables, which were then categorized and evaluated for their success in predicting academic success. The purpose of this meta-analysis was to learn all the types of predictor variables previously studied, evaluate their performance in predicting academic success, and choose from them those predictor variables most likely to perform well in predicting academic success in electronics. The top 12 predictor variables are shown in Table II.

It was desirable to use predictor variables that are readily available, and that do not require the stu- dents to take additional tests. It was noted that seven of the top predictor variables are available from high school transcripts, assuming the students took the ACT or SAT test. Therefore, data for six of these seven predictor variables (high school GPA; ACT-math; high school science grade; high school math grade; high school rank; and ACT-composite) were taken from the subjects' high school tran- scripts. SAT-math was not used due to its high cor- relation with ACT-math.

It was also desirable to measure the subjects' abstract learning preference, to provide a parameter for answering the second research question (see

Predicting Academic Success in Electronics 237

later). The survey chosen to measure this was the Learning Styles Inventory (LSI), by D. A. Kolb (Kolb, 1985). This survey produces two scores: Ab- stract Conceptualization versus Concrete Experience (AC-CE), and Active Experimentation versus Re- flective Observation (AE-RO). It was presumed that students with preference for higher levels of abstract presentation would have higher AC-CE scores.

The subjects for this research were randomly selected from each of the three electronics pro- grams: E% EEl; and EE. Two institutions were se- lected to represent each program. The subjects met the following criteria: (a) they must have had a de- clared major of El; EEl; or EE; and (b) they must have already completed two years of college. The number of subjects from each institution was pro- portional to the size of their particular electronics program. The programs in the study were ET at Snow College and at Utah Valley State College; EET at Weber State University and Brigham Young University; and EE at Utah State University and Brigham Young University.

A total of 149 subjects participated in the re- search: 46 in ET, 50 in EEl; and 43 in EE. Each subject completed the LSI survey, and a copy of their high school and college transcript was ob- tained. Four additional predictor variables were cho- sen for evaluation, due to their availability and potential; these predictor variables were not covered in the meta-analysis. These additional predictor vari- ables were: high school life science GPA; high school electronics GPA; high school computer science GPA; and ACT-natural science.

RESULTS

The three research questions focused on in this study were (1) What are the best predictor variables for predicting success in an electronics major?; (2) Is abstract learning preference an effective discrimi- nator between students in the three electronics pro- grams?; and (3) What is the best multiple-regression model that can be derived for predicting success in each of the three electronics programs? The answers to these questions are summarized later. Success was defined as both college major GPA (CMGPA) and college overall GPA (COGPA).

Question 1

The best four predictor variables for predicting academic success in ET are (1) ACT composite score (ACTCP), (2) High school natural science GPA (HSNSGPA), (3) ACT math score (ACTM), and (4) ACT natural science score (ACTNS). The best four predictor variables for predicting academic success in EET are (1) High school electronics GPA (HSEGPA), (2) HSNSGPA, (3) Abstract Conceptu- alization versus Concrete Experience (AC-CE), and (4) ACTNS. The best four predictor variables for predicting academic success in EE are (1) High school rank (HSRANK), (2) ACTM, (3) HSEGPA, and (4) HSNSGPA. The predictor variables are ranked by their correlation coefficient in Table III. The greater the absolute value of the correlation co- efficient, the greater the predictor variable's ability to predict academic success in electronics.

Table IIl. Ranked Predictor Variables (PV) and Correlation Coefficients (CC), by Program and Overall

ET EET EE Overall

Rank PV CC PV CC PV CC PV CC

1 ACTCP 0.587 a HSEGPA 0.362 HSRANK 0.393 b HSEGPA 0.203 2 HSNSGPA 0.496 a HSNSGPA 0.293 b ACTM 0.317 b HSNSGPA 0.198 b 3 ACTM 0.483 a AC-CE 0.272 b HSEGPA 0.299 HSCSGPA 0.178 4 ACTNS 0.482 a ACTNS 0.266 HSNSGPA 0.241 HSLSGPA 0.166 b 5 HSEGPA 0.452 HSRANK 0.220 ACTCP 0.233 HSGPA 0.146 6 HSLSGPA 0.427 a HSLSGPA 0.210 HSGPA 0.232 HSRANK 0.112 7 HSRANK 0.373 HSGPA 0.201 HSCSGPA 0.214 ACTNS 0.105 8 HSGPA 0.346 b ACTCP 0.160 HSLSGPA 0.159 ACTCP 0.101 9 HSCSGPA 0.318 HSCSGPA 0.143 HSMGPA 0.106 ACTM 0.097

10 HSMGPA 0.223 AE-RO -0.131 ACTNS 0.089 AC-CE 0.097 11 AC-CE 0.192 HSMGPA 0.105 AE-RO -0.019 HSMGPA 0.079 12 AE-RO 0.015 ACTM 0.063 AC-CE -0.009 AE-RO -0.037

aSignificant to et = .01. bSignificant to ~ = .05.

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Table IV. Summary of F-Test Comparison of Means

Means Level of Variable EE EET ET F-Statistic significance

H S G P A 3.523 3.105 2.885 18.58 <.0001 a H S R A N K 87.290 70.100 53.690 21.48 < .0001 a H S M G P A 3.317 2.915 2.632 11.11 < .0001 a HS NS GP A 3.538 3.094 2.741 13.56 <.0001 a HSLSGPA 3.422 3.129 2.830 7.18 .0011 a H S E G P A 3.556 3.304 2.851 2.86 .0662 H S C S GP A 3.537 3.341 3.270 1.01 .3695 A C T M 26.790 23.130 20.500 14.98 <.0001 a ACTNS 26.780 26.640 22.590 10.93 <.0001 a A C T C P 25.300 23.150 19.410 16.60 <.0001 a A C -C E 13.130 11.470 8.100 2.24 .1101 A E - R O 5.000 5.220 4.960 0.01 .9906 C M G P A 3.280 3.099 3.531 6.73 .0016 a C O G P A 3.272 3.376 3.451 8.97 .0002 a

aSignificant to ct = 0.05.

Table V. Simple Correlation Coefficients for Each Predictor Variable Against Response Variable, by Program and Overall

E T EET EE Overall

H S G P A .346 a .201 .232 .146 H S R A N K .373 .220 .393 a .112 H S M G P A .223 .015 .106 .079 H S N G P A .496 b .293 a .241 .198 a HSLSGPA .427 b .210 .159 .166 a HS E GP A .452 .362 .299 .203 HSCSGPA .318 .143 .214 .178 A C T M .483 b .063 .317 a .097 ACTNS .482 b .266 .089 .105 AC T C P .587/' .160 .233 .101 AC-CE .192 .272 a - .009 .097 A E - R O .015 -.131 - .019 - .037

aSignificant to tx = 0.05. bSignificant to ct = 0.01.

Question 2

Abstract learning preference was analyzed in three ways in this study. These analyses are summa- rized next.

First, abstract learning preference, as meas- ured by the AC-CE score from the LSI survey, was analyzed as a predictor variable for students in each electronics program, and for students overall. Ab- stract learning preference was shown to be an effec- tive discriminator between students in the three electronics programs, as summarized in Table IV. This table shows that EE students have the highest preference for abstract learning, followed respec-

tively by EET students and ET students. Although this discrimination was not found to be statistically significant, the results do support the general view that students who prefer abstract material will do best in EE programs, followed by less abstraction for EET; finally, those students who prefer the least ab- straction would be best served in ET programs.

Table IV also shows that the average for the AC-CE variable is highest for EE students, followed by EET students and ET students, respectively. This means that EE students do, as expected, have the highest preference for abstract learning, followed by EET and ET students, respectively, as was pointed out in the previous paragraph.

Predicting Academic Success in Electronics 239

Table VI. Comparison of LSI Survey Results with LSI Survey Established Means

Program Variable Value t Statistic Probability

EE AC-CE 8.10 1.919 <0.10 AE-RO 4.96 0.439 >0.25

EET AC-CE 11.47 4.784 a < 0.001 AE-RO 5.22 -0.688 >0.25

EE AC-CE 13.13 5.443 a <0.001 AE-RO 5.00 -0.13 >0.25

overall AC-CE 11.23 6.582 a <0.001 AE-RO 5.07 -0.396 >0.25

aSignificant to a = 0.01.

Table VII. Regression Equation Models for Each Program and Overall

Program Regression equation

ET

EET

EE

Overall

CMGPA = 2.512 + 0.162 (HSNSGPA) + 0.077 (HSLSGPA) + 0.0229 (ACTCP) - 0.00252 (AC-CE)

CMGPA = 0.787 + 0.378 (HSEGPA) + 0.110 (ACTNS) - 0.083 (ACTCP) 4- 0.021 (AC-CE)

CMGPA = - 0.408 + 0.190 (HSRANK) - 0.132 (HSLSGPA) 4- 0.332 (HSEGPA) + 0.052 (ACTCP)

CMGPA = 2.387 - 0.0053 (HSRANK) 4- 0.126 (HSNSGPA) + 0.129 (HSLSGPA) + 0.190 (HSCSGPA) 4- 0.0063 (AC-CE) - 0.0099 (AE-RO)

Second, abstract learning preference was ana- lyzed as a predictor variable for students in the three electronics programs. Abstract learning preference was found to be a statistically significant predictor variable for students in EET. As shown in Table V, the correlation coefficient (0.272) between AC-CE and CMGPA (College Major GPA) was found to be statistically significant beyond o~ = 0.05. However, abstract learning preference was not found to be a significant predictor variable for students in ET or EE. Again, this supports the interpretation given for the first way of analyzing abstract learning prefer- ence.

Third, electronics students were compared against established means for their abstract learning preference; this comparison is summarized in Table VI. Table VI shows that there are no significant dif- ferences between ET, EET and EE students and the established mean for the AE-RO variable. However,

students in EET and EE programs, as well as elec- tronics students overall, were found to differ signifi- cantly from the established mean for the AC-CE variable. The direction of this difference supports the general supposition that EE students have the greatest preference for abstract learning, followed by EET students and ET students, in that order.

Question 3

The best multiple-regression models for pre- dicting success in each of the three electronics pro- grams, as found through multiple regression, are summarized in Table VII. The effectiveness of these models is summarized in Table VIII; overall they are excellent models, as measured by their R 2, F-ratio, probability of the F-ratio, and shrinkage. The R 2 ba- sically describes the model's ability to predict aca- demic success; it ranges from 0 to 1, where 1 is

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Table VIII. Statistical Data Describing Effectiveness of Multiple-Regression Models

Program of Probability of study R 2 F-ratio F-ratio Shrinkage

ET 0.550 13.43 <0.001 0.189 EET 0.506 14.10 <0.001 0.110 EE 0.364 7.01 <0.001 0.186 Overall 0.227 7.70 <0.001 0.090

perfect. The probability of the given F-ratios basi- cally describes the probability that the given model came about by accident;; the lower the probability, the better. A probability of <0.001, as found for all four models, is extremely good.

Using these models, and the scores supplied by a student, an academic advisor could predict the student's academic success with a fair degree of con- fidence.

Implications

The implications for the findings of this re- search include the following:

1. The idea of using a student's high school science and math grades to predict their success in a program of electronics is now validated by research. Other factors often used, also supported by this research, in- clude ACT and SAT math scores and ACT composite scores, and the student's abstract learning preference.

2. Those having opportunity to counsel with potential electronics students can predict with confidence what the student's grade would be in any of the three electronics programs. Most of the information neces- sary to make this prediction can be ex- tracted from high school transcripts.

. Potential electronics students can be ad- vised that the average EE student has higher high school grades and ACT scores than the average EET or ET student, and that these variables (high school grades and ACT scores) do contribute significantly to the probability of their academic success in EE. Potential electronics students can also be advised that the same holds true for the average EET student as compared to the average ET student.

REFERENCES

Carlson, W. O. (1980). Do you want to be an engineering tech- nician or technologist? Engineering Education, 70: 322-325.

Carr, B. W. (1980). Engineering technology in America--the status in 1979 in comparison with the status in 1959, Doctoral dissertation, University of Kentucky. Dissertation Abstracts International, 44: 1768A.

Cheshier, S. R. (1985). A model proposal regarding the future of engineering technology in America. Engineering Educa- tion, 75: 707-712.

Kenyon, R. A. (1985). The future of engineering science and en- gineering technology: collision or convergence? Engineering Education, 75: 707-712.

Kolb, D. A. (1985). Learning Style Inventory 1985 Technical Speci- fication. McBer and Company, Boston.

Ritz, J. M., Cummings, P. L., Dead, W. F., III, Fay, M. M., Hadley, W. F., and Jacobs, J. A. (1985). Resources in tech- nology: Careers in technology. The Technology Teacher, 45" 15-22.