3. Relationship Between Cognitive Learning Style

3. Relationship Between Cognitive Learning Style and Achievement in an Introductory Computer Programming Course1 Jeroen van Merriënboer

Abstract

In an exploratory study, the relationship between the cognitive style reflection-impulsivity and achievement in an introductory programming course in high school was studied. Reflectives were found to be superior to impulsives in their scores on a program comprehension test. No significant differences were found in scores on a test measuring factual knowledge of programming language features and syntax. These results suggest that a reflective strategy facilitates the development of templates:

Schema's

representing patterns of code associated with specific

programming problems. It is hypothesized that instructional materials not concentrating on the writing of programs but on the reading, modification, and amplification of programs may force impulsives into a more reflective strategy, resulting in better achievement.

3.1. Introduction

The teaching of elementary computer programming in a high level computer language is generally considered an important part of computer education in high schools. It is commonly expected that computer programming fosters the development of specific problem solving skills, which are required to cope with a society that is characterized by fast technological changes. In spite of this belief, much programming instruction is not necessarily geared to fostering such skills (Woodhouse, 1983; Linn, 1985). Attempts to stress systematic problem solving and program design in an introductory programming course encounter various difficulties. A striking observation is that novice programmers are characterized by a rush to the computer. They frequently attempt to go from a detail of the problem specification to the programming code, without any consideration of how to solve the problem as a whole, how to plan the solution, or how to design the code.

1Van

Merriënboer, J. J. G. (1988). Journal of Research on Computing in Education, 21, 181-186.

Chapter 3 /

As Prichard (1982) pointed out, studies on students' cognitive processes can be helpful in designing instructional materials to teach students about computers. The above mentioned observation of programming behavior suggests a relationship with the cognitive style reflection-impulsivity, a construct originally introduced by Kagan (Kagan, Rosman, Day, Albert & Phillips, 1964). Reflection-impulsivity is related to the quality of problem solving and describes the tendency to reflect on the validity of problem solving, when several alternatives are available and the correct solution is not immediately obvious. Impulsives gather their information less systematically and carefully than reflectives, spending less time considering possible solutions or planning in advance, and are prone to make more errors. Generally, impulsives' achievement in school is low compared to reflectives. In a review on reflection-impulsivity, Messer (1976) reported for school age children a small to moderate correlation with, in particular, nonverbal intelligence. Impulsivity is modifiable, at least to a degree. Clements & Gullo (1984) found a positive effect of computer programming on reflection-impulsivity. However, Clements (1986) did not replicate this result in a second study. He suggested that in this second study students had more freedom in applying their preferred cognitive style, which often may have involved impulsive behavior. Obviously, the use of appropriate instructional materials is necessary to force impulsives into a more reflective strategy. In common introductory computing courses, students are mainly engaged in writing programs after learning a small amount of new programming language features along with syntactic details and studying one or more worked-out examples. Thus, students are free to choose their own problem solving strategy to a large extent. Given the characterization of impulsive behavior, impulsives are expected to code a program line as soon as they associate a certain detail in the problem description with a newly learned language feature. After coding a complete program, their debugging will essentially be a process of piecing the language features together in different ways. As a result of this, their understanding of programs remains basically at the level of a single line. On the contrary, reflectives are expected to consider different possible solutions to the problem, or one of its subproblems, before they code a number of functionally related program lines. Their advanced planning may be thought to be helpful in developing schema's or templates that combine language features in larger patterns of code associated with specific programming problems. It is known that experts do structure their knowledge of programming in templates which are used in the comprehension and writing of computer programs (e.g., Atwood, Turner, Ramsey, Hooper & Sidorsky, 1979; Adelson, 1981).

/ Part A

According to this line of reasoning, after an introductory programming course, no difference in achievement between impulsives and reflectives is predicted as far as knowledge of language features and syntax is concerned, that is knowledge restricted to a single-line level. However, reflectives are expected to be superior to impulsives in program comprehension as this supposes the use of templates; that is, the understanding of actions in blocks of functionally related program lines and structures in complete computer programs. This hypothesis was investigated in a small exploratory study.

3.2. Method

3.2.1. Subjects and Setting

High school students 14-16 years of age (N = 21), volunteered for an introductory 10-lesson programming course using a subset of the computer language Comal-80 (Christensen, 1982). Each weekly one-hour lesson consisted of the presentation of a small number of new language features along with some syntactic details and worked-out examples, after which students were engaged in independently writing programs. All lessons took place under the supervision of the same instructor in a computer class provided with NewBrain microcomputers.

3.2.2. Measures

Students' reflection-impulsivity styles were determined from their scores achieved on a computer controlled version of the Matching Familiar Figures Test (MFFT, Kagan et al., 1964; for a description of the computerized version, see Van Merriënboer & Jelsma, 1988 [Appendix A]). The test format involves simultaneous presentation of a figure (e.g., a tree, a cowboy, a cat) with eight facsimiles differing in one or more details. On each of the test's 12 items, the subject is asked to select from the alternatives the one that exactly matches the standard. Average time to first responses and overall number of errors are computed. Subjects above the median on MFFT response time and below the median on errors are called reflective; subjects below the median on response time but above the median on errors are called impulsive. In addition, the reflectivity index provides a continuous variable on the dimension reflection-impulsivity for each subject. The index unites time score and error score in the standardized time score minus the standardized error score (Kagan, Lapidus & Moore, 1978).

Chapter 3 /

Two achievement scores for each student were determined after the programming course: (a) Factual Knowledge Test-score (FKT-score) measuring knowledge of language features

and

syntax

of

Comal-80,

and

(b)

Program

Comprehension

Test-score

(PCT-score) measuring the understanding of actions and structures in Comal-80 programs. The FKT consisted of 24 multiple-choice items concerning isolated program lines; the PCT consisted of 18 multiple-choice items concerning functionally related program lines in complete programs.

3.2.3. Procedures

All students received the same instructions in the same setting and worked with the same instructor. The instructor administered the computerized version of the MFFT in the computer class; the test followed the instructions and items originally developed by Kagan et al. (1964). Following the course the FKT and PCT were administered as evaluation measures in a regular classroom, after which all students received a certificate of participation.

3.3. Results

The internal consistency alpha-coefficients for FKT and PCT were .63 and .73, respectively. Correlations between reflectivity indices and test scores are presented in Table 3.1. Whereas no correlation between the cognitive style and FKT-scores

could

be

observed,

there

was

a

significant

correlation

reflection-impulsivity and PCT-scores (p < .05).

Table 3.1. Correlation Matrix for Reflectivity Indices and Achievement Scores.

Test

1. Reflectivity Index 2. Factual Knowledge Test 3. Program Comprehension Test

Note. N = 21. *p < .05. **p < .01.

/ Part A

2

3

-.05

.39*

--

.55** --

between

Seven students were classified as reflective and 7 students were classified as impulsive. The means and standard deviations of FKT- and PCT-scores for reflectives and impulsives are presented in Table 3.2. A Mann-Whitney U-test was performed on the PCT-scores. The effect due to reflection-impulsivity is shown to be significant (U = 10, p < .05). The means shown in Table 3.2 indicate that reflective subjects were significantly more successful in the comprehension of programs than were impulsive subjects: The mean score for reflective subjects (M = 14.6) was higher than the mean score for impulsive subjects (M = 11.1). Table 3.2. Means and Standard Deviations of Achievement Scores of Reflective and Impulsive Subjects.

Reflectives (n = 7) Test

Maximum Score M

Impulsives (n = 7)

SD

M

SD

FKTa

24

18.6

3.8

17.3

1.6

PCTb

18

14.6

3.2

11.1

1.8

aFactual

Knowledge Test. bProgram Comprehension Test.

3.4. Discussion The prediction, stating that reflectives are superior to impulsives regarding achievement on a program comprehension test after an introductory programming course, is supported by this study. Besides, no significant differences in test scores are found between reflectives and impulsives concerning factual knowledge of language features and syntax. These results support the view that a reflective strategy positively affects the development of templates, which play a prominent part in the comprehension and writing of computer programs. Whereas in the present study the behavior involved in writing programs was not observed directly, a detailed comparison of planning, coding, and debugging strategies in reflectives and impulsives may yield additional support to this assumption. In addition, such an analysis of programming behavior in reflectives and impulsives may contribute to identifying the kind of templates they do or do not use. One may argue that the difference in PCT-scores could be accounted for by factors that are correlated with reflection-impulsivity, such as general school achievement or nonverbal intelligence. However, Webb (1984) reported that measures like mathematics ability, nonverbal reasoning, and spatial ability were equally good predictors of knowledge of language features, knowledge of syntax, and program comprehension. Moreover, in this study no differences in Chapter 3 /

general school achievement could be observed between reflective and impulsive subjects. It is nevertheless apparent that further research with larger sample size and extensive control for possible confoundings is needed to reassure the validity of the documented relation. As Clements (1986) pointed out, it is possible to force impulsives in a reflective strategy through exposure to particular instructional materials. Templates can either be learned from direct instruction or by reading programs written by others. Several authors (e.g., Deimel & Moffat, 1982; Pea, 1986) proposed that an introductory programming course should not concentrate on the writing of programs but on the reading, modification, and amplification of non-trivial, well-designed and working programs. First, such programs demonstrate the application of templates directly. Secondly, modification and amplification of such programs requires a thorough understanding of structures combining several language features, that is, it facilitates the learning of templates that are used in the program. As a consequence, instructional strategies that are based on the comprehension, modification, and amplification of written programs may force impulsives into a more reflective strategy, resulting in better achievement after an introductory programming course. In the near future, research on cognitive styles and achievement in programming should provide a model for explaining and predicting the relations between these two variables. Such a model allows educators to design instructional strategies and materials that ameliorate the negative effects of certain cognitive styles while capitalizing on the strength of others. In my opinion, such improved instructional strategies and materials are needed for fostering the development of useful problem solving skills in computer education.

References

Adelson, B. (1981). Problem solving and the development of abstract categories in programming languages. Memory

& Cognition, 9, 422-433. Atwood, M. E., Turner, A. A., Ramsey, H. R., Hooper, J. N., & Sidorsky, R. C. (1979). An exploratory study of the

cognitive structures underlying the comprehension of software design problems. (Tech. Rep. No. 392). Alexandria, VA: US Army Research Institute for the Behavioral and Social Sciences. Christensen, B. R. (1982). Beginning Comal. Chichester: Ellis Horwood. Clements, D. H., & Gullo, D. F. (1984). Effects of computer programming on young children's cognition. Journal of

Educational Psychology, 76, 1051-1058. Clements, D. H. (1986). Effects of Logo and CAI environments on cognition and creativity. Journal of Educational

Psychology, 78, 309-318. Deimel, L. E., & Moffat, D. V. (1982). A more analytical approach to teaching the introductory programming course. In J. Smith, & M. Schuster (Eds.), Proceedings of the NECC (pp. 114-118). Columbia: The University of Missouri.

/ Part A

Kagan, J., Rosman, B. L., Day, D., Albert, J., & Phillips, W. (1964). Information processing in the child: Significance of analytic and reflective attitudes. Psychological Monographs, 78(1, Whole No. 578). Kagan, J., Lapidus, D. R., & Moore, M. (1978). Infant antecedents of cognitive functioning: A longitudinal study. Child

Development, 49, 1005-1023. Linn, M. C. (1985). The cognitive consequences of programming instruction in classrooms. Educational Researcher,

14(5), 14-29. Messer, S. B. (1976). Reflection-Impulsivity: A review. Psychological Bulletin, 83, 1026-1052. Pea, R. D. (1986). Language-independent conceptual "bugs" in novice programming. Journal of Educational

Computing Research, 2, 25-36. Prichard, Jr. W. H. (1982, January). Instructional computing in 2001: A scenario. Phi Delta Kappan, pp. 322-325. Van Merriënboer, J. J. G., & Jelsma, O. (1988). The Matching Familiar Figures Test: Computer or experimenter controlled administration? Educational and Psychological Measurement, 48, 161-164. Webb, N. M. (1984). Microcomputer learning in small groups: Cognitive requirements and group processes. Journal

of Educational Psychology, 76, 1076-1088. Woodhouse, D. (1983). Introductory courses in computing: Aims and languages. Computers & Education, 7, 79-89.

Chapter 3 /