The Effects of Mind Mapping with Cooperative Learning on Programming Performance,Problem Solving Skill and Metacognitive Knowledge among Computer Science Students

Abstract

The purpose of the study is to investigate the effects of mind mapping with cooperative learning (MMCL) and cooperative learning (CL) on: (a) programming performance; (b) problem solving skill; and (c) metacognitive knowledge among computer science students in Malaysia. The moderating variable is the students' logical thinking level with two categories: high logical thinking (HLT) ability and low logical thinking (LLT) ability. Past research has shown problems with regard to computer programming. Many discussion and debates among computer science experts agree that there are problems due to the traditional method used to teach computer programming which did not contribute to the development of above dependent variables. There were gaps or deficiencies identified in students' knowledge in computer programming in each phase of the programming processes. The critical problem, which was due to the lacking of skills in analyzing programming problems, was identified at the first phase of the programming processes. This problem consequently will have an effect on the next phase of a programming sequence. Based on the literature, the above independent variables identified can be used to help lessen the burden at the first phase of the programming processes. The study further investigated the effects of MMCL and CL among the students with high logical thinking (HLT) and low logical thinking (LLT) on the three above mentioned dependent variables. A quasi-experimental study with posttest-only control group method that employed a 3 × 2 Factorial Design was applied in the study. The sample consisted of 127 students from 2 classes in three Universiti Teknologi MARA campuses which were all randomly selected and assigned to the treatment groups (MMCL and CL) and control (T) group. The equivalence of all groups was established using the homogeneity test between the three groups based on the students' Malaysian Education Certificate (SPM) grades in Mathematics, Additional Mathematics, Physic, and Chemistry subjects. The results showed that the students in MMCL and CL groups have significant positive overall effects in programming performance, problem solving skill, and metacognitive knowledge. Students taught via the MMCL method significantly outperformed the students taught via the T method in programming performance and metacognitive knowledge. Meanwhile, students taught via the CL method significantly outperformed the students taught via the T method in metacognitive knowledge. Even though there was no significant effect between MMCL and CL on any dependent variables, the descriptive statistics for programming performance and metacognitive knowledge between the three instructional method groups did show some positive effects. For the effects of instructional methods on programming performance mean scores, there were two major findings. First, the results showed that the overall mean scores for programming performance for the students taught via MMCL method were higher than the CL group whose scores were, in turn, higher than the scores of the students taught via the T method. Second, both HLT and LLT students' mean scores in programming performance for MMCL method were higher compared to the mean scores acquired by the CL and T methods. For the effects of instructional methods on metacognitive knowledge mean scores, there were two major findings. First, the results showed that the mean scores for metacognitive knowledge for the LLT students taught via MMCL method was higher than LLT students taught via CL method whose scores, in turn, were higher than the LLT students taught via the T method. Second, the results showed that the HLT students taught via the CL method outperformed the HLT students taught via the T method in metacognitive knowledge. The results of this study also showed that the difference between HLT and LLT students among the three instructional methods were significant, overall in both programming performance and problem solving skills. Further analysis revealed that the HLT students taught via both MMCL and CL methods significantly outperformed the LLT students taught via the same method in problem solving skills. Finally, the results showed that there were no significant interaction effects between the instructional methods and the students' logical thinking levels for programming performance, problem solving skills, and metacognitive knowledge scores. The findings of this study suggest that mind mapping with cooperative learning method (MMCL) is preferred compared to CL and T methods in programming performance, problem solving skills, and metacognitive knowledge for students of all logical thinking levels.

Get full access to this article

View all access options for this article.

References

All

A. C.

, & Havens

R. L.

(1997). Cognitive/concept mapping: A teaching strategy for nursing. Journal of Advanced Nursing, 25, 1210–1219.

All

A. C.

Huycke

L. I.

, & Fisher

M. J.

(2003). Instructional tools for nursing education: Concept maps. Nursing Education Perspectives, 24(6), 311–317.

Anderson

J. R.

(1987). Skill acquisition: Compilation of weak-method problem solutions. Psychological Review, 94, 192–210.

Anderson

L. W.

, & Krathwohl

D. R.

(Eds.). (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objectives ( Abridged ed.). New York: Longman.

Blackwell

A. F.

(2002). What is programming? Proceedings of the 14th Annual Workshop of the Psychology of Programming Interest Group (PPIG), Brunel University, 14, 204–218.

Bonar

, & Soloway

(1989). Pre-programming knowledge: A major source of misconceptions in novice programmers. In Soloway

& Spohrer

J. C.

(Eds.), Studying the novice programmer (pp. 325–354). Mahwah, NJ: Erlbaum.

Brinkmann

(2003). Graphical knowledge display: Mind mapping and concept mapping as efficient tools in mathematics education. Mathematics Education Review, 16, 35–48.

Brown

A. L.

, & Palincsar

A. S.

(1989). Guided cooperative learning and individual knowledge acquisition. In Resnick

L. B.

(Ed.), Knowing, learning and instruction: Essays in honor of Robert Closer (pp. 393–451). Hillsdale, NJ: Lawrence Erlbaum Associates.

Buzan

(2002). How to mind map: Make the most of your mind and learn how to create, organize, and plan. Great Britain: Martins the Printers Limited.

10.

Buzan

, & Buzan

(1993). The mind map book: How to use radiant thinking to maximize your brain's untapped potential. New York: Penguin Group.

11.

Cohen

, & Mannion

(1994). Research methods in education. London: Routledge.

12.

Dale

Weems

, & Headington

(1996). Programming and problem solving with C++. Boston, MA: Jones and Bartlett Publishers.

13.

Felder

R. M.

(1996). Active-inductive-cooperative learning: An instructional model for chemistry? Journal of Chemical Education, 73, 832–836.

14.

George

, & Mallery

(2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston, MA: Allyn & Bacon.

15.

Hemmerich

Lim

, & Neel

(1994). Prime time: Strategies for life-long learning in mathematics and science in the middle and high school grades. Portsmouth: Heinemann.

16.

Henderson

P. B.

(1986). Anatomy of an introductory computer science course. Proceedings of the 17th SIGCSE '86: Technical symposium on Computer Science Education (pp. 257–264). New York: ACM Press.

17.

Hill

L. H.

(2004). Concept mapping in a pharmacy communications course to encourage meaningful student learning. American Journal of Pharmaceutical Education, 68(5), Article 109.

18.

Ismail

M. N.

Ngah

N. A.

, & Umar

I. N.

(2006). Instructional strategy in the teaching of computer programming: What exactly is the problem? Proceedings of the Malaysian Educational Technology Association (META) 19th Convention, 1, 267–273.

19.

Jenkinson

Coulter

, & Wright

(1993). Short form 36(SF36) health survey questionnaire: Normative data for adults of working age. British Medical Journal, 306, 1437–1440.

20.

Johnson

D. W.

, & Johnson

R. T.

(1992). Encouraging thinking through constructive controversy. In Davidson

& Worsham

(Eds.), Enhancing thinking through cooperative learning (pp. 120–138). New York: College Teachers Press.

21.

Lawson

A. E.

(1995). Science teaching and the development of thinking. California: Wadsworth.

22.

Lawson

A. E.

(2000). Development and validation of the classroom test of formal reasoning (Rev. ed.). Journal of Research in Science Teaching, 15(1), 11–24.

23.

Linn

M. C.

(1985). The cognitive consequences of programming instruction in classrooms. Educational Researchers, 14(5), 14–16, 25–29.

24.

Linn

M. C.

, & Clancy

M. J.

(1992). The case for case studies of programming problems. Communications of the ACM, 35(3), 121–132.

25.

Maheshwari

(1997). Teaching programming paradigms and languages for qualitative learning. Proceedings of the second Australasian Conference on Computer Science Education (pp. 32–39). New York: ACM Press.

26.

Margulies

(1991). Mapping inner space: Learning and teaching mind mapping. Tucson, AZ: Zephyr Press.

27.

Mayer

R. E.

(1992). Thinking, problem solving, cognition (2nd ed.). New York: Freeman and Company.

28.

McCracken

Almstrum

Diaz

Guzdial

Hagen

Kolikant

(2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. SIGCSE Bulletin, 33(4), 125–180.

29.

McGill

T. J.

, & Volet

S. E.

(1997). A conceptual framework for analyzing students' knowledge of programming. Journal of Research on Computing in Education, 29(3), 276.

30.

Novak

J. D.

(1981). Applying learning psychology and philosophy to biology teaching. The American Biology Teacher, 43(1), 12–20.

31.

Novak

J. D.

(1998). Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations. Mahwah, NJ: Lawrence Erlbaum Associates.

32.

O'Donnell

A. M.

, & Dansereau

D. F.

(1992). Scripted cooperation in student dyads: A method for analysing and enhancing academic learning and performance. In Hertz-Lazarowitz

& Miller

(Eds.), Interaction in cooperative groups (pp. 120–141). Cambridge, MA: Cambridge University Press.

33.

O'Neil

Jr. , & Schacter

(1997). Test specifications for problem-solving assessment. Los Angeles, CA: National Center for Research on Evaluation, Standards, and Student Testing. Retrieved April 8, 2006, from http://www.cse.ucla.edu/CRESST/Reports/TECH463.PDF

34.

Oliver

(1993). The contextual model: An alternative model for teaching introductory computer programming. Journal of Computers in Mathematics and Science Education, 12(2), 147–167.

35.

Parham

J. R.

(2003). An assessment and evaluation of computer science education. Journal of Computing Sciences in Colleges, 19(12), 115–127.

36.

Piaget

(1972). Intellectual evolution from adolescence to adult. Human Development, 15, 1–12.

37.

Reitman

W. R.

(1964). Heuristic design procedures, open constraints and the structure of ill-defined problems. In Shelly

M. W.

& Bryan

G. L.

(Eds.), Human judgements and optimality (pp. 282–315). New York: Wiley and Sons.

38.

Riley

(1981). Teaching problem solving in an introductory computer science class. Proceedings of the Twelfth SIGCSE Technical Symposium on Computer Science Education (pp. 244–251). New York: ACM Press.

39.

Roadrangka

Yeany

R. H.

, & Padila

M. J.

(1983, April). The construction and validation of a group assessment of logical thinking (GALT). Paper presented at the meeting of the National Association for Research in Science Teaching, Dallas, Texas.

40.

Schraw

, & Dennison

R. S.

(1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460–475.

41.

Swing

, & Peterson

(1982). The relationship of student ability and small group interaction to student achievement. American Educational Research Journal, 19, 259–274.

42.

Volet

S. E.

(1991). Modeling and coaching of relevant metacognitive strategies for enhancing university students' learning. Learning and Instruction, 1, 319–336.

43.

Vygotsky

L. S.

(1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.

44.

Webb

N. M.

(1983). Predicting learning from student interaction: Defining the interaction variable. Educational Psychologist, 18, 33–41.

45.

Webb

N. M.

(1991). Task-related verbal interaction and mathematics learning in small groups. Journal of Research in Mathematics Education, 22, 366–389.