Using Animation to Help Students Learn Computer Algorithms

Abstract

This paper compares the effects of graphical study aids and animation on the problem-solving performance of students learning computer algorithms. Prior research has found inconsistent effects of animation on learning, and we believe this is partly attributable to animations not being designed to convey key information to learners. We performed an instructional analysis of the to-be-learned algorithms and designed the teaching materials based on that analysis. Participants studied stronger or weaker text-based information about the algorithm, and then some participants additionally studied still frames or an animation. Across 2 studies, learners who studied materials based on the instructional analysis tended to outperform other participants on both near and far transfer tasks. Animation also aided performance, particularly for participants who initially read the weaker text. These results suggest that animation might be added to curricula as a way of improving learning without needing revisions of existing texts and materials. Actual or potential applications of this research include the development of animations for learning complex systems as well as guidelines for determining when animations can aid learning.

Get full access to this article

View all access options for this article.

References

Baecker, R. M. , & Sherman, D. (1981). Sorting out sorting [30-min. color sound film, presented at ACM SIGGRAPH, 1981, excerpted and reprinted in SIGGRAPH video review 7, 1983]. San Francisco: Morgan Kaufmann.

Baecker, R. (1998). Sorting out sorting: A case study of software visualization for teaching computer science. In J. Stasko , J. Domingue , M. H. Brown , & B. A. Price (Eds.), Software visualization: Programming as a multimedia experience (pp. 369–381). Cambridge, MA: MIT Press.

Brown, M. H. (1988). Perspectives on algorithm animation. In Proceedings of the Association for Computing Machinery Special Interest Group on Computer Human Interaction Conference on Human Factors (pp. 33–38). New York: ACM.

Byrne, M. D. , Catrambone, R. , & Stasko, J. T. (1999). Examining the effects of animation and prediction in student learning of computer algorithms. Computers and Education, 33, 253–278.

Cormen, T. H. , Leiserson, C. E. , & Rivest, R. L. (1998). Introduction to algorithms. New York: McGraw-Hill.

Hansen S. , Schrimpsher, D. , & Narayanan, N. H. (1999). From algorithm animations to animation-embedded hypermedia visualizations. In Proceedings ED-MEDIA 1999: World Conference on Educational Multimedia, Hypermedia & Telecommunications [CD-ROM]. Norfolk, VA: Association for the Advancement of Computing in Education.

Hays, T. (1996). Spatial abilities and the effects of computer animation on short term and long term comprehension. Journal of Educational Computing Research, 14, 139–155.

Hegarty, M. (1992). Mental animation: Inferring motion from static displays of mechanical systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 1084–1102.

Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32, 1–19.

10.

Mayer, R. E. (2001). Multimedia learning. Cambridge, UK: Cambridge University Press.

11.

McNamara, D. S. , Kintsch, E. , Songer, N. B. , & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14, 1–43.

12.

Mousavi, S. Y. , Low, R. , & Sweller, J. (1995). Reducing cognitive load by mixing auditory and visual presentation modes. Journal of Educational Psychology, 87, 319–334.

13.

Price, B. A. , Baecker, R. M. , & Small, I. S. (1993). A principled taxonomy of software visualization. Journal of Visual Languages and Computing, 4, 211–266.

14.

Reiber, L. P. (1991). Animation, incidental learning, and continuing motivation. Journal of Educational Psychology, 83, 318–328.

15.

Reiber, L. P. , & Kini, A. S. (1991). Theoretical foundations of instructional applications of computer-generated animated visuals. Journal of Computer-Based Instruction, 18(3), 83–88.

16.

Rigney, J. , & Lutz, K. A. (1976). Effect of graphic analogies of concepts in chemistry on learning and attitude. Journal of Educational Psychology, 68, 305–311.

17.

Scaife, M. , & Rogers, Y. (1996). External cognition: How do graphical representations work? International Journal of Human-Computer Studies, 45, 185–213.

18.

Seaman, M. A. , Levin, J. R. , & Serlin, R. C. (1991). New developments in pairwise multiple comparisons: Some powerful and practical procedures. Psychological Bulletin, 110, 577–586.

19.

Shaffer, J. P. (1986). Modified sequentially rejective multiple test procedures. Journal of the American Statistical Association, 81, 826–831.

20.

Stasko, J. T. (1990). TANGO: A framework and system for algorithm animation. Computer, 23(9), 27–39.

21.

Stasko, J. T. (1992). Animating algorithms with XTango. SIGACT News, 23(2), 67–71.

22.

Stephenson, S. D. (1994). The use of small groups in computer-based training: A review of recent literature. Computers in Human Behavior, 10, 243–259.

23.

Tindall-Ford, S. , Chandler, P. , & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 3, 257–287.