Analyzing Performance in a Computer Programming Course Through a Two-System Model

doi:10.23940/ijpe.22.02.p1.7178

Abstract

Abstract: Kahneman's two-systems model helps understand a student's cognitive processes while solving C programming concepts. System 1 is fast and unconscious, which may cause cognitive bias, leading to a wrong answer. System 2 is slow and conscious, which can help overcome the bias situation, but is lazy. Understanding any concepts or program must start with attempts to believe it, which is the nature of System 1, but System 2 checks those concepts are true first, then believing it or not believing it, which helps in finding errors, the correct output of a program, etc. The activation of System 2 guides the efforts of students for complex problem-solving. This paper shows the various natures of System 1 and System 2, which guide a student's cognitive processes while solving C-programming concepts-based quizzes. The Two-System model can help find a student's performance using System 1 and System 2 in a Computer Programming Course.

Key words: Kahneman's two-systems model, cognitive bias, cognitive process

Swanand K. Navandar, Manjushree D. Laddha, and Arvind W. Kiwelekar. Analyzing Performance in a Computer Programming Course Through a Two-System Model [J]. Int J Performability Eng, 2022, 18(2): 71-78.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Kiwelekar A.W., Navandar S., andYadav D.K.A Two-Systems Perspective for Computational Thinking. In International Conference on Intelligent Human Computer Interaction, Springer, Cham, pp. 3-11, 2020.
2. Kahneman D.Thinking, fast and slow. Macmillan, 2011.
3. Kannengiesser, U. and Gero, J.S.Design Thinking, Fast and Slow: a Framework for Kahneman's Dual-system Theory in Design.Design Science, vol. 5, 2019.
4. Gero, J.S. and Kannengiesser, U.The Function-behaviour-structure Ontology of Design. In An anthology of theories and models of design, Springer, London, pp. 263-283, 2014.
5. Evans J.S.B. and Stanovich, K.E. Dual-process Theories of Higher Cognition: Advancing the Debate. Perspectives on psychological science, vol. 8, no. 3, pp. 223-241. 2013.
6. Mittal S., Joshi A., andFinin T. Thinking,Fast and Slow: Combining Vector Spaces and Knowledge Graphs.arXiv preprint arXiv:1708.03310, 2017.
7. Rossi, F. and Loreggia, A.Preferences and ethical priorities: thinking fast and slow in AI. InProceedings of the 18th international conference on autonomous agents and multiagent systems, pp. 3-4, 2019.
8. Preisz A.Fast and Slow Thinking; and the Problem of Conflating Clinical Reasoning and Ethical Deliberation in Acute Decision‐making. Journal of paediatrics and child health, vol. 55, no. 6, pp. 621-624, 2019.
9. Croskerry P.Cognitive and Affective Biases, and Logical Failures. In Diagnosis, CRC Press, pp. 97-111, 2017.
10. Skellern C.Thinking Fast and Slow in the Evaluation of Injury Plausibility in Child Protection. Journal of Paediatrics and Child Health, vol. 56, no. 9, pp. 1330-1334, 2020.
11. Ganapini M.B., Campbell M., Fabiano F., Horesh L., Lenchner J., Loreggia A., Mattei N., Rossi F., Srivastava B., andVenable K.B.Thinking Fast and Slow in AI: the Role of Metacognition.arXiv preprint arXiv:2110.01834, 2021.
12. Booch G., Fabiano F., Horesh L., Kate K., Lenchner J., Linck N., Loreggia A., Murugesan K., Mattei N., Rossi F. and Srivastava B.Thinking Fast and Slow in AI.arXiv preprint arXiv:2010.06002, 2020
13. Kaurav S., Ganesan D., Deepak P., andChakraborti S.Thinking Fast and Slow: a CBR Perspective. In The International FLAIRS Conference Proceedings, vol. 34, 2021.
14. Ward, T. and Garety, P.A.Fast and Slow Thinking in Distressing Delusions: a Review of the Literature and Implications for Targeted Therapy. Schizophrenia Research, vol. 203, pp. 80-87, 2019.
15. Csernoch M.Thinking Fast and Slow in Computer Problem Solving. Journal of Software Engineering and Applications, vol. 10, no. 01, pp. 11, 2017.
16. Lawson M.A., Larrick R.P., andSoll J.B.Comparing Fast Thinking and Slow Thinking: the Relative Benefits of Interventions, Individual Differences, and Inferential Rules. Judgment & Decision Making, vol. 15, no. 5, 2020.
17. Heller S.B., Shah A.K., Guryan J., Ludwig J., Mullainathan S., andPollack H.A.Thinking, Fast and Slow? Some Field Experiments to Reduce Crime and Dropout in Chicago. The Quarterly Journal of Economics, vol. 132, no. 1, pp. 1-54, 2017.
18. Solaki A., Berto F., andSmets S.The Logic of Fast and Slow Thinking. Erkenntnis, vol. 86, no. 3, pp. 733-762, 2021.
19. Kahneman, D, Thinking, fast and slow. Macmillan, 2011.
20. Schwab K.,Meerts-Brandsma, L., Qwynne Lackey, N., Povilaitis, V., Zajchowski, C., Moseley, B., and Dustin, D. Learning, Fast and Slow.SCHOLE: A Journal of Leisure Studies and Recreation Education, pp. 1-8, 2020.
21. Richmond G., Cho C., Gallagher H.A., He Y., andBartell T.Fast and Slow Thinking to Address Persistent and Complex Problems in Teaching and Learning. Journal of Teacher Education, vol. 72, no. 4, pp. 401-404, 2021.
22. Zalewski A., Borowa K., andKowalski D.On Cognitive Biases in Requirements Elicitation. In Integrating Research and Practice in Software Engineering, Springer, Cham, pp. 111-123, 2020.
23. Mohanani R., Salman I., Turhan B., Rodríguez P., andRalph P.Cognitive Biases in Software Engineering: a Systematic Mapping Study. IEEE Transactions on Software Engineering, vol. 46, no. 12, pp. 1318-1339, 2018.
24. Saposnik G., Redelmeier D., Ruff C.C., andTobler P.N.Cognitive Biases Associated with Medical Decisions: a Systematic

[1]	Swanand K. Navandar, Arvind W. Kiwelekar, and Manjushree D. Laddha. The Impact of Cognitive Bias on Students’ Programming Performance in an Introduction to Programming Course [J]. Int J Performability Eng, 2022, 18(8): 589-597.
[2]	Varsha T. Lokare, Arvind W. Kiwelekar, and Laxman D. Netak. Analysis of the Influence of Physiological and Cognition Measures in Higher Education using Machine Learning [J]. Int J Performability Eng, 2022, 18(2): 117-127.