The Impact of Cognitive Bias on Students’ Programming Performance in an Introduction to Programming Course

doi:10.23940/ijpe.22.08.p7.589597

Abstract

Abstract: Cognitive biases are the primary source of errors in decision-making. These biases enable learners to make snap judgments, leading to errors. These biases usually result in incorrect decisions when students solve Multiple-Choice Questions (MCQ) and Short-Answer Questions (SAQ) types of problems. Furthermore, each person's brain is unique with respect to cognitive processing; their perception capability and biases may vary when confronted with the same situation. Hence, it is critical to investigate learners' numerous biases. This article analyzes various types of biases, such as Anchor effect bias, Availability bias, Attention bias, Confirmation bias, and Framing effect bias when students are asked to solve MCQ and SAQ types of problems. Students' performance in an introductory course on programming is used to analyze the effect of cognitive biases on their performance. We recorded audio interviews with respondents to ascertain why they selected a particular option for a given question. Three experts manually classify the respondent's biases in response to a specific question using this audio clip.

Key words: Kahneman's two-systems model, cognitive bias, introduction to programming course

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.

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

References

1. Bennedsen, J. and Caspersen, M.E.Failure Rates in Introductory Programming. AcM SIGcSE Bulletin, vol. 39, no. 2, pp. 32-36, 2007.
2. Watson, C. and Li, F.W. Failure Rates in Introductory Programming Revisited. In Proceedings of the2014 conference on Innovation & technology in computer science education, pp. 39-44, 2014.
3. McCracken M., Almstrum V., Diaz D., Guzdial M., Hagan D., Kolikant Y.B.D., Laxer C., Thomas L., Utting I., andWilusz T. Report by the ITiCSE2001 Working Group on Assessment of Programming Skills of First-year CS Students. Distribution, vol. 33, no. 4, pp. 125-180, 2001.
4. Hamouda S., Edwards S.H., Elmongui H.G., Ernst J.V., andShaffer C.A.RecurTutor: An Interactive Tutorial for Learning Recursion. ACM Transactions on Computing Education (TOCE), vol. 19, no, 1, pp. 1-25, 2018.
5. Yang J.S.JavelinaCode: A Web-based Object-oriented Programming Environment with Static and Dynamic Visualization, 2016.
6. Heinonen K., Hirvikoski K., Luukkainen M., andVihavainen A.Using Codebrowser to Seek Differences between Novice Programmers. In Proceedings of the 45th ACM technical symposium on Computer science education, pp. 229-234, 2014.
7. Bosse, Y. and Gerosa, M.A.Why Is Programming So Difficult to Learn? Patterns of Difficulties Related to Programming Learning Mid-stage. ACM SIGSOFT Software Engineering Notes, vol. 41, no. 6, pp. 1-6, 2017.
8. Kim, J. and Lerch, F.J.Why Is Programming (Sometimes) So Difficult? Programming as Scientific Discovery in Multiple Problem Spaces. Information Systems Research, vol. 8, no. 1, pp. 25-50, 1997.
9. Bada, S.O. and Olusegun, S.Constructivism Learning Theory: A Paradigm for Teaching and Learning. Journal of Research & Method in Education, vol. 5, no. 6, pp. 66-70, 2015.
10. Yahong H., Weihong W., andLi J. Teaching Discrete Mathematics with the Constructivism Learning Theory. In2011 6th International Conference on Computer Science & Education (ICCSE), IEEE, pp. 815-816, 2011.
11. Daniel, K. Thinking, Fast and Slow, 2017.
12. 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.
13. Chen D., Bai Y., Zhao W., Ament S., Gregoire J.M., andGomes C.P.Deep Reasoning Networks: Thinking Fast and Slow. arXiv preprint arXiv:1906.00855, 2019.
14. 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.
15. Goel G., Chen N., andWierman A. Thinking Fast and Slow: Optimization Decomposition across Timescales. In2017 IEEE 56th Annual Conference on Decision and Control (CDC), IEEE, pp. 1291-1298, 2017.
16. 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.
17. Mittal S., Joshi A., andFinin, T. Thinking Fast and Slow: Combining Vector Spaces and Knowledge Graphs. arXiv preprint arXiv:1708.03310, 2017.
18. 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.
19. Murdock, C.W. and Sullivan, B.What Kahneman Means for Lawyers: Some Reflections on Thinking, Fast and Slow. Loy. U. Chi. LJ, vol. 44, pp. 1377, 2012.
20. Rossi, F. and Loreggia, A.Preferences and Ethical Priorities: Thinking Fast and Slow in AI. In AAMAS, pp. 3-4, 2019.
21. Aujayeb A.Cognitive Bias in Diagnosis in the COVID-19 Era. Int J Case Rep Images, vol. 11, pp. 101170Z01AA2020, 2020.
22. P. Croskerry, Cognitive and Affective Biases in Medicine, https://sjrhem.ca/wp-content/uploads/2015/11/CriticaThinking-Listof50-biases.pdf, accessed in July 2021
23. Khadilkar, S.V. and Khadilkar, S.S.Bias in Clinical Practice. The Journal of Obstetrics and Gynecology of India, vol. 70, no. 1, pp. 1-5, 2020.
24. O'Sullivan, E.D. and Schofield, S.J. Cognitive Bias in Clinical Medicine. Journal of the Royal College of Physicians of Edinburgh, vol. 48, no. 3, pp. 225-232, 2018.
25. Behimehr, S. and Jamali, H.R. Cognitive Biases and Their Effects on Information Behaviour of Graduate Students in Their Research Projects, 2020.
26. Rüping A.Taming the Biases: A Few Patterns on Successful Decision-making. In Proceedings of the 19th European Conference on Pattern Languages of Programs, pp. 1-5, 2014.