Investigating the Influence of Affective State on Help Seeking: A Study with Novice Programmers
Abstract
The research investigates how specific affective states, such as frustration, boredom, and anxiety, influence help-seeking behaviors in programming problem-solving activities. Carried out with 73 beginner programming students divided into two CS1 classes, the study uses an interactive learning environment to collect and analyze data from student interactions. The results reveal that negative affective states are significantly associated with the search for hints that offer ready-made answers to problems. One of the conclusions from these results is that negative affective states can motivate students to prefer quick and less challenging solutions, emphasizing the importance of considering the affective dimension in the design of interactive learning environments.
References
Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the Lisp tutor. Cognitive Science, 13(4), 467–505.
Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-task behavior in the cognitive tutor classroom: When students "game the system". In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 383–390).
Barrett, L. F. (2009). Variety is the spice of life: A psychological construction approach to understanding variability in emotion. Cognition and Emotion, 23(7), 1284–1306.
Bennedsen, J., & Caspersen, M. E. (2019). The ultimate barrier to teaching programming: Teachers who do not know how to program. ACM Transactions on Computing Education (TOCE), 19(1), 1–28.
Bosch, N., & D’Mello, S. K. (2013). Detecting learning-centered affective states in the wild. IEEE Transactions on Affective Computing, 4(3), 298–310.
Burton, R. R., & Brown, J. S. (1979). An investigation of computer coaching for informal learning activities. International Journal of Man-Machine Studies, 11(1), 5–24.
Chetty, Y., & Van Der Westhuizen, D. (2013). Hate, frustration, and learning programming. South African Journal of Higher Education, 27(3), 740–758.
Craig, S. D., Graesser, A. C., Sullins, J., & Gholson, B. (2004). Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29(3), 241–250.
dos Santos, N. R., Oliveira, E. H., de Oliveira, D. B., Carvalho, L. S., Lauschner, T., de Lima, M. A., Kautzmann, T. R., & Jaques, P. A. (2022). Análise do nível de confusão de estudantes com base no grau de dificuldade de questões de programação. In Anais do XXXIII Simpósio Brasileiro de Informática na Educação (pp. 1016–1027). SBC
Grover, S., & Basu, S. (2017). Measuring problem-solving skills in introductory programming. ACM Transactions on Computing Education (TOCE), 17(2), 1–26.
Harley, J. M., Bouchet, F., Hussain, M. S., Azevedo, R., & Calvo, R. A. (2013). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 29(1), 293–302.
Joni, S.-N., Soloway, E., Goldman, R., & Ehrlich, K. (1983). Just so stories: How the program got that bug. ACM SIGCUE Outlook, 17(4), 13–26.
