Abstract
Modeling a learner’s frustration in adaptive environments can inform scaffolding. While much work has explored momentary frustration, there is limited research investigating the dynamics of frustration over time and its relationship with problem-solving behaviors. In this paper, we clustered 86 undergraduate students into four frustration trajectories as they worked with an adaptive learning environment for introductory computer science. The results indicate that students who initially report high levels of frustration but then reported lower levels later in their problem solving were more likely to have sought help. These findings provide insight into how frustration trajectory models can guide adaptivity during extended problem-solving episodes.
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References
Andres, J.M.A.L., et al.: Affect sequences and learning in Betty’s brain. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pp. 383–390 (2019)
Baker, R., D’Mello, S., Rodrigo, M., Graesser, A.: Better to be frustrated than bored: the incidence and persistence of affect during interactions with three different computer-based learning environments. Int. J. Hum.-Comput. Stud. 68(4), 223–241 (2010)
Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., Koedinger, K.: Why students engage in “gaming the system” behavior in interactive learning environments. J. Interact. Learn. Res. 19(2), 185–224 (2008)
Berry, D.S., Hansen, J.S.: Positive affect, negative affect, and social interaction. J. Pers. Soc. Psychol. 71(4), 796 (1996)
Bosch, N., D’Mello, S.: The affective experience of novice computer programmers. Int. J. Artif. Intell. Educ. 27(1), 181–206 (2017). https://doi.org/10.1007/s40593-015-0069-5
Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)
Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. J. Educ. Media 29(3), 241–250 (2004)
Dillon, J., et al.: Student emotion, co-occurrence, and dropout in a MOOC context. In: Proceedings of the International Conference on Educational Data Mining, pp. 353–357 (2016)
D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instr. 22(2), 145–157 (2012)
Emerson, A., et al.: Predictive student modeling in block-based programming environments with Bayesian hierarchical models. In: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 62–70 (2020)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)
Kodinariya, T.M., Makwana, P.R.: Review on determining number of cluster in k-means clustering. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1(6), 90–95 (2013)
Liu, Z., Pataranutaporn, V., Ocumpaugh, J., Baker, R.: Sequences of frustration and confusion, and learning. In: Proceedings of the International Conference on Educational Data Mining, pp. 114–119 (2013)
Pardos, Z.A., Baker, R.S., San Pedro, M.O., Gowda, S.M., Gowda, S.M.: Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 117–124 (2013)
Rachmatullah, A., Wiebe, E., Boulden, D., Mott, B., Boyer, K., Lester, J.: Development and validation of the computer science attitudes scale for middle school students (MG-CS attitudes). Comput. Hum. Behav. Rep. 2, 100018 (2020)
Richey, J.E., et al.: More confusion and frustration, better learning: the impact of erroneous examples. Comput. Educ. 139, 173–190 (2019)
Shute, V.J., et al.: Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Comput. Educ. 86, 224–235 (2015)
Stachel, J., Marghitu, D., Brahim, T.B., Sims, R., Reynolds, L., Czelusniak, V.: Managing cognitive load in introductory programming courses: a cognitive aware scaffolding tool. J. Integr. Des. Process Sci. 17(1), 37–54 (2013)
Wiebe, E.N., Lamb, A., Hardy, M., Sharek, D.: Measuring engagement in video game-based environments: investigation of the user engagement scale. Comput. Hum. Behav. 32, 123–132 (2014)
Wiggins, J.B., et al.: Exploring novice programmers’ hint requests in an intelligent block-based coding environment. In: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, pp. 52–58 (2021)
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This work is supported by the National Science Foundation through the grants DUE-1626235 and DUE-1625908. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Tian, X. et al. (2021). Modeling Frustration Trajectories and Problem-Solving Behaviors in Adaptive Learning Environments for Introductory Computer Science. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_63
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