Abstract
This paper investigates how to provide novice programmers with feedback about their learning process including hints and explanations to improve their learning. The aim is to improve the feedback effectiveness and perceived utility by making it more meaningful through the use of explanations. Our proposals were implemented in the context of computer science education and an experiment was conducted to evaluate the effect of explainable feedback on changes in learners’ strategies, performance and perceptions. The first results of this experiment show no significant effect of process and self-regulation feedback (explained or not) on students’ strategies or learning outcomes. Also, we conducted a qualitative analysis that allowed us to propose a series of recommendations for stakeholders exploring feedback explainability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Afzaal, M., et al.: Generation of automatic data-driven feedback to students using explainable machine learning. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds.) AIED 2021. LNCS (LNAI), vol. 12749, pp. 37–42. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78270-2_6
Arguel, A., Lockyer, L., Kennedy, G., Lodge, J.M., Pachman, M.: Seeking optimal confusion: a review on epistemic emotion management in interactive digital learning environments. Interact. Learn. Environ. (2019)
Bey, A., Pérez-Sanagustín, M., Broisin, J.: Unsupervised automatic detection of learners’ programming behavior. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds.) EC-TEL 2019. LNCS, vol. 11722, pp. 69–82. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29736-7_6
Broisin, J., Venant, R., Vidal, P.: Lab4CE: a remote laboratory for computer education. Int. J. Artif. Intell. Educ. 27(1), 154–180 (2015). https://doi.org/10.1007/s40593-015-0079-3
Conati, C., Barral, O., Putnam, V., Rieger, L.: Toward personalized XAI: a case study in intelligent tutoring systems. Artif. Intell. 298, 103503 (2021)
Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. (2007)
Keuning, H., Jeuring, J., Heeren, B.: A systematic literature review of automated feedback generation for programming exercises. ACM Trans. Comput. Educ. 19, 1–43 (2018)
Kleij, E., Veldkamp, T.: Effects of feedback in a computer-based assessment for learning - ScienceDirect. Comput. Educ. (2012)
Kraus, S., et al.: AI for explaining decisions in multi-agent environments. In: Proceedings of the AAAI Conference on Artificial Intelligence. Springer (2020)
Narciss, S., Huth, K., Narciss, D.: How to design informative tutoring feedback for multi-media learning. Inst. Des. Multimedia Learn. (2002)
Serral, E., De Weerdt, J., Sedrakyan, G., Snoeck, M.: Automating immediate and personalized feedback taking conceptual modelling education to a next level (2016)
Timmers, C., Veldkamp, B.: Attention paid to feedback provided by a computer-based assessment for learning on information literacy. Comput. Educ. (2011)
Wisniewski, B., Zierer, K., Hattie, J.: The power of feedback revisited: a meta-analysis of educational feedback research. Front. Psychol. (2020)
Yeo, G., Neal, A.: Subjective cognitive effort: a model of states, traits, and time. J. Appl. Psychol. 93, 617 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Félix, E., Amadieu, F., Venant, R., Broisin, J. (2022). Process and Self-regulation Explainable Feedback for Novice Programmers Appears Ineffectual. In: Hilliger, I., Muñoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds) Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-031-16290-9_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16289-3
Online ISBN: 978-3-031-16290-9
eBook Packages: Computer ScienceComputer Science (R0)