Personalized Learning Systems for Computer Science Students: Analyzing and Predicting Learning Behaviors Using Programming Error Data
Authors: 
Mubina Kamberovic, Senka Krivic, 
Amra Delic, Sandor Szedmak, Vedran LjubovicAuthors Info & Claims
Mubina Kamberovic, Senka Krivic, Amra Delic, Sandor Szedmak, Vedran LjubovicAuthors Info & ClaimsPages 86 - 91
Abstract
The integration of technology in education has become indispensable in acquiring new skills, knowledge, and competencies. This paper addresses the issue of analyzing and predicting the learning behavior of Computer Science students. Specifically, we present a dataset of compiler errors made by students during the first semester of an Introduction to Programming course where they learn the C programming language. We approach the problem of predicting the number of student errors as a missing data imputation problem, utilizing several prediction methods including Singular Value Decomposition, Polynomial Regression via Latent Tensor Reconstruction, Neural Network-based method, and Gradient Boosting. Our experimental results demonstrate high accuracy in predicting student learning behaviors over time, which can be leveraged to enhance personalized learning for individual students.
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Published In
June 2023
446 pages
ISBN:9781450398916
DOI:10.1145/3563359
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Published: 16 June 2023
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UMAP '23: 31st ACM Conference on User Modeling, Adaptation and Personalization
June 26 - 29, 2023
Limassol, Cyprus
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