Personalized Learning Systems for Computer Science Students : Analyzing and Predicting Learning Behaviors Using Programming Error Data

Mubina Kamberovic*, Senka Krivic, Amra Delic, Sandor Szedmak, Vedran Ljubovic

*Corresponding author for this work

Research output: Contribution to conferenceAbstractScientificpeer-review

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.

Original languageEnglish
Pages86-91
Number of pages6
DOIs
Publication statusPublished - 26 Jun 2023
MoE publication typeNot Eligible
EventConference on User Modeling, Adaptation and Personalization - Limassol, Cyprus
Duration: 26 Jun 202330 Jun 2023
Conference number: 31

Conference

ConferenceConference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP
Country/TerritoryCyprus
CityLimassol
Period26/06/202330/06/2023

Keywords

  • adaptive systems
  • behaviour modeling
  • personalized learning
  • student modeling

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