Details

Autor(en) / Beteiligte

• Akhuseyinoglu, Kamil
• Brusilovsky, Peter
• Herder, Eelco
• Masthoff, Judith
• Tintarev, Nava
• Tkalčič, Marko

Titel

Data-Driven Modeling of Learners’ Individual Differences for Predicting Engagement and Success in Online Learning

Ist Teil von

• Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, 2021, p.201-212

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2021

Quelle

ACM Digital Library

Beschreibungen/Notizen

• Individual differences have been recognized as an important factor in the learning process. However, there are few successes in using known dimensions of individual differences in solving an important problem of predicting student performance and engagement in online learning. At the same time, learning analytics research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and could be used to connect these patterns with measures of student performance. Our paper attempts to bridge these two research directions. By applying a sequence mining approach to a large volume of learner data collected by an online learning system, we build models of student learning behavior. However, instead of following modern work on behavior mining (i.e., using this behavior directly for performance prediction tasks), we attempt to follow traditional work on modeling individual differences in quantifying this behavior on a latent data-driven personality scale. Our research shows that this data-driven model of individual differences performs significantly better than several traditional models of individual differences in predicting important parameters of the learning process, such as success and engagement.