Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

arXiv:2403.07194v1 Announce Type: cross
Abstract: The aim of this study was to predict university students' learning performance using different sources of data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from face recording videos, interaction zones from eye tracking, and test performance from final knowledge evaluation. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We …

abstract aim arxiv cs.ai cs.cy cs.hc cs.lg data data sources intelligent multimodal multimodal data performance prediction strategies students study systems tutoring type university