2021/10/28 by Wilson Chango, Rebeca Cerezo, Miguel Sanchez-Santillan +3 · 3 citations
Computer Science · #Educational Technology and Assessment #Intelligent Tutoring Systems and Adaptive Learning #Online Learning and Analytics
paper · pdf · doi:10.1007/s12528-021-09298-8
openalex publication_date 2021/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Abstract The aim of this study was to predict university students’ learning performance using different sources of performance and multimodal 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 videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.