Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses

被引：29

作者：

Chango W. ^{[1
]}

Cerezo R. ^{[2
]}

Romero C. ^{[3
]}

机构：

[1] Pontifical Catholic University of Ecuador, Department of Systems and Computing

[2] University of Oviedo - Spain, Department of Psychology

[3] University of Córdoba – Spain, Department of Computer Science

来源：

Computers and Electrical Engineering | 2021年 / 89卷

关键词：

Blended learning; Data fusion; Multimodal learning; Multisource data; Predicting academic performance;

D O I：

10.1016/j.compeleceng.2020.106908

中图分类号：

学科分类号：

摘要：

In this paper we apply data fusion approaches for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. We collect and preprocess data about first-year university students from different sources: theory classes, practical sessions, on-line Moodle sessions, and a final exam. Our objective is to discover which data fusion approach produces the best results using our data. We carry out experiments by applying four different data fusion approaches and six classification algorithms. The results show that the best predictions are produced using ensembles and selecting the best attributes approach with discretized data. The best prediction models show us that the level of attention in theory classes, scores in Moodle quizzes, and the level of activity in Moodle forums are the best set of attributes for predicting students’ final performance in our courses. © 2020

引用

共 25 条

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