Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses. (January 2021)

• Record Type: Journal Article
• Authors: Chango, Wilson; Cerezo, Rebeca; Romero, Cristóbal
• Is Part Of: Computers & electrical engineering. Volume 89(2021)
• Publication Date: 2021-01
• Subjects: Blended learning -- Predicting academic performance -- Multisource data -- Multimodal learning -- Data fusion; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106908
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Blended learning university courses provide us multiple-source and multimodal data. Data fusion approaches improve the prediction of students' academic performance. Ensembles and selecting the best attributes approach produce the best results. White-box classification models provide instructors very understandable explanations. Abstract: 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. Graphical abstract: Image, graphical abstract