Improving Prediction of Students' Performance in Intelligent Tutoring Systems Using Attribute Selection and Ensembles of Different Multimodal Data Sources

Autor/inn/en	Chango, Wilson; Cerezo, Rebeca; Sanchez-Santillan, Miguel; Azevedo, Roger; Romero, Cristóbal
Titel	Improving Prediction of Students' Performance in Intelligent Tutoring Systems Using Attribute Selection and Ensembles of Different Multimodal Data Sources
Quelle	In: Journal of Computing in Higher Education, 33 (2021) 3, S.614-634 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Cerezo, Rebeca)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1042-1726
DOI	10.1007/s12528-021-09298-8
Schlagwörter	Grade Prediction; Intelligent Tutoring Systems; College Students; Data Use; Learning Strategies; Psychological Patterns; Nonverbal Communication; Eye Movements; Pretests Posttests + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Collegestudent; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Non-verbal communication; Nonverbale Kommunikation; Augenbewegung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01