Machine Learning Applied to Student Attentiveness Detection: Using Emotional and Non-Emotional Measures

Autor/inn/en	Elbawab, Mohamed; Henriques, Roberto
Titel	Machine Learning Applied to Student Attentiveness Detection: Using Emotional and Non-Emotional Measures
Quelle	In: Education and Information Technologies, 28 (2023) 12, S.15717-15737 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Henriques, Roberto)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-023-11814-5
Schlagwörter	Students; Electronic Learning; Attention Span; Artificial Intelligence; Measures (Individuals); Emotional Response; Accuracy; Nonverbal Communication; Student Behavior + Suchen Sie Ihr Suchwort? Student; Schüler; Schülerin; Studentin; Künstliche Intelligenz; Messdaten; Emotionales Verhalten; Non-verbal communication; Nonverbale Kommunikation; Student behaviour; Schülerverhalten
Abstract	Electronic learning (e-learning) is considered the new norm of learning. One of the significant drawbacks of e-learning in comparison to the traditional classroom is that teachers cannot monitor the students' attentiveness. Previous literature used physical facial features or emotional states in detecting attentiveness. Other studies proposed combining physical and emotional facial features; however, a mixed model that only used a webcam was not tested. The study objective is to develop a machine learning (ML) model that automatically estimates students' attentiveness during e-learning classes using only a webcam. The model would help in evaluating teaching methods for e-learning. This study collected videos from seven students. The webcam of personal computers is used to obtain a video, from which we build a feature set that characterizes a student's physical and emotional state based on their face. This characterization includes eye aspect ratio (EAR), Yawn aspect ratio (YAR), head pose, and emotional states. A total of eleven variables are used in the training and validation of the model. ML algorithms are used to estimate individual students' attention levels. The ML models tested are decision trees, random forests, support vector machines (SVM), and extreme gradient boosting (XGBoost). Human observers' estimation of attention level is used as a reference. Our best attention classifier is the XGBoost, which achieved an average accuracy of 80.52%, with an AUROC OVR of 92.12%. The results indicate that a combination of emotional and non-emotional measures can generate a classifier with an accuracy comparable to other attentiveness studies. The study would also help assess the e-learning lectures through students' attentiveness. Hence will assist in developing the e-learning lectures by generating an attentiveness report for the tested lecture. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01