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Published in:
2020 | OriginalPaper | Chapter
Online Learning Engagement Assessment Based on Multimodal Behavioral Data
Authors : Liying Wang, Yunfan He
Published in: Transactions on Edutainment XVI
Publisher: Springer Berlin Heidelberg
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Abstract
Online learning monitoring is still on the urgent demand to track and analyze the learning engagement of the learners. To this end, the multimodal behavioral data of online learners are collected during the online learning process from the psychological, physiological and behavioral dimension which are respectively dependent on the techniques of expression recognition, physiological heart rate acquisition with Internet of Things and operation events listening. Based on the dataset with 25 experimental p, online learning engagement states are statistically analyzed and assessed by applying the schema including both prior rules and data fitting method which can quantitatively evaluate the learning engagement. Through the questionnaire survey and statistical validation, the assessment results show the schema can measure learning engagement in multiple dimensions and evaluate the whole state more comprehensively and automatically.