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http://dx.doi.org/10.17662/ksdim.2022.18.4.067

A Study on Evaluation of e-learners' Concentration by using Machine Learning  

Jeong, Young-Sang (서울과학기술대학교 데이터사이언스)
Joo, Min-Sung ((주)위드마인드)
Cho, Nam-Wook (서울과학기술대학교 산업공학과)
Publication Information
Journal of Korea Society of Digital Industry and Information Management / v.18, no.4, 2022 , pp. 67-75 More about this Journal
Abstract
Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.
Keywords
E-learning; Concentration; Machine Learning; Ensemble Technique; Long Short-Term Memory (LSTM);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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