{"title":"A review on multimodal online educational engagement detection system using facial expression, eye movement and speech recognition","authors":"R. Angeline, A. Nithya","doi":"10.17762/TURCOMAT.V12I9.3667","DOIUrl":null,"url":null,"abstract":"During this lockdown period, an online educational engagement system plays a vital role to enrich the knowledge of learners in various fields without interrupting their learning process. Online educational engagement systems include all the activities of a learner like listening, reading, writing, and so on. While participating in these activities, a participant may show various levels of engagements like fully engaged, partially engaged and completely not engaged. The participation of online learners has to be identified for an effective learning process. The existing literature could be classified depending upon the learners’ participation as automatic, semi-automatic and manual. Further it could be sub categorised based on the data types used to identify the engagement system. In this paper, a review on computer based automatic online educational engagement detection systems is presented. Several educational engagement methods are applied for computer based online engagement detection systems. In these systems examining a participant’s presence and attention with the modalities of facial expression, eye movement and speech are found to be a challenging task. In this work, it is also identified that there are few challenges like preparation and usage of proper datasets, identifying suitable performance metrics for different tasks involved and providing recommendations for future enhancement of online educational engagement detection by combining the modalities of facial expression, eye movement and speech are still unattended. Though there are several research gaps involved, an online educational engagement system will help the learners to engage themselves in a productive way of learning and getting evaluated efficiently and effectively during the lockdown period of pandemic disease COVID-19 without interrupting their learning process and gaining knowledge. © 2021 Karadeniz Technical University. All rights reserved.","PeriodicalId":52230,"journal":{"name":"Turkish Journal of Computer and Mathematics Education","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Turkish Journal of Computer and Mathematics Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17762/TURCOMAT.V12I9.3667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 1
基于面部表情、眼动和语音识别的多模式在线教育参与检测系统综述
在封锁期间,在线教育参与系统在不中断学习过程的情况下,在丰富学习者各个领域的知识方面发挥着至关重要的作用。在线教育参与系统包括学习者的所有活动,如听力、阅读、写作等。在参与这些活动时,参与者可能会表现出不同程度的参与,如完全参与、部分参与和完全不参与。为了有效的学习过程,必须确定在线学习者的参与情况。根据学习者的参与程度,现有文献可分为自动、半自动和手动。此外,可以根据用于识别参与系统的数据类型对其进行细分。本文综述了基于计算机的在线教育参与自动检测系统。几种教育参与方法被应用于基于计算机的在线参与检测系统。在这些系统中,用面部表情、眼球运动和言语的方式来检查参与者的存在和注意力是一项具有挑战性的任务。在这项工作中,还发现了一些挑战,如准备和使用合适的数据集,为所涉及的不同任务确定合适的性能指标,以及通过结合面部表情、眼动和语音的模式为未来增强在线教育参与检测提供建议,这些挑战仍然无人关注。尽管存在一些研究空白,但在线教育参与系统将帮助学习者在新冠肺炎疫情封锁期间以高效、有效的方式进行学习和评估,而不会中断他们的学习过程和获取知识。©2021卡拉德尼兹工业大学。保留所有权利。
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