Nazmus Sakeef, M. Ali Akber Dewan, Fuhua Lin, Dharamjit Parmar
{"title":"Detecting cognitive engagement in online course forums: A review of frameworks and methodologies","authors":"Nazmus Sakeef, M. Ali Akber Dewan, Fuhua Lin, Dharamjit Parmar","doi":"10.1016/j.nlp.2025.100146","DOIUrl":null,"url":null,"abstract":"<div><div>A key aspect of online learning in higher education involves the utilization of course discussion forums. Assessing the quality of posts, such as cognitive engagement, within online course discussion forums, and determining students’ interest and participation is challenging yet beneficial. This research investigates existing literature on identifying the cognitive engagement of online learners through the analysis of course discussion forums. Essentially, this review examines three educational frameworks - <em>Van Der Meijden’s Knowledge Construction in Synchronous and Asynchronous Discussion Posts (KCSA), Community of Inquiry (CoI), and Interactive, Constructive, Active, and Passive (ICAP)</em>, which have been widely used for students’ cognitive engagement detection analyzing their posts in course discussion forums. This study also examines the natural language processing and deep learning approaches employed and integrated with the above three educational frameworks in the existing literature concerning the detection of cognitive engagement in the context of online learning. The article provides recommendations for enhancing instructional design and fostering student engagement by leveraging cognitive engagement detection. This research underscores the significance of automating the identification of cognitive engagement in online learning and puts forth suggestions for future research directions.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100146"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Language Processing Journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949719125000226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A key aspect of online learning in higher education involves the utilization of course discussion forums. Assessing the quality of posts, such as cognitive engagement, within online course discussion forums, and determining students’ interest and participation is challenging yet beneficial. This research investigates existing literature on identifying the cognitive engagement of online learners through the analysis of course discussion forums. Essentially, this review examines three educational frameworks - Van Der Meijden’s Knowledge Construction in Synchronous and Asynchronous Discussion Posts (KCSA), Community of Inquiry (CoI), and Interactive, Constructive, Active, and Passive (ICAP), which have been widely used for students’ cognitive engagement detection analyzing their posts in course discussion forums. This study also examines the natural language processing and deep learning approaches employed and integrated with the above three educational frameworks in the existing literature concerning the detection of cognitive engagement in the context of online learning. The article provides recommendations for enhancing instructional design and fostering student engagement by leveraging cognitive engagement detection. This research underscores the significance of automating the identification of cognitive engagement in online learning and puts forth suggestions for future research directions.
高等教育在线学习的一个重要方面是课程论坛的利用。评估在线课程论坛中帖子的质量,如认知参与,并确定学生的兴趣和参与是具有挑战性的,但也是有益的。本研究通过对课程讨论论坛的分析,调查了现有的关于识别在线学习者认知参与的文献。从本质上讲,本综述考察了三个教育框架- Van Der Meijden的同步和异步讨论帖子中的知识构建(KCSA),探究社区(CoI)和互动,建设性,主动和被动(ICAP),这些框架已被广泛用于学生的认知参与检测分析他们在课程讨论论坛中的帖子。本研究还考察了现有文献中关于在线学习背景下认知参与检测的自然语言处理和深度学习方法与上述三种教育框架的结合。本文提供了通过利用认知参与检测来加强教学设计和促进学生参与的建议。本研究强调了在线学习中认知参与自动化识别的重要性,并对未来的研究方向提出了建议。