CLASSIFYING LEARNERS’ COGNITIVE ENGAGEMENT FROM ONLINE DISCUSSION USING TEXT MINING

<p class="2">The purpose of this paper is to explore how adult learners engage in asynchronous online discussion through the implementation of an audio-based argumentation activity. The study designed scaffolded audio-based argumentation activities to promote students’ cognitive engagement. The research was conducted in an online graduate course at a liberal arts university. Primary data sources were learners’ text-based discussions, audio-recorded argumentation postings, and semi-structured interviews. Findings indicate that the scaffolded, audio-based argumentation activity helped students achieve higher levels of thinking skills as well as exert greater cognitive efforts during discussions. In addition, most students expressed a positive perception of and satisfaction with their experience. Implications for practice and future research areas are discussed.</p>

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Game learning analytics of instant messaging and online discussion forums in higher education

Education + Training ◽

10.1108/et-11-2020-0334 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ana Beatriz Hernández-Lara ◽

Alexandre Perera-Lluna ◽

Enric Serradell-López

Keyword(s):

Text Mining ◽

Learning Process ◽

Learning Analytics ◽

Instant Messaging ◽

Online Discussion ◽

Communication Patterns ◽

Discussion Forums ◽

Communication Tools ◽

Content Type ◽

Online Discussion Forums

PurposeWith the growth of digital education, students increasingly interact in a variety of ways. The potential effects of these interactions on their learning process are not fully understood and the outcomes may depend on the tool used. This study explores the communication patterns and learning effectiveness developed by students using two basic synchronous and asynchronous communication tools in e-learning environments, specifically business simulation games.Design/methodology/approachThe authors conduct a quasi-experiment research with 478 online business students, 267 of whom used online discussion forums and 211 interacted via an instant messaging app. The application of learning analytics and text mining on natural language processing allows us to explore the student communication patterns with each of tools and their effectiveness in terms of learning.FindingsThe results confirm the complementarity of the communication tools, asynchronous tools being especially the suitable for task-related communication and synchronous ones for speeding up and facilitating student social interactions.Originality/valueThe main value of this research lies in the use of data analytics and text mining to access and analyse the content of student interactions to assess the learning process in greater depth, comparing synchronous and asynchronous learning modes, considering that little is known about the impact of online synchronous interaction or instant messaging, and even less about the different features, content and performance that emerge when these two learner interaction modalities are compared.

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The Effects of Facilitating Feedback on Online Learners’ Cognitive Engagement: Evidence from the Asynchronous Online Discussion

Education Sciences ◽

10.3390/educsci4020193 ◽

2014 ◽

Vol 4 (2) ◽

pp. 193-208 ◽

Cited By ~ 6

Author(s):

Wenge Guo ◽

Ye Chen ◽

Jing Lei ◽

Yan Wen

Keyword(s):

Online Discussion ◽

Cognitive Engagement ◽

Online Learners ◽

Asynchronous Online Discussion

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Which Step Do I Take First? Troubleshooting with Bayesian Models

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00123 ◽

2015 ◽

Vol 3 ◽

pp. 73-85

Author(s):

Annie Louis ◽

Mirella Lapata

Keyword(s):

Text Mining ◽

Human Performance ◽

Question Answering ◽

Online Discussion ◽

Generative Models ◽

Experimental Results ◽

Discussion Forums ◽

Community Question Answering ◽

Online Discussion Forums ◽

Share Information

Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information. In this paper, we propose text mining techniques which aid users navigate troubleshooting-oriented data such as questions asked on forums and their suggested solutions. We introduce Bayesian generative models of the troubleshooting data and apply them to two interrelated tasks: (a) predicting the complexity of the solutions (e.g., plugging a keyboard in the computer is easier compared to installing a special driver) and (b) presenting them in a ranked order from least to most complex. Experimental results show that our models are on par with human performance on these tasks, while outperforming baselines based on solution length or readability.

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