Learning Analytics in Informal, Participatory Collaborative Learning

Michelle L. F. Cheong (), Aditya V. Singh (), Jean Y.-C. Chen () and Bing Tian Dai ()
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Michelle L. F. Cheong: Singapore Management University
Aditya V. Singh: Singapore Management University
Jean Y.-C. Chen: Singapore Management University
Bing Tian Dai: Singapore Management University

Chapter Chapter 19 in Handbook of Big Data and Analytics in Accounting and Auditing, 2023, pp 439-462 from Springer

Abstract: Abstract Learning Analytics was recognized to be “the third wave of large-scale developments in instructional technology”. Learning Management Systems (LMSs) have been widely adopted as the learning analytics tools because the captured data represents how the learners’ interact with the system during formal learning. However, most LMSs’ analytics models do not capture learning activities outside the systems. We built an integrated Telegram mobile application and a web-based portal discussion forum, to enable informal, participatory and collaborative learning beyond the classroom. We analyzed student-initiated question-and-answer discussion posts where our machine learning algorithm will predict the quality of the posts, and the system will prompt the students to improve their posts. With six in-built engagement features, our system generated higher number of high-quality posts, resulting in better learning outcomes among the students. Based on three implementation runs in an undergraduate course, our results show that there were positive correlations between post quality and student assessment outcomes. Students who used the system could achieve higher knowledge gain, and in-class intervention by the course instructor to review the weekly discussion posts will further improve knowledge gain. Mandatory participation benefitted the academically stronger students, while academically weaker students will need positive intervention actions when mandatory use of the system is enforced. We envisage that our system can be a successful alternative for workplace learning and ultimately contribute to organization knowledge creation. Using the system, working professionals can post questions and answers shared among peers within their own organizations and learn through such informal discussions, which can be blended seamlessly in their day-to-day workflow. While our system has not been implemented in workplace learning, we attempt to draw inference from our implementation results, to understand the parallels in the business organization context.

Keywords: Q&A Forum; Text mining; Natural language processing; Machine learning; Impact assessment; Informal learning; Participatory collaborative learning; Workplace learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-19-4460-4_19

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