An AI-Enabled Learning System with Personalized Learning Pathways: A Pilot Study of Its Impact on Learning of Statistics (75364)

Session Information: Design, Implementation & Assessment of Innovative Technologies in Education
Session Chair: Ed Sykes

Saturday, 25 November 2023 10:25
Session: Session 1
Room: Room C (Live Stream)
Presentation Type: Live-Stream Presentation

All presentation times are UTC + 9 (Asia/Tokyo)

AI-enabled systems offering personalized learning pathways are gaining imminence with their capability to meet diverse learners’ needs at-scale. In this work, we piloted an off-the-shelf learning resource that offers personalized learning pathways (termed as LeaP), an adaptive learning tool integrated within the learning management system (LMS). LeaP comprises a mapping engine, a ranking engine and a recommendation engine. Semantic algorithm enables LeaP to dynamically determine content relevancy against quiz performance at the diagnostic or post-study phases, offering suitable learning resources embedded within the LMS to close learning gaps just-in-time. A LeaP revision unit was designed in a freshman statistics course to prepare learners (n = 357) for an Excel skills test in linear regression in the October 2022 semester. The variables analyzed were the Excel test scores, extent of LeaP completion (engagement levels) and prior ability measured by the preceding semester Grade Point Average (GPA). Using non-parametric statistics, it was found that amongst LeaP users, levels of engagement were not differentiated by GPA, indicating the choice of LeaP usage was not motivated by prior ability. However, the greatest and significant score differences were found between non-LeaP learners and those who fully completed all the LeaP quizzes, with higher scores favouring the latter. At-risk learners had poorer engagement levels and test performance compared to non-at-risk peers, which warrants a closer look at how intelligent tutoring systems (ITS) should be designed to meet their needs in online learning environments. Limitations and suggestions for future implementation and research were also proposed.

Authors:
Poh Nguk Lau, Temasek Polytechnic, Singapore
Steven Chee Kuen Ng, Temasek Polytechnic, Singapore
Li Fern Tan, Temasek Polytechnic, Singapore


About the Presenter(s)
Ms Poh Nguk Lau is a University Associate Professor/Senior Lecturer at Temasek Polytechnic in Singapore

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00