Abstract
AI-enabled systems offering personalized learning pathways or options are gaining imminence, showing immense potential to meet diverse learners’ needs on a more practical scale. In this work, we piloted a learning resource that offers personalized learning pathways (or LeaP), powered by AI technology. The efficacy of the learning tool was evaluated using a skills test in a freshman statistics course. The results largely replicated what was found in the literature. For learners who used the resource, levels of engagement were not dependent on prior ability measured by past-semester GPA performance. The greatest difference in test scores was seen in the test task which the LeaP unit modelled after, with significant differences between learners who engaged with LeaP deeply versus those who did not attempt the unit at all. 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. Suggestions for future implementation and research were also proposed.
Author Information
Poh Nguk Lau, Temasek Polytechnic, Singapore
Steven Chee Kuen Ng, Temasek Polytechnic, Singapore
Li Fern Tan, Temasek Polytechnic, Singapore
Paper Information
Conference: ACE2023
Stream: Design
This paper is part of the ACE2023 Conference Proceedings (View)
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To cite this article:
Lau P., Ng S., & Tan L. (2024) An AI-Enabled Learning System With Personalized Learning Pathways a Pilot Study of Its Impact on Learning of Statistics ISSN: 2186-5892 The Asian Conference on Education 2023: Official Conference Proceedings https://doi.org/10.22492/issn.2186-5892.2024.104
To link to this article: https://doi.org/10.22492/issn.2186-5892.2024.104
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