Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2022
Abstract
Implementing adaptive learning is often a challenging task at higher learning institutions where the students come from diverse backgrounds and disciplines. In this work, we collected informal learning journals from learners. Using the journals, we trained two machine learning models, an automated topic alignment and a doubt detection model to identify areas of adjustment required for teaching and students who require additional attention. The models form the baseline for a quiz recommender tool to dynamically generate personalized quizzes for each learner as practices to reinforce learning. Our pilot deployment of our AI-enabled Adaptive Learning System showed that our approach delivers promising results for learner-centered teaching and personalized learning.
Keywords
Adaptive, Personalized Learning, Learning Analytics, AI in Education
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Pacific Asia Conference on Information Systems PACIS 2022: Virtual, July 5-9: Proceedings
First Page
1
Last Page
16
Publisher
AIS
City or Country
Virtual
Citation
TAN, Kar Way; LO, Siaw Ling; OUH, Eng Lieh; and NEO, Wei Leng.
AI-enabled adaptive learning using automated topic alignment and doubt detection. (2022). Pacific Asia Conference on Information Systems PACIS 2022: Virtual, July 5-9: Proceedings. 1-16.
Available at: https://ink.library.smu.edu.sg/sis_research/7200
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://aisel.aisnet.org/pacis2022/110/