TY - GEN
T1 - Adaptive Sequence Learning
T2 - 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023
AU - Nguyen, Danny
AU - Howard, Leo
AU - Yan, Hongxin
AU - Lin, Fuhua
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Personalized learning paths have become a promising instructional strategy in online learning, as they can cater to individual learners' needs and preferences. However, creating effective personalized learning paths is a complex task due to the high degree of variability in learners' characteristics, behaviors, and learning contexts. Existing recommendation methods do not adequately address this challenge, as they do not work effectively in dynamic environments. This paper tries to address this gap by proposing a personalized learning path recommendation system using a contextual multi-armed bandit approach to offer a student an optimal learning sequence and provide the student with a modified sequence when re-planning is required.
AB - Personalized learning paths have become a promising instructional strategy in online learning, as they can cater to individual learners' needs and preferences. However, creating effective personalized learning paths is a complex task due to the high degree of variability in learners' characteristics, behaviors, and learning contexts. Existing recommendation methods do not adequately address this challenge, as they do not work effectively in dynamic environments. This paper tries to address this gap by proposing a personalized learning path recommendation system using a contextual multi-armed bandit approach to offer a student an optimal learning sequence and provide the student with a modified sequence when re-planning is required.
KW - Multi-Armed bandit (MAB) algorithms
KW - adaptive learning
KW - exploration and exploitation
KW - knowledge components (KC)
KW - personalized learning
UR - http://www.scopus.com/inward/record.url?scp=85182589812&partnerID=8YFLogxK
U2 - 10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361498
DO - 10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361498
M3 - Published Conference contribution
AN - SCOPUS:85182589812
T3 - 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023
SP - 751
EP - 756
BT - 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023
Y2 - 14 November 2023 through 17 November 2023
ER -