TY - GEN
T1 - An AI-Learner Shared Control Model Design for Adaptive Practicing
AU - Yan, Hongxin
AU - Lin, Fuhua
AU - Kinshuk,
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Online higher education offers great learning flexibility but demands learners’ high self-regulated learning (SRL) skills, especially in self-paced and asynchronous online learning. The lack of SRL skills in many learners often leads to poor academic outcomes, underscoring the need for SRL support. Our study introduces CAP (Confidence-based Adaptive Practicing), a model of adaptive practicing designed to enhance SRL in STEM disciplines. CAP incorporates knowledge tracing and question sequencing as two core functions. Unlike traditional adaptive learning systems that rely solely on machine control, CAP integrates learner confidence feedback and learner control in its rule-based intuitive algorithms. To avert the subjectivities of human judgement on learner confidence, CAP employs Thompson Sampling machine learning to refine the algorithms for adaptive accuracy and efficiency. This innovative AI-learner shared control approach has garnered positive feedback from field experts, highlighting its potential effectiveness in facilitating SRL.
AB - Online higher education offers great learning flexibility but demands learners’ high self-regulated learning (SRL) skills, especially in self-paced and asynchronous online learning. The lack of SRL skills in many learners often leads to poor academic outcomes, underscoring the need for SRL support. Our study introduces CAP (Confidence-based Adaptive Practicing), a model of adaptive practicing designed to enhance SRL in STEM disciplines. CAP incorporates knowledge tracing and question sequencing as two core functions. Unlike traditional adaptive learning systems that rely solely on machine control, CAP integrates learner confidence feedback and learner control in its rule-based intuitive algorithms. To avert the subjectivities of human judgement on learner confidence, CAP employs Thompson Sampling machine learning to refine the algorithms for adaptive accuracy and efficiency. This innovative AI-learner shared control approach has garnered positive feedback from field experts, highlighting its potential effectiveness in facilitating SRL.
KW - Adaptive Practicing
KW - Confidence-based Adaptive Practicing
KW - Knowledge Tracing
KW - Question Sequencing
KW - Self-regulated Learning
KW - Wheel-spinning
UR - http://www.scopus.com/inward/record.url?scp=85195864633&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-63028-6_21
DO - 10.1007/978-3-031-63028-6_21
M3 - Published Conference contribution
AN - SCOPUS:85195864633
SN - 9783031630279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 272
EP - 280
BT - Generative Intelligence and Intelligent Tutoring Systems - 20th International Conference, ITS 2024, Proceedings
A2 - Sifaleras, Angelo
A2 - Lin, Fuhua
T2 - 20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024
Y2 - 10 June 2024 through 13 June 2024
ER -