LUO, WEI-JIE WANG, WEI-HAO YIN, TIANJIAO LI, CHENG-HSUAN SHIH, YU-TING
Graduate Institute of Educational Information and Measurement, National Taichung University of Education
Email：BMS113104@gm.ntcu.edu.tw
Abstract
This study aims to investigate whether the self-directed learning agent in TALPer follows the Self-Regulated Learning (SRL) model to effectively guide students in exhibiting self-regulated learning behaviors. While the Taiwan Adaptive Learning Platform (TALP) provides students with opportunities for autonomous learning, the absence of direct teacher support makes it crucial for AI-based learning agents to offer timely and appropriate guidance. This research focuses on whether the learning agent in TALPer can
deliver direction-oriented and feedback-rich support in alignment with SRL theory, assisting students in setting clear learning goals, selecting strategies, monitoring their progress, and making necessary adjustments. Based on the four key SRL phases—goal setting, strategic planning, monitoring, and regulation—this study employs lag sequential analysis to examine the sequences of learning behaviors exhibited during student–system interactions. The findings contribute to understanding the role of TALPer’s learning support tools in promoting self-regulated learning and offer practical implications for the design of future digital learning environments.
Keywords: Lag Sequential Analysis, Self-Regulated Learning, Learning Behavior Analysis, Taiwan Adaptive Learning Platform (TALP), TALPer