@article{4766,
  author = {Hsing-Cheng Liu, Yao-Liang Chung},
  title = {An Integrated Multi-Method Framework for Analyzing Learning Dynamics and Knowledge Tracing in Educational Data},
  journal = {Journal of E-Technology},
  year = {2026},
  volume = {17},
  number = {3},
  doi = {https://doi.org/10.6025/jet/2026/17/3/139-157},
  url = {https://www.dline.info/jet/fulltext/v17n3/jetv17n3_3.pdf},
  abstract = {The rapid growth of online learning has generated vast educational data, yet existing Knowledge Tracing
(KT) research predominantly focuses on predictive accuracy. This narrow focus often neglects the underlying
mechanisms of learning progression, latent cognitive state transitions, and the structural organization of
curricula. To address these critical limitations, this study proposes an integrated, multi-method analytical
framework applied to the large-scale EdNet KT1 dataset. The framework synergistically combines Item
Response Theory, Hidden Markov Modeling, Learning Transition Networks, Community Detection, Dynamic
Time Warping trajectory clustering, Transformer-based Knowledge Tracing, and SHAP-based explainable
AI.
The converging quantitative results reveal a highly homogeneous learning environment within the dataset,
characterized by universal learner mastery, strong network connectivity, and a unified knowledge structure
without distinct subcommunities. Despite this data homogeneity, the framework successfully demonstrates
its capacity to map complex learning dynamics from multiple analytical perspectives. Crucially, SHAP analysis
confirms that the deep learning model's predictions are driven by pedagogically meaningful features, such
as historical correctness and item difficulty, rather than opaque statistical artifacts.
By transcending traditional single-method approaches, this research provides a comprehensive, multidimensional
understanding of learning dynamics. It effectively bridges the gap between abstract algorithmic
prediction and actionable educational insights, ultimately offering a robust blueprint for developing
transparent, trustworthy, and adaptive personalized learning interventions in modern intelligent educational
systems.},
}