Journal of E-Technology Hsing-Cheng Liu, Yao-Liang Chung 17 3 2026 https://doi.org/10.6025/jet/2026/17/3/139-157 https://www.dline.info/jet/fulltext/v17n3/jetv17n3_3.pdf 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.