Independent edge evaluation for directed acyclic graphs in probabilistic sequence modeling

For sub-step scaffold rows (original=0), records are retained and programmatically inherit latent dependency labels from the parent main task via the assistment_id. The final execution filters a restricted cohort of exactly M = 669 unique learners, structured to enforce zero temporal data leakage during downstream sequence modeling. The second component is the Prerequisite Graph Topology (stored in assistments_prereq_edges.sql).

This file provides the explicit graph-theoretic formalization of the assessment space used to evaluate cross-skill transfer. Parsed directly via regular expressions within the scripts, it contains a mapping of 110 unique Knowledge Components (KCs), directed prerequisite edges mapping cognitive transitions, and a set of 857 pre-enumerated valid pedagogical paths ordered longest-first, acting as the ground-truth Directed Acyclic Graph (DAG) templates.

The third component is the Complete Source Code Package (stored in IEE_DAG_Final_Submit.ipynb). This comprehensive Python replication notebook contains the complete implementation pipeline. It includes environment setup pinning sympy==1.13.1 to resolve native compatibility conflicts with PyTorch 2.x optimizers.

It provides the exact mathematical architecture for the proposed IEE-BKT model (implementing localized evidence vectors and K-step sequential Bayesian updates) along with re-implemented baselines including Standard BKT, LSTM-based Deep Knowledge Tracing (DKT), and Self-Attentive Knowledge Tracing (SAKT). Finally, it executes a strict student-level 10-fold cross-validation routine to control for intra-student tracking bias.

Researchers can utilize these files to fully replicate the evaluation metrics, including paired t-test distributions, RMSE improvements, and visual ROC/AUC patterns where IEE-BKT secures a baseline performance of AUC = 0.5514.