Part of leap-kt-toolkit, a systematic re-implementation of published Knowledge Tracing models under one protocol. This repository holds every fold of every dataset this model has been run on, with the per-epoch training logs and the exact user split alongside the checkpoints.
Protocol
User-level 80/20 train/test split · 5-fold cross-validation over the training portion · held-out fold as validation · early stopping patience 10 on validation AUC · max 200 epochs.
Every cell in the project runs under identical settings; a cell that cannot is recorded as a documented failure rather than re-run under bespoke settings.
Results
dataset
AUC
ACC
F1
published reference
delta
assist2009
0.7273 ± 0.0034
0.6971
0.7740
—
—
assist2012
0.7452 ± 0.0011
0.7351
0.8236
—
—
assist2017
0.7044 ± 0.0012
0.6864
0.4486
—
—
bridge2algebra2006
0.7464 ± 0.0008
0.8358
0.9067
—
—
dbe_kt22
0.8042 ± 0.0007
0.7950
0.8737
—
—
Per-fold values are in each dataset's summary.json. The mean is never reported without the spread — 0.75 ± 0.001 and 0.75 ± 0.09 are different claims.
Why these numbers may differ from other reproductions
Multi-concept questions are not expanded into multiple rows. Toolkits that do expand them place consecutive test positions carrying the same question and the same response, so a model is shown the answer one step before predicting it; on ASSIST2009 that is around 37% of positions and lifts DKT from a published ~0.75 to ~0.89 AUC. Here concepts are an extra axis on the interaction rather than extra rows, so the leak is not expressible and every interaction is scored exactly once.
Cells carrying a published reference value are additionally leak-audited before release: train/test user disjointness, no window crossing the split boundary, exactly-once scoring, and a label-shuffle control that must collapse AUC to chance. Cells with no comparable published number rely on the structural guarantee above rather than on that audit.
Files
<dataset>/summary.json mean ± std and per-fold AUC
<dataset>/split.json the exact user partition, with a checksum
<dataset>/fold<k>/checkpoint/ config.json + weights
<dataset>/fold<k>/epochs.jsonl every epoch's train loss and validation metrics;
each row carries its own model/dataset/fold
<dataset>/fold<k>/run.json protocol and package version for that run
