LEAP-LAB-KUS/leap-kt-deepirt-2019-04

leap-kt · DEEPIRT

Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory — Yeung, EDM 2019 (arXiv:1904.11738)

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
algebra2005	0.8034 ± 0.0026	0.8065	0.8794	—	—
assist2009	0.7512 ± 0.0015	0.7327	0.8146	—	—
assist2015	0.7197 ± 0.0011	0.7506	0.8470	—	—
dbe_kt22	0.7843 ± 0.0004	0.7881	0.8702	—	—
ednet500	0.6544 ± 0.0015	0.6737	0.7835	—	—

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.

Each published cell passed a leak audit before being recorded: train/test user disjointness, no window crossing the split boundary, exactly-once scoring, and a label-shuffle control that must collapse AUC to chance.

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

Provenance

Produced by leap-kt at commit(s) 4365ff7, f8dc2a0.