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LEAP-LAB-KUS/leap-kt-afm-2006
leap-kt-afm-2006 is a machine learning model from LEAP-LAB-KUS. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for leap-kt. The card lists the license as mit.
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Updated Aug 4, 2026
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From the Hugging Face model README
AFM —
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.
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.
| dataset | AUC | ACC | F1 | published reference | delta |
|---|---|---|---|---|---|
algebra2005 | 0.7150 ± 0.0006 | 0.7845 | 0.8718 | — | — |
assist2009 | 0.6167 ± 0.0007 | 0.6640 | 0.7874 | — | — |
assist2015 | 0.6511 ± 0.0005 | 0.7369 | 0.8465 | — | — |
dbe_kt22 | 0.7217 ± 0.0002 | 0.7750 | 0.8671 | — | — |
ednet500 | 0.5995 ± 0.0012 | 0.6537 | 0.7820 | — | — |
statics2011 | 0.7863 ± 0.0006 | 0.7928 | 0.8755 | — | — |
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.
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.
<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
Produced by leap-kt at commit(s) 92091dd.