leap-kt: 6 dataset(s), 138 files

44c6db3 verified 1 day ago

2.93 kB

license: mit
library_name: leap-kt
tags:
  - knowledge-tracing
  - education
  - educational-data-mining
  - pfa
datasets:
  - algebra2005
  - assist2009
  - assist2015
  - dbe_kt22
  - ednet500
  - statics2011
metrics:
  - auc
  - accuracy
  - f1

leap-kt · PFA

PFA —

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`	0.7590 ± 0.0003	0.7907	0.8738	—	—
`assist2009`	0.7029 ± 0.0001	0.6990	0.8016	—	—
`assist2015`	0.6873 ± 0.0002	0.7416	0.8466	—	—
`dbe_kt22`	0.7300 ± 0.0002	0.7761	0.8671	—	—
`ednet500`	0.6113 ± 0.0016	0.6540	0.7816	—	—
`statics2011`	0.7862 ± 0.0007	0.7926	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.

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) 92091dd.