README.md

---
license: mit
library_name: leap-kt
tags:
  - knowledge-tracing
  - education
  - educational-data-mining
  - bkt
datasets:
  - algebra2005
  - assist2009
  - assist2012
  - assist2015
  - bridge2algebra2006
  - dbe_kt22
  - ednet500
  - junyi
metrics:
  - auc
  - accuracy
  - f1
---

# leap-kt · BKT

**Bayesian Knowledge Tracing** — Corbett & Anderson, UMUAI 1994

Part of [leap-kt-toolkit](https://github.com/LEAP-LAB-KUS/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` | **0.7650** ± 0.0002 | 0.7937 | 0.8752 | — | — |
| `assist2009` | **0.7171** ± 0.0004 | 0.7144 | 0.8052 | — | — |
| `assist2012` | **0.6841** ± 0.0001 | 0.7152 | 0.8214 | — | — |
| `assist2015` | **0.7019** ± 0.0002 | 0.7471 | 0.8472 | — | — |
| `bridge2algebra2006` | **0.7461** ± 0.0002 | 0.8426 | 0.9119 | — | — |
| `dbe_kt22` | **0.7026** ± 0.0004 | 0.7766 | 0.8689 | — | — |
| `ednet500` | **0.6204** ± 0.0013 | 0.6513 | 0.7770 | — | — |
| `junyi` | **0.7366** ± 0.0001 | 0.7381 | 0.8313 | — | — |

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
```

## Provenance

Produced by `leap-kt` at commit(s) `1ad8f67`, `6c6da46`, `7b83c7d`, `7e2fc9f`.