Statistical rigor, stated plainly.
Every number here is measured on public data and reported as-is. None is a claim of state of the art.
Predictive accuracy on public benchmarks.
AHCOS's measurement engine was evaluated on public knowledge-tracing datasets. On ASSISTments-2009 it reaches an AUC of 0.835 (p<.005). Across these benchmarks, the ensemble beats every reproducible baseline we tested.
Every figure on this page is measured on public data and reported as-is. None is a claim of state of the art.
| Public benchmark | Metric | Result |
|---|---|---|
| ASSISTments-2009 | AUC | 0.835 |
| Statics-2011 | AUC | 0.824 |
| ASSISTments-2015 | AUC | 0.706 |
Note On Statics-2011 (0.824) and ASSISTments-2015 (0.706), results sit within the range reported in the field.
How is capability inferred?
Bayesian Knowledge Tracing (BKT)
Models the probability that a latent skill has been mastered, updating that belief as a learner produces correct and incorrect responses.
Item Response Theory (IRT)
Models ability as a latent trait, separating how able a person is from how hard or discriminating each item is.
The ensemble
Combining the two yields estimates that are both responsive and stable — and, critically, each estimate carries a confidence band rather than a bare number.
What it means — and what it does not.
An AUC of 0.835 means the model reliably ranks correct outcomes above incorrect ones on a recognized public benchmark. The low Expected Calibration Error (0.0586) means its stated confidence can be trusted — it backs our principle of confidence, not false certainty.
What it is not: a claim of state of the art, or a claim about anything beyond the measurement layer. These results validate the tracing and estimation engine — not other parts of the platform. Real-world outcome studies, run with pilot partners, are how this validation deepens next.
Sources and methods.
- DatasetsASSISTments 2009 & 2015; Statics 2011 — public knowledge-tracing benchmarks.
- MethodBayesian Knowledge Tracing (Corbett & Anderson, 1994); Item Response Theory.
- BaselinesDeep Knowledge Tracing (DKT) and successor models, used as reproducible comparison points.
- StandardW3C Verifiable Credentials; Open Badges 3.0.