@inproceedings{ihlenfeldt-fancsali-2026-reliability,
title = "Reliability Estimation Methods for {B}ayesian Knowledge Tracing Mastery Classifications",
author = "Ihlenfeldt, Samuel D. and
Fancsali, Stephen E.",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting-Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Full Papers",
month = oct,
year = "2026",
address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2026.aimecon-main.32/",
pages = "288--296",
ISBN = "979-8-9983004-0-0",
abstract = "We evaluate the psychometric reliability of Bayesian Knowledge Tracing for skill mastery classification. Using large-scale intelligent tutoring system data, we compare two simulation-based approaches for estimating classification consistency as reliability. Results support both approaches, show reliability increases with response-sequence length, and highlight the necessity of adjusting for chance agreement."
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%0 Conference Proceedings
%T Reliability Estimation Methods for Bayesian Knowledge Tracing Mastery Classifications
%A Ihlenfeldt, Samuel D.
%A Fancsali, Stephen E.
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F ihlenfeldt-fancsali-2026-reliability
%X We evaluate the psychometric reliability of Bayesian Knowledge Tracing for skill mastery classification. Using large-scale intelligent tutoring system data, we compare two simulation-based approaches for estimating classification consistency as reliability. Results support both approaches, show reliability increases with response-sequence length, and highlight the necessity of adjusting for chance agreement.
%U https://aclanthology.org/2026.aimecon-main.32/
%P 288-296
Markdown (Informal)
[Reliability Estimation Methods for Bayesian Knowledge Tracing Mastery Classifications](https://aclanthology.org/2026.aimecon-main.32/) (Ihlenfeldt & Fancsali, AIME-Con 2026)
ACL