Quantifying Precision in Measurement of Individualized, Evidence-Based Learning Across Sessions in Applied Behavior Analysis Therapy
Abstract
Current performance criteria used in Applied Behavior Analysis (ABA) are often based on whether a learner meets a predetermined benchmark e.g., 8 out of 10 correct responses across several sessions. Although these criteria help practitioners make instructional decisions, they provide limited information about how likely it is that the learner has truly acquired the skill. In this paper, we introduce a Bayesian approach that combines practitioners' prior expectations with the learner's ongoing performance data to estimate the learner's future performance level. Rather than providing only a pass or fail decision based on the criteria, we add an element of precision around that criteria to make decisions more nuanced to the testing conditions used. We also developed an R Shiny application for Measurement of Individualized, Evidence-Based Learning (MIEBL) that allows practitioners to apply this method in practice, supporting more informed instructional decisions and providing insight into the strengths and limitations of commonly used mastery criteria.
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BibTeX
@misc{ramos2026quantifying,
title = {{Quantifying Precision in Measurement of Individualized, Evidence-Based Learning Across Sessions in Applied Behavior Analysis Therapy}},
author = {Mark Louie F. Ramos and Reetam Majumder and Ji Young Kim and Jonathan W. Ivy},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16729},
eprint = {2610.16729},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16729},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantifying Precision in Measurement of Individualized, Evidence-Based Learning Across Sessions in Applied Behavior Analysis Therapy
AU - Mark Louie F. Ramos
AU - Reetam Majumder
AU - Ji Young Kim
AU - Jonathan W. Ivy
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.16729
UR - https://arxiv.org/abs/2610.16729
AB - Current performance criteria used in Applied Behavior Analysis (ABA) are often based on whether a learner meets a predetermined benchmark e.g., 8 out of 10 correct responses across several sessions. Although these criteria help practitioners make instructional decisions, they provide limited information about how likely it is that the learner has truly acquired the skill. In this paper, we introduce a Bayesian approach that combines practitioners' prior expectations with the learner's ongoing performance data to estimate the learner's future performance level. Rather than providing only a pass or fail decision based on the criteria, we add an element of precision around that criteria to make decisions more nuanced to the testing conditions used. We also developed an R Shiny application for Measurement of Individualized, Evidence-Based Learning (MIEBL) that allows practitioners to apply this method in practice, supporting more informed instructional decisions and providing insight into the strengths and limitations of commonly used mastery criteria.
ER -