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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