MIEBL: Measurement of Individualized, Evidence-Based Learning Criteria Designed for Discrete Trial Training

TL;DR

This paper introduces a new tool for selecting performance criteria in discrete trial training based on individualized assessments and probability theory.

Contribution

The paper introduces MIEBL, a novel framework grounded in probability theory for individualized performance criterion selection in discrete trial training.

Findings

MIEBL provides consistent results aligned with existing research outcomes.

The tool helps practitioners evaluate and adjust teaching strategies based on assessment data.

Researchers can use MIEBL to reduce bias in performance assessments and improve decision-making.

Abstract

Informing the selection of a performance criterion for discrete trial training has been the subject of a growing body of empirical research, but an explicit framework has not yet been established. This paper proposes a tool for selecting a performance criterion that uses individualized assessment characteristics and mastery level goals and is grounded on sound probability theory. This tool is demonstrated to provide results that are consistent with existing research outcomes, and its use is advocated to better inform practitioners and researchers on the implications of their performance assessment choices. A tutorial with ready-to-use software is provided. Practitioners can use the tool to evaluate their assessment strategies and outcome expectations. Practitioners can use the tool to help make judgments about whether to continue with a teaching strategy or switch to another one. Researchers can use the tool to account for bias between observed performance and actual mastery level at the end of instruction, which is a confounder to observed performance during maintenance or generalization. Researchers and practitioners can use the tool to make better-informed decisions.

The online version contains supplementary material available at 10.1007/s40617-025-01058-9.

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