This article introduces a novel approach to enhance competency-based learning by combining the Deming Cycle with neutrosophic statistics. Competency-based education focuses on practical skills, but uncertainty in student performance and assessment can hinder its effectiveness. Neutrosophic statistics, unlike traditional methods, explicitly models indeterminacy, providing a more complete picture of uncertainty in educational data. This approach integrates neutrosophic numbers into regression analysis to predict learning outcomes and quantify the confidence level of those predictions. These predictions, with their associated indeterminacy, then inform the Deming Cycle (Plan-Do-Check-Act), enabling educators to dynamically adjust teaching strategies based on data-driven in-sights. This leads to more informed decision-making, improved accuracy and reliability in predictions, and ultimately fosters continuous improvement in competency-based education. © 2025, University of New Mexico. All rights reserved.
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