Enhancing Competency-Based Learning with Neutrosophic Regression  and the Deming Cycle

Dr Hortencio Flores Flores; Dr. Irma Reyes Blácido; Dr. Magda Patricia Ramos Cevallos; Dr. Nemesia Hidalgo Penadillo; Mgtr. Luz Magali Villarroel Nuñez

Authors

Dr Hortencio Flores Flores Universidad Nacional de Educación Enrique Guzmán y Valle, La Cantuta, Chosica, Lima, Perú
Dr. Irma Reyes Blácido Universidad Nacional de Educación Enrique Guzmán y Valle, La Cantuta, Chosica, Lima, Perú
Dr. Magda Patricia Ramos Cevallos Universidad Nacional de Educación Enrique Guzmán y Valle, La Cantuta, Chosica, Lima, Perú
Dr. Nemesia Hidalgo Penadillo Universidad Nacional de Educación Enrique Guzmán y Valle, La Cantuta, Chosica, Lima, Perú
Mgtr. Luz Magali Villarroel Nuñez Universidad Nacional de Educación Enrique Guzmán y Valle, La Cantuta, Chosica, Lima, Perú

Keywords:

Competency-based learning, Deming Cycle, neutrosophic numbers, regression analysis, uncertainty, educational data, machine learning, continuous improvement

Abstract

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 re
gression 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 predic
tions, and ultimately fosters continuous improvement in competency-based education.

DOI: 10.5281/zenodo.14759367