Project-based learning as a space for validating Neutrosophic Plithogenic Hypotheses.

Main Article Content

Mirella C. Ortiz-Zambrano
Adriana María Castro Maridueña
Luz Marina Bejarano Ospina
Carlos Feliciano Vivas Lucas

Abstract

This study addresses a crucial challenge in modern education: how to validate neutrosophic plithogenic hypotheses through project-based learning (PBL). This approach is relevant given the growing interest in educational methodologies that integrate uncertainty and a diversity of perspectives to foster meaningful learning. Although existing literature extensively explores PBL, it lacks approaches that consider the inherent indeterminacy of plithogenic hypotheses, which encompass multiple degrees of truth, falsity, and indeterminacy. To overcome this limitation, this study employs a methodological framework that combines PBL with neutrosophic tools, using qualitative and quantitative analysis to evaluate student-designed projects. The results reveal that PBL allows for the validation of plithogenic hypotheses by providing a dynamic environment where students explore solutions under uncertainty, generating more robust and contextualized knowledge. This work contributes to the field of education by introducing an innovative model that enriches neutrosophic theory and offers practical applications for designing curricula that promote critical thinking and complex problem-solving. Furthermore, the findings suggest that PBL can be adapted to diverse educational contexts, strengthening students' ability to confront ambiguous challenges with creative and structured approaches.

Downloads

Download data is not yet available.

Article Details

How to Cite
Project-based learning as a space for validating Neutrosophic Plithogenic Hypotheses. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 39, 271-282. https://fs.unm.edu/NCML2/index.php/112/article/view/855
Section
Articles

How to Cite

Project-based learning as a space for validating Neutrosophic Plithogenic Hypotheses. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 39, 271-282. https://fs.unm.edu/NCML2/index.php/112/article/view/855