Neutrosophic multicriteria method for the evaluation of law student's reflective and ethical abilities induced by a tutor

Main Article Content

Ned Vito Quevedo Arnaiz
Nemis García Arias
Cristian Fernando Benavides Salazar

Abstract

At the university, professional training is integrated with research, ethical and civic education, contributing to the student's reflection and educational development. However, the study load often causes students not to reflect to the extent they should. This research aimed to implement a multi-criteria neutrosophic method for the evaluation of the reflective and ethical capacities of law students, induced by a tutor. The aspects of the comprehensive training of the student in their development as a jurist are analyzed, focusing on their capacity for ethical and reflective assessment in the face of the influences exerted by the tutor during student research. This approach allowed collecting information on the ethical characteristics of teachers, which facilitated an in-depth analysis of their behavior and its impact on student research. The results obtained revealed how the tutor's behavior can influence the creation of reflective and ethical capacities in students, which is essential for their comprehensive professional training. Thus, the implementation of the proposed method not only seeks to facilitate the educational process, but also to strengthen the ethical and reflective development of future lawyers, ensuring their preparation to face the challenges of professional practice.

Downloads

Download data is not yet available.

Article Details

How to Cite
Neutrosophic multicriteria method for the evaluation of law student’s reflective and ethical abilities induced by a tutor. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 36, 157-166. http://fs.unm.edu/NCML2/index.php/112/article/view/677
Section
Articles

How to Cite

Neutrosophic multicriteria method for the evaluation of law student’s reflective and ethical abilities induced by a tutor. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 36, 157-166. http://fs.unm.edu/NCML2/index.php/112/article/view/677