Evaluation of Practice-Based Curriculum Objectives  Achievement Degree Using Einstein Aggregation Operators of  Single-Valued Neutrosophic Credibility Numbers

Tong Zhang; Xueping Lu; Kun Chen; Chunping Liu; Jun Ye

Authors

Tong Zhang School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China
Xueping Lu School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China
Kun Chen School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China
Chunping Liu School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, China
Jun Ye School of Civil and Environmental Engineering, Ningbo University, Ningbo, China

Keywords:

single-valued neutrosophic credibility number; weighted averaging operator of Einstein; weighted geometric operator of Einstein; curriculum objectives achievement evaluation

Abstract

Currently, student-centered and outcome-based engineering education accreditation is
being actively promoted in universities across China. Evaluation of the curriculum objectives
achievement degree serves as an effective method after OBE-based courses teaching to assess the
improvement of students' abilities, evaluate education quality, and facilitate self-reflection of
teaching by instructors. The process of evaluating curriculum objectives involves a complex
multiple-attribute decision-making (MADM) scenario, often accompanied by elements of
vagueness, uncertainty, and inconsistency. The application of single-valued neutrosophic
credibility numbers sets (SvNCNs) offers a robust approach to handle and represent uncertain
information throughout this evaluation process. Therefore, to enhance the accuracy of course
achievement evaluation, this paper proposes a MADM framework based on SvNCNs, integrated
with improved Einstein aggregation operators, to the achievement degree of practice-based
curriculum objectives evaluation. The method is applied to the practice-based curriculum and is
further compared and analyzed with other classical methods to show the efficiency of the
proposed method, which will assist decision-makers in making better decisions when dealing with
similar MADM assessment problems.

DOI: 10.5281/zenodo.15467845