A MultiAlist Framework for Learning Quality Assessment in the Digital Media Technology Major under the Big Data Paradigm

Abstract

The rise of digital media education necessitates novel evaluation strategies that are 
adaptable to diverse cognitive systems and educational data sources. This study proposes a 
comprehensive method for teaching quality evaluation in the Digital Media Technology major, 
employing big data analytics enriched by the MultiAlist system of thought. Through integrating 
multi-dimensional educational indicators and processing them with neutrosophic logic and 
plithogenic set theory, this research develops a multi-valent evaluation framework. A case study 
at a Chinese university is conducted to demonstrate the approach, incorporating behavioral, 
academic, and engagement metrics. The results provide a transparent, scalable model for 
education stakeholders, supported by big data tools and multi-systemic logic for increased 
precision and inclusivity. 

DOI: 10.5281/zenodo.15485557