Multidimensional Validation of Belonging-Supportive Deep Learning Environments in Higher Education Using CFA and Item Response Theory
DOI:
https://doi.org/10.55927/jeda.v5i2.28Keywords:
Deep Learning, Higher Education, CFA, IRT, Psychometric Validation.Abstract
The development of digital learning in higher education requires the creation of a learning environment that not only focuses on academic achievement, but also supports students' sense of belonging and psychosocial support. This study aims to test the multidimensional validity and psychometric characteristics of belonging-supportive deep learning environments instruments using Confirmatory Factor Analysis (CFA) and Item Response Theory (IRT). The study used a quantitative approach with a cross-sectional survey design of 412 students from several universities in Indonesia. Data were collected through a Likert scale questionnaire and analyzed using CFA and IRT. The results of the study show that the multidimensional model has good construct validity and reliability. IRT analysis also showed that most items had high discriminating power and were able to accurately measure variations in sense of belonging and learning support. This research contributes to the development of psychometric instruments for higher education and supports the design of a more inclusive and supportive digital learning environment.
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