Data Analytics–Based Evaluation of Student Perceptions of Learning Quality in Islamic Higher Education
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Basrul Abdul Majid
Husni Husni
Alim Misbullah
Muhammad Aizal Hanafi Bin Fazli Hisam
This study employs an integrated data analytics approach to examine student perceptions of learning quality at the Faculty of Islamic Economics and Business (FEBI), IAIN Lhokseumawe. The analytical framework combines descriptive statistics, K-Means clustering, and multiple linear regression to provide a comprehensive evaluation of instructional quality. Data were collected from 805 active undergraduate students using a structured questionnaire measuring nine learning quality indicators related to instructional delivery, lecturer competence, communication, assessment practices, and institutional compliance. The findings indicate that students generally perceive the quality of learning positively, with an overall mean score of 4.32 on a five-point Likert scale. Lecturer’s Mastery of Material and Accuracy in Answering Questions emerged as the highest-rated indicators, while Material Suitability with the Semester Learning Plan (RPS) and Transparency of Assessment Criteria received comparatively lower scores. Regression results show that instructional quality indicators significantly influence overall student satisfaction, with assessment transparency as the strongest predictor and material suitability with the RPS as the weakest. These results highlight the importance of transparent assessment practices and consistent alignment between instructional materials and the RPS in shaping students’ learning experiences, enhancing student trust, reducing uncertainty, and sustaining educational quality in Islamic higher education.
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