Evaluating the Effectiveness of Blended Learning Models Using a Weighting and Scoring–Based Decision Support System

Abstract

The increasing adoption of blended learning in higher education has created a need for systematic and objective evaluation frameworks capable of capturing its multidimensional nature. Existing evaluation approaches often rely on fragmented or subjective measures, limiting their usefulness for strategic decision-making. This study aims to develop and apply a weighting and scoring–based Decision Support System (DSS) to evaluate and rank alternative blended learning models based on multiple criteria. A quantitative multi-criteria decision-making (MCDM) approach was employed. Five evaluation criteria—Instructional Design Quality, Technology Usability, Student Engagement, Learning Flexibility, and Learning Outcome Achievement—were identified through literature review and validated using a two-round Delphi process involving seven experts. Each blended learning model was assessed using a structured Likert-scale scoring rubric, and overall performance scores were calculated through weighted aggregation. The findings indicate that the fully interactive LMS-supported blended learning model achieved the highest overall score, followed by flipped classroom and project-based models, while lecture-dominant blended learning ranked lowest. The results highlight the critical role of technological integration and active learning strategies in enhancing blended learning effectiveness. The proposed DSS offers a transparent and replicable framework for evaluating blended learning models and supporting evidence-based decision-making in higher education. However, the study is limited by its reliance on expert judgment and lack of large-scale empirical validation. Future research should incorporate advanced MCDM techniques and real-world learning data to improve robustness and generalizability.