A MULTI-CRITERIA DECISION ANALYSIS (MCDA)-BASED EVALUATION MODEL FOR PERSONALIZED LEARNING PLATFORMS IN HIGHER EDUCATION

In recent years, the rapid integration of artificial intelligence and adaptive technologies into higher education has accelerated the shift toward personalized learning. However, despite the proliferation of platforms such as ALEKS, Knewton Alta, Smart Sparrow, Realizeit, and Coursera, there remains a lack of comprehensive evaluation models that can systematically compare their pedagogical, technical, and usability effectiveness. This study proposes a Multi-Criteria Decision Analysis (MCDA)-based evaluation model to assess the overall performance of personalized learning platforms across ten key criteria, including adaptive algorithms, learning analytics, user interactivity, instructor involvement, technical integration, data security, and cost-efficiency. The research adopts a mixed-method approach, combining qualitative content analysis and quantitative scoring based on a five-point Likert scale. The collected data were analyzed through weighted aggregation to calculate the integrated efficiency index (Ip) for each platform. Findings reveal that Realizeit achieved the highest overall score (4.6/5), demonstrating strong AI-driven adaptability and superior LMS integration. ALEKS ranked second (4.1/5) due to its effective gap-analysis algorithm, while Coursera showed the lowest adaptability index (3.6/5), mainly due to limited personalization depth. The proposed MCDA-based model provides a systematic and replicable framework for decision-makers in higher education institutions to select, implement, and evaluate digital learning platforms effectively.