A Novel ANP-DEMATEL Framework for Multi-Criteria Decision-Making in Adaptive E-Learning Systems

E-learning systems that support personalized learning require sophisticated decision-making methods to adapt content to students optimally. This paper deals with applying multi-criteria decision-making methods in assigning learning objects in an e-learning system to students based on relevant customization criteria. The novelty of this study lies in the application of ANP and DEMATEL to improve content adaptation for students. Structuring the decision-making problem according to the DEMATEL and using ANP for prioritization has made the entire selection of learning objects better with respect to cognitive and learning styles and Bloom’s taxonomy levels. The method consists of various forms. In the first, DEMATEL has identified dependencies between criteria and clusters, mentioning their influence values on a 0–4 scale. A linear transformation model quantified the compatibility level of a student profile to a learning material. The transformed DEMATEL results were incorporated in all the interdependencies among criteria. The unweighted supermatrix was normalized by cluster weights assigned by experts before the iterative computation led to the converging weighted supermatrix. The outcome was that the individual students made these final priority rankings for learning materials. A pilot experiment was carried out to validate the system, and the results revealed that in the experimental group, the personalized learning environment showed the maximum statistical improvement over the control group. The research was conducted in Croatia, and the participants were students (N = 77) from two public universities and one polytechnic. Ultimately, the newly developed combined ANP-DEMATEL approach was effective in an instantaneous result-optimized dynamic learning path generation, ensuring knowledge acquisition. This research further contributes to developing intelligent educational systems by demonstrating how ANP and DEMATEL can be used synergistically to improve e-learning personalization. Future work could include optimizing weight assignment strategies or using new learning contexts to further adaptivity.