Validation of an Adaptive Decision Support System Framework for Outcome-Based Blended Learning
Rahimah Abd Halim, Rosmayati Mohemad, Noraida Haji Ali, Anuar Abu Bakar, Hamimah Binti Ujir · International Journal of Advanced Computer Science and Applications · 2025
The Adaptive Decision Support System Learning Framework (A-DSS-LF) was developed to address diverse learner needs in blended learning environments by integrating learning styles, cognitive levels, practical skills, and value practices. This study validates the framework using the Fuzzy Delphi Method (FDM), a consensus-building tool that synthesizes expert opinions and addresses uncertainties in subjective judgments. A panel of 15 experts evaluated the framework’s constructs: Learning Process, Learning Assessment, Decision Support System, and Adaptive Learning Profile. All constructs met the FDM’s consensus criterion, achieving threshold values between 0.087 and 0.118 (≤0.2), indicating high consistency and low variability. The defuzzification process confirmed values exceeding 0.5, with scores ranging from 0.873 to 0.922 and expert agreement surpassing 75 percent for all elements. These findings confirm the robustness and applicability of the A-DSS-LF, validating its role in enhancing personalized learning outcomes and supporting teachers in tailoring adaptive learning resources. The framework is scalable and can be implemented in secondary school computer science education and online learning platforms to create personalized learning paths, improve engagement, and bridge the gap between online and offline learning. This study reinforces the significance of expert validation in adaptive learning frameworks, ensuring their scalability and adaptability for future applications in diverse educational settings.