Decision Support Framework for Teachers Using Web-Based Instructional Strategies in Blended English Courses

Abstract

Web-based and blended learning environments present a challenge to teachers in choosing appropriate instruction strategies to address the needs of various learners. This paper presents the Decision Support Framework of Web-Based Instruction (DSF-WBI) to support teachers in blended English courses by enabling them to make intelligent and interpretable decisions. DSF-WBI combines fuzzy reasoning dedicated to pedagogical transparency with machine learning trained for predictive accuracy and tested on four datasets representing learner engagement, sequence-level language practice, writing proficiency, and analytic evidence of English learner proficiency. The framework had an average accuracy of 91.9, cross-domain strength (less than 4% reduction in performance), and a 95% adoption rate among teachers. DSF-WBI is an effective tool that combines interpretability and performance, enabling educators to make transparent and data-informed instructional choices that support explainable AI applications in web learning.