Towards Learner-Centered Educational Technologies: Analyzing the Alignment of Teaching and Learning Styles in Philippine High Schools

Abstract

As educational technologies become increasingly integral to Philippine high schools, aligning digital interfaces with users' cognitive preferences remains a critical challenge. This study models the instructional alignment between students’ learning styles and teachers’ teaching styles to provide empirical data architecture for designing Adaptive Learning Systems (ALS). Utilizing a quantitative correlational-predictive design, data from 319 students and 77 teachers in Surigao del Sur were computationally analyzed using the Grasha-Riechmann frameworks. Findings revealed that students predominantly preferred a collaborative learning style, while teachers primarily adopted a facilitative instructional approach. Predictive modeling indicated that students' learning preferences did not significantly differ by sex or school type, offering a streamlined, demographic-agnostic baseline for machine learning algorithms. Conversely, teachers' educational background significantly predicted their instructional deployment. By quantitatively clustering these behavioral profiles into distinct instructional models, this research translates pedagogical constructs into computational parameters. The resulting data blueprints enable software engineers to develop AI-integrated platforms and collaborative digital workspaces that dynamically adapt to users' specific configurations, thereby optimizing human-computer interaction and system efficiency in modern digital education.