Modeling Students’ Cognitive Load Profiles as a Diagnostic Framework for Adaptive Mathematics Instruction in Higher Education

Abstrak

This study addresses the growing need for cognitively responsive adaptive learning in higher education mathematics by examining students’ cognitive load profiles as a diagnostic foundation for instructional design. While previous research has primarily treated cognitive load as an outcome variable, limited attention has been given to its configuration at the individual level. This study aims to model students’ cognitive load profiles and explore their implications for adaptive mathematics instruction. A quantitative exploratory design was employed involving 180 undergraduate students selected through probability sampling. Data were collected using a validated Cognitive Load Questionnaire measuring intrinsic, extraneous, and germane cognitive load. A person-centered profiling approach was applied to identify distinct cognitive load configurations. The results revealed three profiles: overload (34.4%), unproductive load (27.2%), and optimal load (38.4%). High intrinsic and extraneous load combined with moderate germane load indicates suboptimal cognitive resource allocation. This study contributes theoretically by positioning cognitive load profiles as a diagnostic framework for adaptive learning design, and practically by informing cognitively responsive instructional strategies in mathematics education.