2026 ASEE Annual Conference & Exposition

Cognitive Load Theory (CLT) has become a foundational framework for understanding how instructional design affects student learning, particularly in cognitively demanding fields such as engineering. Among the most widely used self-report measures for cognitive load is the instrument developed by Leppink and colleagues in 2013, which differentiates between intrinsic, extraneous, and germane load. However, subsequent research by the developers has called into question the validity of the germane load subscale, suggesting that it may not accurately represent a distinct construct within CLT.

This work-in-progress paper builds on the evolving understanding of this instrument by asking the following research question: Can the measurement of cognitive load be refined to capture peripheral influences on student learning that fall outside the traditional intrinsic and extraneous categories? To address this question, this paper refines and expands Leppink et al.’s instrument by maintaining the validated intrinsic and extraneous load scales while developing new items to assess peripheral cognitive load, which is conceptualized as a subcomponent of extraneous load that reflects the cognitive burden imposed by factors unrelated to inherent difficulty of the subject or its instructional design, such as external responsibilities, physical and mental health, or time constraints. These influences, though external to the learning task itself, still detract from the cognitive resources available for processing instructional material and thus directly impact learning outcomes. Failing to account for these peripheral factors may cause existing cognitive load measures to underestimate the total mental demand experienced by students and may limit inferential capability, particularly in high-stakes or cognitively intensive learning environments.

This paper will present preliminary findings from an ongoing instrument development study situated in undergraduate engineering education that combines qualitative feedback and quantitative analyses, including reliability, factor structure testing, and regression. This mixed-methods design evaluates the internal consistency, conceptual clarity, and predictive capability of the expanded measure. By integrating quantitative and qualitative validation approaches, this study aims to clarify the measurement of cognitive load components in complex learning contexts. The expected outcome is an empirically supported and theoretically coherent instrument capable of distinguishing between different dimensions of cognitive load, including peripheral aspects that may play a critical role in student engagement and mental effort during engineering instruction. The refined measure will equip researchers and instructors with a more precise tool for identifying cognitive barriers in STEM learning and for designing instruction that optimizes student comprehension and promotes more effective learning.