A Cognitive Load Theory Approach to Defining and Measuring Task Complexity Through Element Interactivity

Abstract

Educational researchers have been confronted with a multitude of definitions of task complexity and a lack of consensus on how to measure it. Using a cognitive load theory-based perspective, we argue that the task complexity that learners experience is based on element interactivity. Element interactivity can be determined by simultaneously considering the structure of the information being processed and the knowledge held in long-term memory of the person processing the information. Although the structure of information in a learning task can easily be quantified by counting the number of interacting information elements, knowledge held in long-term memory can only be estimated using teacher judgment or knowledge tests. In this paper, we describe the different perspectives on task complexity and present some concrete examples from cognitive load research on how to estimate the levels of element interactivity determining intrinsic and extraneous cognitive load. The theoretical and practical implications of the cognitive load perspective of task complexity for instructional design are discussed.

Direct effects of cognitive load on self-efficacy during instruction.

“…However, content changed over time in the course, which would lead intrinsic load to vary from lesson to lesson based on varying levels of element interactivity within the material (O. Chen et al, 2023). While the course was generally structured to offer a logical progression of content, that progression would not necessarily lead to a stable trend in intrinsic load over time.…”

“…To the extent that the measure used in Study 2 reliably differentiates between cognitive load types, it may suggest the importance of more refined analysis for both prior knowledge and new content when evaluating models with intrinsic load (O. Chen et al, 2023) and evolving utility of instructional features that may become extraneous as participants develop a greater understanding of content (i.e., expertise reversal effect; Kalyuga, 2007). It also may reflect the challenge of measuring cognitive load given its localized nature in cognitive processing (Kalyuga & Plass, 2018).…”

“…To the extent that instructional features were fairly consistent in the asynchronous delivery of the course, they were likely to manifest in trait-like ways. However, content changed over time in the course, which would lead intrinsic load to vary from lesson to lesson based on varying levels of element interactivity within the material (O. Chen et al, 2023).…”

“…Additionally, the discrepancy between the state and trait aspects of the different measures of cognitive load may suggest that these different measures function differently over time. To the extent that the measure used in Study 2 reliably differentiates between cognitive load types, it may suggest the importance of more refined analysis for both prior knowledge and new content when evaluating models with intrinsic load (O. Chen et al, 2023) and evolving utility of instructional features that may become extraneous as participants develop a greater understanding of content (i.e., expertise reversal effect; Kalyuga, 2007).…”

Offloading Through Emphasis Manipulation Sequencing During a Complex Learning Process in Cognitive Load and Learning Transfer

“…To prevent this, learners should be empowered to utilize challenging information retrieved from available cognitive cues themselves. Second, offloading methods can holistically support more complex learning (e.g., Chen et al, 2023). In other words, offloading can reduce the cognitive load of complex strategies and concepts, allowing a learning process that requires higher elemental interactions.…”