Using eye movements to measure intrinsic, extraneous, and germane load in a multimedia learning environment.

Abstract

In a previous study, DeLeeuw and Mayer (2008) found support for the triarchic model of cognitive load (Sweller, Van Merriënboer, & Paas, 1998, 2019) by showing that three different metrics could be used to independently measure 3 hypothesized types of cognitive load: intrinsic, extraneous, and germane. However, 2 of the 3 metrics that the authors used were intrusive in nature because learning had to be stopped momentarily to complete the measures. The current study extends the design of DeLeeuw and Mayer (2008) by investigating whether learners’ eye movement behavior can be used to measure the three proposed types of cognitive load without interrupting learning. During a 1-hr experiment, we presented a multimedia lesson explaining the mechanism of electric motors to participants who had low prior knowledge of this topic. First, we replicated the main results of DeLeeuw and Mayer (2008), providing further support for the triarchic structure of cognitive load. Second, we identified eye movement measures that differentiated the three types of cognitive load. These findings were independent of participants’ working memory capacity. Together, these results provide further evidence for the triarchic nature of cognitive load (Sweller et al., 1998, 2019), and are a first step toward online measures of cognitive load that could potentially be implemented into computer assisted learning technologies.

How the poor get richer: Signaling guides attention and fosters learning from text‐graph combinations for students with low, but not high prior knowledge

“…Moreover, we are aware that pupil diameter is a rather innovative measure of cognitive load that just recently gained more and more attention in the field of instructional research (e.g., Authors, 2019; Scharinger et al, 2015; Zu et al, 2019). Especially the results obtained by Zu et al (2019) are promising in that they showed that the ratio of pupil size change was sensitive to extraneous cognitive load, that is related to the design of learning materials (e.g., element interactivity). However, in our study the measure did not add to explain potential underlying mechanisms of the expertise reversal effect.…”

“…Second, based on the cognitive load explanation of the expertise reversal effect, we examined how signals affect pupil diameter and time‐on‐task as objective indicators of cognitive load (Kalyuga & Plass, 2018; Paas et al, 2003). Recent research showed that the pupil diameter is suited as a measure for cognitive load in that it increases with increasing processing demands (e.g., for multimedia learning, Authors, 2019; Zu et al, 2019; for text reading, Scharinger et al, 2015). Similarly, time‐on‐task reflects the cognitive resources that people invests into accomplishing a task, that is, their effort (e.g., Koriat et al, 2014).…”

Analyzing Relationships Between Causal and Assessment Factors of Cognitive Load: Associations Between Objective and Subjective Measures of Cognitive Load, Stress, Interest, and Self-Concept

“…Besides the measurement of cognitive load via self-reports on questionnaires (e.g., Paas, 1992;Leppink et al, 2013;Krell, 2017), other approaches suggest or use more objective, physiological measures as indicators for respondents' cognitive load (cf. Sweller et al, 2011); such as eye movements (Ikehara and Crosby, 2005;Zu et al, 2019), degree of pupil dilation (Huh et al, 2019), or physiological stress parameters such as heart rate and cortisol secretion (Veltman and Gaillard, 1993;Kennedy and Scholey, 2000;Cranford et al, 2014). Previous research has attempted to triangulate both objective and subjective cognitive load measures (Kahneman and Peavler., 1969;Antonenko et al, 2010;Zu et al, 2019).…”

“…Sweller et al, 2011); such as eye movements (Ikehara and Crosby, 2005;Zu et al, 2019), degree of pupil dilation (Huh et al, 2019), or physiological stress parameters such as heart rate and cortisol secretion (Veltman and Gaillard, 1993;Kennedy and Scholey, 2000;Cranford et al, 2014). Previous research has attempted to triangulate both objective and subjective cognitive load measures (Kahneman and Peavler., 1969;Antonenko et al, 2010;Zu et al, 2019). Physiological measures have also been proposed as sources for validity evidence for self-reports, if positive correlations between both measures can be shown (e.g., Solhjoo et al, 2019).…”

Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?

“…Pupil size can be assessed with most commonly available video-based eye trackers. Starting with the seminal paper by Hess and Polt (1964), it has been shown repeatedly that the pupil dilates with increases in cognitive processing demands (e.g., Richter and Scheiter 2019;Scharinger et al 2015;Szulewski et al 2017;Zu et al 2019).…”