Combining physiological data and subjective measurements to investigate cognitive load during complex learning

Abstract

Cognitive load theory is one of the most influential theoretical explanations of cognitive processing during learning. Despite its success, attempts to assess cognitive load during learning have proven difficult. Therefore, in the current study, students' self-reported cognitive load after the problem-solving process has been combined with measures of physiological data, namely, electrodermal activity (EDA) and skin temperature (ST) during the problem-solving process. Data was collected from 15 students during a high and low complex task about learning and teaching geometry. This study first investigated the differences between subjective and physiological data during the problem-solving process of a high and low complex task. Additionally, correlations between subjective and physiological data were examined. Finally, learning behavior that is retrieved from log-data, was related with EDA. Results reveal that the manipulation of task complexity was not reflected by physiological data. Nevertheless, when investigating individual differences, EDA seems to be related to mental effort.

Multimodal learning analytics to investigate cognitive load during online problem solving

“…By contrast, Shi, Ruiz, Taib, Choi, and Chen (2007) investigated 11 subjects when dealing with four tasks that differed in level of difficulty, but the results revealed insignificant differences across task difficulty levels for GSR. Similarly Larmuseau et al (2019) investigated GSR for tasks that differed in terms of element interactivity. Results of 15 participants indicated no noticeable difference in GSR data between a high and low element interactivity task.…”

“…In view of optimizing CL, it is important to accurately measure CL during the online problem-solving process. Former studies used physiological measurements such as GSR, ST, HR and HRV to investigate CL (Cranford, Tiettmeyer, Chuprinko, Jordan, & Grove, 2014;Haapalainen, Kim, Forlizzi, & Dey, 2010;Larmuseau, Vanneste, Cornelis, Desmet, & Depaepe, 2019;Nourbakhsh, Wang, Chen, & Calvo, 2012). Despite the merits of these studies our current understanding of the association between physiological data and CL is characterized by at least three limitations.…”

“…First, most studies did not systematically manipulate CL based on insights from CLT (Morton et al, 2019). Second, the majority of studies did not combine physiological data with self-reports and task performance (Dindar, Malmberg, Järvelä, Haataja, & Kirschner, 2019;Larmuseau et al, 2019). Third, there is no unambiguous answer to which physiological features are best in assessing CL (Larmuseau et al, 2019).…”

What does physiological synchrony reveal about metacognitive experiences and group performance?

“…For example, electrodermal activity (EDA) sensors can be easily implemented in learning settings. EDA is a measure of the autonomic nervous system that can be related to individuals’ various cognitive and affective states, such as feelings of familiarity (Morris, Cleary, & Still, 2008), monitoring and error detection (Hajcak, McDonald, & Simons, 2003), task difficulty (Pecchinenda, 1996), motivation (Kreibig, Gendolla, & Scherer, 2012) and mental effort (Larmuseau et al ., 2019). EDA signals are situated, and they fluctuate in relation to the momentary changes or challenges in a context.…”

The effects of video lecture viewing strategies on cognitive load

“…Mental effort also plays a role in extraneous load (Larmuseau et al 2019), which brings Leppink et al's (2013) conceptualization of extraneous load into question due to the fact that their extraneous load items are heavily reflective of poor instruction regardless of effort. Other research has addressed this issue by focusing on mental effort calculations rather than instructional explanations alone (Kalyuga and Sweller 2005;Paas 1992;Rikers et al 2004;van Merriënboer and Sweller 2005).…”