Differentiating Different Types of Cognitive Load: a Comparison of Different Measures

Korbach, Andreas; Brünken, Roland; Park, Babette

doi:10.1007/s10648-017-9404-8

Cited by 98 publications

(71 citation statements)

References 64 publications

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“…During this period of time, the acquisition of information from the currently viewed point in a stimulus (i.e., process model) takes place. Hence, the analysis of the average fixation duration allowed for additional assumption regarding the cognitive load during the comprehension of process models [60]. Based on the defined measures, Fig.…”

Section: Study I and Study Iimentioning

confidence: 99%

Measuring the Cognitive Complexity in the Comprehension of Modular Process Models

Winter

Pryss

Probst

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Modularization in process models is a method to cope with the inherent complexity in such models (e.g., model size reduction). Modularization is capable to increase the quality, the ease of reuse, and the scalability of process models. Prior conducted research studied the effects of modular process models to enhance their comprehension. However, the effects of modularization on cognitive factors during process model comprehension are less understood so far. Therefore, this paper presents the results of two exploratory studies (i.e., a survey research study with N = 95 participants; a follow-up eye tracking study with N = 19 participants), in which three types of modularization (i.e., horizontal, vertical, orthogonal) were applied to process models expressed in terms of the Business Process Model and Notation (BPMN) 2.0. Further, the effects of modularization on the cognitive load, the level of acceptability, and the performance in process model comprehension were investigated. In general, the results revealed that participants were confronted with challenges during the comprehension of modularized process models. Further, performance in the comprehension of modularized process models showed only a few significant differences, however, the results obtained regarding the cognitive load revealed that the complexity and concept of modularization in process models were misjudged initially. The insights unraveled that the attitude towards the application and the behavioral intention to apply modularization in process model is still not clear. In this context, horizontal modularization appeared to be the best comprehensible modularization approach leading to a more finegrained comprehension of respective process models. The findings indicate that alterations in modular process models (e.g., change in the representation) are important to foster and enable their comprehension. Finally, based on our results, implications for research and practice as well as directions for future work are discussed in this paper.

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Section: Study I and Study Iimentioning

confidence: 99%

Measuring the Cognitive Complexity in the Comprehension of Modular Process Models

Winter

Pryss

Probst

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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“…We also relied on subjective measures for assessing cognitive load. Strategies to enhance the understanding of cognitive load management could involve updated models of cognitive load (Kalyuga, 2011) and should seek to assess extraneous load individually with more objective measures, for example by relying psychophysiological measures or eye-tracking (Conrad & Bliemel, 2016;Korbach, Brünken, & Park, 2018), which would also contribute to a better understanding of cognitive load through process data. Considering the survey instrument, we also add to the limitations that we had to remove three items of the initial instrument related to the constructs of germane load and satisfaction with the learning process.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Ladders for Learning: Is Scaffolding the Key to Teaching Problem-Solving in Technology-Mediated Learning Contexts?

Janson

Söllner

Leimeister

2020

AMLE

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The research presented in this paper was partially funded by the German Federal Ministry of Education and Research in the context of the project KoLeArn (www.KoLeArn.de), Grant No. 01BE17008A. The authors are responsible for the content of this publication. We express our gratitude to the students of the University of Kassel who took part in this study. We would also like to thank Marian Thiel de Gafenco for his work and ideas in the early phases of this research project. Furthermore, this research builds on a paper that has been presented at the Academy of Management Annual Meeting 2017 in Atlanta (Janson & Söllner, 2017). We thank the reviewers and attendees as well as the mentors of the Management Education and Learning Writers Workshop for their valuable feedback that helped us to improve our research and to write this paper. Last but not least, we thank the Associate Editor for his guidance as well as the three anonymous reviewers for their constructive feedback and openness during the review process.

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“…In general, there is an agreed dimension classifying cognitive load measurement methods: subjective and objective measures (Brünken, Plass, & Leutner, ). Subjective measures, which are based on one's introspection, have been used widely because of their easiness of use (Korbach, Brünken, & Park, ). They include self‐rating scales such as a mental effort rating scale (Paas, ) and the NASA Task Load Index (Hart & Staveland, ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…They include the three types: (a) outcome measures, usually based on task performance, (b) behavioural measures based on their correlation with learning processes, and (c) physiological measures. Physiological measures have an edge because they track cognitive load continuously and unobtrusively and to detect subtle changes in cognitive load (Korbach et al, ).…”

Section: Literature Reviewmentioning

confidence: 99%

A novel method to monitoring changes in cognitive load in video‐based learning

Huh

Kim

2019

Computer Assisted Learning

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One of the golden rules in instructional design methods is to optimize the use of working memory capacity and avoid cognitive overload. The study of cognitive load has historically relied on one's introspection. However, it is difficult to capture changes in cognitive load levels during learning sensitively. This paper suggests an approach to investigating dynamic changes in cognitive load by using a pupillometry. With the method, this study explores the effects of learners' prior knowledge and task complexity on cognitive load. An experiment was conducted on two groups of students (N = 19) with distinct levels of prior knowledge. In the experimental session, participants watched a video lecture on a mathematics proposition, while being eye‐tracked. The lecture consists of sections, which can be either a high task complexity or a low task complexity based on elements they have. Pupil dilations acquired in each section were used to explore the time course of cognitive load. To formulate cognitive load patterns, a time‐series clustering was used. The research conducted a chi‐square analysis to test differences in cognitive load patterns by prior knowledge and task complexity. Results show that pupil dilation patterns can be applied to monitor changes in cognitive load during learning.

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Differentiating Different Types of Cognitive Load: a Comparison of Different Measures

Cited by 98 publications

References 64 publications

Measuring the Cognitive Complexity in the Comprehension of Modular Process Models

Measuring the Cognitive Complexity in the Comprehension of Modular Process Models

Ladders for Learning: Is Scaffolding the Key to Teaching Problem-Solving in Technology-Mediated Learning Contexts?

A novel method to monitoring changes in cognitive load in video‐based learning

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