Measuring Cognitive Load for Adaptive Instructional Systems by Using a Pressure Sensitive Computer Mouse

Witte, Thomas E. F.; Haase, Henrike; Schwarz, Jessica

doi:10.1007/978-3-030-77873-6_15

Cited by 6 publications

(2 citation statements)

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“…In addition, one may track learners' bodily expressions, such as gestures [71], posture [82], body/eye movement and facial expressions [80], via cameras/webcams. A recent study even made use of a pressure sensitive computer mouse to measure cognitive load [81]. This type of data has been largely used to measure learners' affective states [74], attention [68], frustration [83], or boredom [84].…”

Section: ) Physiological Datamentioning

confidence: 99%

Student Modeling and Analysis in Adaptive Instructional Systems

et al. 2022

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There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-theart review of 11 years of research (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

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Section: ) Physiological Datamentioning

confidence: 99%

Student Modeling and Analysis in Adaptive Instructional Systems

et al. 2022

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“…These theoretical assumptions on predictive smartpen measures have also been supported in studies on computer mouse interaction as a related type of goal‐driven fine motor activity performed by hand. For example, high load tasks were found to lead to a reduction of velocity of mouse cursor movements (Rheem et al, 2018) as well as to an increase in mouse click pressure (Witte et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Digital ink and differentiated subjective ratings for cognitive load measurement in middle childhood

Altmeyer

Barz

Lauer

et al. 2023

Brit J of Edu Psychol

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Background: New methods are constantly being developed to adapt cognitive load measurement to different contexts. However, research on middle childhood students' cognitive load measurement is rare. Research indicates that the three cognitive load dimensions (intrinsic, extraneous, and germane) can be measured well in adults and teenagers using differentiated subjective rating instruments. Moreover, digital ink recorded by smartpens could serve as an indicator for cognitive load in adults. Aims: With the present research, we aimed at investigating the relation between subjective cognitive load ratings, velocity and pressure measures recorded with a smartpen, and performance in standardized sketching tasks in middle childhood students. Sample: Thirty-six children (age 7-12) participated at the university's laboratory. Methods: The children performed two standardized sketching tasks, each in two versions. The induced intrinsic cognitive load or the extraneous cognitive load was varied between the versions. Digital ink was recorded while the children drew with a smartpen on real paper and after each task, they were asked to report their perceived intrinsic and extraneous cognitive load using a newly developed 5-item scale. Results: Results indicated that cognitive load ratings as well as velocity and pressure measures were substantially related to the induced cognitive load and to performance in both sketching tasks. However, cognitive load ratings and smartpen measures were not substantially related.

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