Multimodal learning analytics to investigate cognitive load during online problem solving. (22nd May 2020)

• Record Type: Journal Article
• Authors: Larmuseau, Charlotte; Cornelis, Jan; Lancieri, Luigi; Desmet, Piet; Depaepe, Fien
• Is Part Of: British journal of educational technology. Volume 51:Number 5(2020:Sep.)
• Page Start: 1548
• Page End: 1562
• Publication Date: 2020-05-22
• Subjects: cognition -- distance learning -- learning analytics -- machine learning -- problem solving -- sensor technologies -- wearables; Audio-visual education -- Periodicals; 371.33
• DOI: 10.1111/bjet.12958
• Languages: English
• ISSNs: 0007-1013

Abstract

Abstract: To have insight into cognitive load (CL) during online complex problem solving, this study aimed at measuring CL through physiological data. This study experimentally manipulated intrinsic and extraneous load of exercises in the domain of statistics, resulting in four conditions: high complex with hints, low complex with hints, high complex without hints and low complex without hints. The study had a within‐subject‐design in which 67 students solved the exercises in a randomized order. Self‐reported CL was combined with physiological data, namely, galvanic skin response (GSR), skin temperature (ST), heart rate (HR) and heart rate variability (HRV). Multiple imputation was used for handling missing data from resp. 16 and 19 students for GSR/ST and HR/HRV. First, differences between conditions in view of physiological data were examined. Second, we investigated how much variance of self‐reported CL and task performance was explained by physiological data. Finally, we investigated which features can be used to assess (objective) CL. Results revealed no significant differences between the manipulated conditions in terms of physiological data. Nonetheless, HR and ST were significantly related to self‐reported CL, whereas ST to task performance. Additionally, this study revealed the potential of ST and HR to assess high CL.