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Exploiting Wearable Technologies to Measure and Predict Students’ Effort

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Perspectives on Wearable Enhanced Learning (WELL)

Abstract

Effort is considered a key factor of students’ success and its influences on learning outcomes have been studied for decades. To study this relationship, researchers have been measuring it in several different ways. One traditional way to measure effort is to rely on indicators such as the time spent on a task. This solution is not entirely reliable, as divergent results can be found in the literature. Additionally, it is not possible to know the internal and external conditions that led to these observations and how they can influence the results. Being able to accurately measure and predict students’ effort can contribute to the understanding of its relationship with learning outcomes, and allow teachers to identify students who are struggling or not truly engaged into learning trough new tools. One promising way to acquire information about students’ internal phenomena is to exploit wearable technologies. In this chapter, after reviewing different definitions of effort, we present a landscape of students’ effort measurement and prediction. Then, we discuss how wearable technologies can be exploited to enhance the accuracy of these measurements and predictions.

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Notes

  1. 1.

    Smart equipment that can be worn by users and easily purchased on the market (Lu et al. 2017), such as smartwatches and glasses.

  2. 2.

    According to Chen et al. (2016), it is a physiological measurement. But we chose to classify it as behavioral because the subject can control his head movements and mouth openness.

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Authors and Affiliations

  1. Université de Lorraine, CNRS, Inria, LORIA, Nancy, France

    Barbara Moissa, Geoffray Bonnin & Anne Boyer

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  1. Barbara Moissa
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  2. Geoffray Bonnin
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  3. Anne Boyer
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Correspondence to Barbara Moissa .

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Editors and Affiliations

  1. Department of Economics and Social Sciences, Beuth University of Applied Sciences, Berlin, Berlin, Germany

    Ilona Buchem

  2. Informatik 5 (Information Systems and Databases), RWTH Aachen University, Aachen, Germany

    Ralf Klamma

  3. Performance Augmentation Lab, Oxford Brookes University, Oxford, UK

    Fridolin Wild

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Moissa, B., Bonnin, G., Boyer, A. (2019). Exploiting Wearable Technologies to Measure and Predict Students’ Effort. In: Buchem, I., Klamma, R., Wild, F. (eds) Perspectives on Wearable Enhanced Learning (WELL). Springer, Cham. https://doi.org/10.1007/978-3-319-64301-4_19

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