Fitbit for learning: Towards capturing the learning experience using wearable sensing. Issue 136 (April 2020)

• Record Type: Journal Article
• Authors: Giannakos, Michail N.; Sharma, Kshitij; Papavlasopoulou, Sofia; Pappas, Ilias O.; Kostakos, Vassilis
• Is Part Of: International journal of human-computer studies. Issue 136(2020)
• Publication Date: 2020-04
• Subjects: Wearables -- Learning experience -- Sensing -- Machine learning -- Tracking learning; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2019.102384
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: We propose using wearable sensing to capture students' perceived learning. Data from 31 students in 93 class sessions were collected. We apply machine learning to infer students' perceived learning. Wearable data predict students' perceived learning with 11% error. 6.25 min of data is needed to achieve a reliable estimate (13.8% error). Abstract: The assessment of learning during class activities mostly relies on standardized questionnaires to evaluate the efficacy of the learning design elements. However, standardized questionnaires pose additional strain on students, do not provide "temporal" information during the learning experience, require considerable effort and language competence, and sometimes are not appropriate. To overcome these challenges, we propose using wearable devices, which allow for continuous and unobtrusive monitoring of physiological parameters during learning. In this paper we set out to quantify how well we can infer students' learning experience from wrist-worn devices capturing physiological data. We collected data from 31 students in 93 class sessions (3 class sessions per student), and our analysis shows that wrist data can predict the learning experience with 11% error. We also show that 6.25 min (SD = 3.1 min) of data are needed to achieve a reliable estimate (i.e., 13.8% error). Our work highlights the benefits and limitations of utilizing wearable devices to assess learning experiences. Our findings help shape the future of quantified-self technologies in learning by pointing out the substantial benefits of physiological sensing for self-monitoring, evaluation, and metacognitive reflection in learning.