File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.compedu.2023.104797
- Scopus: eid_2-s2.0-85152603450
- WOS: WOS:000986705600001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Measuring emotions in education using wearable devices: A systematic review
Title | Measuring emotions in education using wearable devices: A systematic review |
---|---|
Authors | |
Keywords | Data science applications in education Distributed learning environments Evaluation methodologies Teaching/learning strategies |
Issue Date | 8-Apr-2023 |
Publisher | Elsevier |
Citation | Computers & Education, 2023, v. 200 How to Cite? |
Abstract | Wearable devices that detect real-time and fine-grained physiological signals offer potentials for understanding the intricate mechanisms of emotions in education. However, due to the diversities of wearable devices, physiological signals, educational emotions, and educational contexts, there is lack of consensus on the affordance and constraints of wearable devices for measuring emotions in education. The present study conducted a systematic literature review and examined 50 peer-reviewed journal articles and influential proceedings published over the last 15 years (January 2008 to December 2022). Five research questions were addressed concerning research backgrounds, theoretical frameworks, methodologies, remaining challenges, and ethical considerations. Findings demonstrated that while most studies focused on university students in controlled environments, recent advances in wearable devices have enabled emotion measurements of younger learners in natural settings. Research interests have developed towards understanding the theoretical connections between emotion and cognition leveraging wearable devices. Electrodermal activity and heart rate were the most frequently measured signals whereas “engagement”, “positive”, and “anxiety” were the most studied emotions. Machine learning and inferential statistics were often adopted to examine associations between physiological signals and educational emotions. Moreover, we identified a need for updated ethical guidelines in advanced data collection using wearable devices. This review can not only inform wearable device usages in educational practices but also shed light on future research. |
Persistent Identifier | http://hdl.handle.net/10722/341765 |
ISSN | 2023 Impact Factor: 8.9 2023 SCImago Journal Rankings: 3.651 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ba, S | - |
dc.contributor.author | Hu, X | - |
dc.date.accessioned | 2024-03-26T05:37:01Z | - |
dc.date.available | 2024-03-26T05:37:01Z | - |
dc.date.issued | 2023-04-08 | - |
dc.identifier.citation | Computers & Education, 2023, v. 200 | - |
dc.identifier.issn | 0360-1315 | - |
dc.identifier.uri | http://hdl.handle.net/10722/341765 | - |
dc.description.abstract | <p>Wearable devices that detect real-time and fine-grained physiological signals offer potentials for understanding the intricate mechanisms of emotions in education. However, due to the diversities of wearable devices, physiological signals, educational emotions, and educational contexts, there is lack of consensus on the affordance and constraints of wearable devices for measuring emotions in education. The present study conducted a systematic literature review and examined 50 peer-reviewed journal articles and influential proceedings published over the last 15 years (January 2008 to December 2022). Five research questions were addressed concerning research backgrounds, theoretical frameworks, methodologies, remaining challenges, and ethical considerations. Findings demonstrated that while most studies focused on university students in controlled environments, recent advances in wearable devices have enabled emotion measurements of younger learners in natural settings. Research interests have developed towards understanding the theoretical connections between emotion and cognition leveraging wearable devices. Electrodermal activity and heart rate were the most frequently measured signals whereas “engagement”, “positive”, and “anxiety” were the most studied emotions. Machine learning and inferential statistics were often adopted to examine associations between physiological signals and educational emotions. Moreover, we identified a need for updated ethical guidelines in advanced data collection using wearable devices. This review can not only inform wearable device usages in educational practices but also shed light on future research.</p> | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Computers & Education | - |
dc.subject | Data science applications in education | - |
dc.subject | Distributed learning environments | - |
dc.subject | Evaluation methodologies | - |
dc.subject | Teaching/learning strategies | - |
dc.title | Measuring emotions in education using wearable devices: A systematic review | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.compedu.2023.104797 | - |
dc.identifier.scopus | eid_2-s2.0-85152603450 | - |
dc.identifier.volume | 200 | - |
dc.identifier.eissn | 1873-782X | - |
dc.identifier.isi | WOS:000986705600001 | - |
dc.identifier.issnl | 0360-1315 | - |