Facial Expressions When Learning with a Queer History App: Application of the Control Value Theory of Achievement Emotions

Autor/inn/en	Ahn, Byunghoon; Harley, Jason M.
Titel	Facial Expressions When Learning with a Queer History App: Application of the Control Value Theory of Achievement Emotions
Quelle	In: British Journal of Educational Technology, 51 (2020) 5, S.1563-1576 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Harley, Jason M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.12989
Schlagwörter	Nonverbal Communication; Computer Software; LGBTQ People; Data Analysis; Cognitive Processes; Emotional Response; Social Influences; Instructional Design; Multimedia Instruction; History; Multiple Choice Tests; Eye Movements; Profiles; Correlation; Psychological Patterns; Learning Processes; Reading Processes + Suchen Sie Ihr Suchwort? Non-verbal communication; Nonverbale Kommunikation; Auswertung; Cognitive process; Kognitiver Prozess; Emotionales Verhalten; Sozialer Einfluss; Lesson concept; Lessonplan; Unterrichtsentwurf; Multimediales Lernen; Geschichte; Geschichtsdarstellung; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Augenbewegung; Charakterisierung; Profilanalyse; Korrelation; Learning process; Lernprozess; Leseprozess
Abstract	Learning analytics (LA) incorporates analyzing cognitive, social and emotional processes in learning scenarios to make informed decisions regarding instructional design and delivery. Research has highlighted important roles that emotions play in learning. We have extended this field of research by exploring the role of emotions in a relatively uncommon learning scenario: learning about queer history with a multimedia mobile app. Specifically, we used an automatic facial recognition software (FaceReader 7) to measure learners' discrete emotions and a counter-balanced multiple-choice quiz to assess learning. We also used an eye tracker (EyeLink 1000) to identify the emotions learners experienced while they read specific content, as opposed to the emotions they experienced over the course of the entire learning session. A total of 33 out of 57 of the learners' data were eligible to be analyzed. Results revealed that learners expressed more negative-activating emotions (ie, anger, anxiety) and negative-deactivating emotions (ie, sadness) than positive-activating emotions (ie, happiness). Learners with an angry emotion profile had the highest learning gains. The importance of examining typically undesirable emotions in learning, such as anger, is discussed using the control-value theory of achievement emotions. Further, this study describes a multimodal methodology to integrate behavioral trace data into learning analytics research. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01