#lets-discuss: Analyzing Student Affect in Course Forums Using Emoji [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).

Autor/inn/en	Blobstein, Ariel; Gal, Kobi; Kim, Hyunsoo Gloria; Facciotti, Marc; Karger, David; Sripathi, Kamali
Titel	#lets-discuss: Analyzing Student Affect in Course Forums Using Emoji [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).
Quelle	(2022), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Computer Mediated Communication; Nonverbal Communication; Social Media; Online Courses; Group Discussion; Textbooks; Electronic Publishing; Emotional Response; Visual Aids; Student Attitudes; Prediction; Course Content; Teaching Methods; Models; Computational Linguistics; Biology; Science Instruction; State Universities; Open Source Technology; Language Usage; Classification + Suchen Sie Ihr Suchwort? Computerkonferenz; Non-verbal communication; Nonverbale Kommunikation; Soziale Medien; Online course; Online-Kurs; Gruppendiskussion; Textbook; Text book; Schulbuch; Lehrbuch; Elektronisches Publizieren; Emotionales Verhalten; Anschauungsmaterial; Schülerverhalten; Vorhersage; Kursprogramm; Teaching method; Lehrmethode; Unterrichtsmethode; Analogiemodell; Linguistics; Computerlinguistik; Biologie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Staatliche Universität; Sprachgebrauch; Classification system; Klassifikation; Klassifikationssystem
Abstract	Emoji are commonly used in social media to convey attitudes and emotions. While popular, their use in educational contexts has been sparsely studied. This paper reports on the students' use of emoji in an online course forum in which students annotate and discuss course material in the margins of the online textbook. For this study, instructors created 11 custom emoji-hashtag pairs that enabled students to quickly communicate affects and reactions in the forum that they experienced while interacting with the course material. Example reporting includes, inviting discussion about a topic, declaring a topic as interesting, or requesting assistance about a topic. We analyze emoji usage by over 1,800 students enrolled in multiple offerings of the same course across multiple academic terms. The data show that some emoji frequently appear together in posts associated with the same paragraphs, suggesting that students use the emoji in this way to communicating complex affective states. We explore the use of computational models for predicting emoji at the post level, even when posts are lacking emoji. This capability can allow instructors to infer information about students' affective states during their "at home" interactions with course readings. Finally, we show that partitioning the emoji into distinct groups, rather than trying to predict individual emoji, can be both of pedagogical value to instructors and improve the predictive performance of our approach using the BERT language model. Our procedure can be generalized to other courses and for the benefit of other instructors. [For the full proceedings, see ED623995.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01