Sentiment Evolution with Interaction Levels in Blended Learning Environments: Using Learning Analytics and Epistemic Network Analysis

Autor/inn/en	Huang, Changqin; Han, Zhongmei; Li, Ming; Wang, Xizhe; Zhao, Wenzhu
Titel	Sentiment Evolution with Interaction Levels in Blended Learning Environments: Using Learning Analytics and Epistemic Network Analysis
Quelle	In: Australasian Journal of Educational Technology, 37 (2021) 2, S.81-95 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1449-5554
Schlagwörter	Learning Analytics; Interaction; Blended Learning; Electronic Learning; Synchronous Communication; Discussion; Network Analysis; Social Networks; Attitude Change; Psychological Patterns; Educational Technology; Graduate Students; Foreign Countries; China + Suchen Sie Ihr Suchwort? Interaktion; Diskussion; Netzplantechnik; Social network; Soziales Netzwerk; Attitudinal change; Einstellungsänderung; Unterrichtsmedien; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Ausland
Abstract	Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students' sentiments evolved from positive to confused/negative to insightful. (As Provided).
Anmerkungen	Australasian Society for Computers in Learning in Tertiary Education. Ascilite Secretariat, P.O. Box 44, Figtree, NSW, Australia. Tel: +61-8-9367-1133; e-mail: info@ascilite.org.au; Web site: https://ajet.org.au/index.php/AJET
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01