Literaturnachweis - Detailanzeige
| Autor/inn/en | Zhang, Si; Gao, Qianqian; Wen, Yun; Li, Mengsiying; Wang, Qiyun |
|---|---|
| Titel | Automatically Detecting Cognitive Engagement beyond Behavioral Indicators: A Case of Online Professional Learning Community |
| Quelle | In: Educational Technology & Society, 24 (2021) 2, S.58-72 (15 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1436-4522 |
| Schlagwörter | Faculty Development; Teacher Participation; Online Courses; Computer Mediated Communication; Communities of Practice; Educational Technology; Technology Uses in Education; Foreign Countries; Elementary School Teachers; Secondary School Teachers; Group Discussion; China |
| Abstract | Online discourse is widely used in diverse contexts of learning and professional training, but superficial interactions and digression often occur. In the face of these problems and the large-scale unstructured text data, the traditional way of learning analytics has been challenged in terms of providing timely intervention and feedback. In this paper, a workflow for automatically detecting in-service teachers' cognitive engagement in an online professional learning community is described. Discourse data of 1834 in-service teachers involved in a teacher professional development program was collected and processed using the Word2vec toolkit to generate lexical vectors. The method of vector space projection was used to calculate the new information contained in each post, cosine similarity was used to calculate topic relevance, and cluster analysis was used to explore in-service teachers' discourse characteristics. Results showed that in-service teachers' average contribution was 4.59 posts and the average length of each post was 39.47 characters in Chinese. In the mathematics online professional learning community, the average amount of new information contained in each post was 0.221 and in-service teachers' posts contained much new information in the early stages of online discourse. Most in-service teachers' posts were relevant to the discussion topic. Cluster analysis showed three different groups of posts with unique characteristics: high topic relevance with much new information, high topic relevance with little new information, and low topic relevance with little new information. Finally, limitations are discussed and future research directions are proposed. (As Provided). |
| Anmerkungen | International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
Literaturbeschaffung und Bestandsnachweise in Bibliotheken prüfen
Permalink als QR-Code
Inhalt auf sozialen Plattformen teilen (nur vorhanden, wenn Javascript eingeschaltet ist)
Teile diese Seite:
