Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data

Autor/inn/en	Xu, Beijie; Recker, Mimi
Titel	Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data
Quelle	In: Educational Technology & Society, 15 (2012) 3, S.103-115 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Electronic Libraries; Instructional Design; Mixed Methods Research; Use Studies; Federal Aid; Experienced Teachers; Beginning Teachers; Teaching Experience; Research Methodology; Educational Research; Computer Uses in Education; Internet; Discussion; Pattern Recognition; Data; Information Retrieval; Data Analysis; Statistical Analysis; Teacher Developed Materials + Suchen Sie Ihr Suchwort? Digitale Bibliothek; Elektronische Bibliothek; Lesson concept; Lessonplan; Unterrichtsentwurf; Benutzerschulung; Junior teacher; Junglehrer; Research method; Forschungsmethode; Bildungsforschung; Pädagogische Forschung; Computernutzung; Diskussion; Mustererkennung; Daten; Auswertung; Statistische Analyse
Abstract	Teachers and students increasingly enjoy unprecedented access to abundant web resources and digital libraries to enhance and enrich their classroom experiences. However, due to the distributed nature of such systems, conventional educational research methods, such as surveys and observations, provide only limited snapshots. In addition, educational data mining, as an emergent research approach, has seldom been used to explore teachers' online behaviors when using digital libraries. Building upon results from a preliminary study, this article presents results from a clustering study of teachers' usage patterns while using an educational digital library tool, called the Instructional Architect. The clustering approach employed a robust statistical model called latent class analysis. In addition, frequent itemsets mining was used to clean and extract common patterns from the clusters initially generated. The final clusters identified three groups of teachers in the IA: "key brokers", "insular classroom practitioners", and "inactive islanders". Identified clusters were triangulated with data collected in teachers' registration profiles. Results showed that increased teaching experience and comfort with technology were related to teachers' effectiveness in using the IA. (Contains 5 tables and 1 figure.) (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Athabasca University, School of Computing & Information Systems, 1 University Drive, Athabasca, AB T9S 3A3, Canada. Tel: 780-675-6812; Fax: 780-675-6973; Web site: http://www.ifets.info
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10