Learning Analytics from Research to Practice: A Content Analysis to Assess Information Quality on Product Websites

Autor/in	Sarmonpal, Sandra
Titel	Learning Analytics from Research to Practice: A Content Analysis to Assess Information Quality on Product Websites
Quelle	(2018), (137 Seiten) PDF als Volltext Verfügbarkeit Ed.D. Dissertation, Pepperdine University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-0-4387-3315-2
Schlagwörter	Hochschulschrift; Dissertation; Learning Analytics; Research and Development; Theory Practice Relationship; Web Sites; Outcomes of Education; Merchandise Information; Elementary Secondary Education; Information Sources; Credibility + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Forschung und Entwicklung; Theorie-Praxis-Beziehung; Web-Design; Lernleistung; Schulerfolg; Information source; Informationsquelle; Glaubwürdigkeit
Abstract	The purpose of this study was to examine and describe the nature of the research to practice gap in learning analytics applications in K12 educational settings. It was also the purpose of this study to characterize how learning analytics are currently implemented and understood. A secondary objective of this research was to advance a preliminary learning analytics implementation framework for practitioners. To achieve these purposes, this study applied quantitative content analysis using automated text analysis techniques to assess the quality of information provided on analytics-based product websites against learning analytics research. Because learning analytics implementations require adoption of analytical tools, characterizing content on analytics-based product websites provides insight into data practices in K12 schools and how learning analytics are practiced and understood. A major finding of this study was that learning analytics do not appear to be applied in ways that will improve learning outcomes for students as described by the research. A second finding was that policy influence expressed in the study corpus suggest competing interests within the current policy structure for K12 education settings. Keywords: quantitative content analysis, automated text analysis, learning analytics, big data, frameworks, educational technology, website content analysis. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01