Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education

Autor/in	Mah, Dana-Kristin
Titel	Learning Analytics and Digital Badges: Potential Impact on Student Retention in Higher Education
Quelle	In: Technology, Knowledge and Learning, 21 (2016) 3, S.285-305 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2211-1662
DOI	10.1007/s10758-016-9286-8
Schlagwörter	Educational Research; Data Collection; Data Analysis; Recognition (Achievement); Evaluation Methods; Information Storage; School Holding Power; Higher Education; College Freshmen; Student Experience; Student Records; Learning Processes; Predictor Variables; At Risk Students; Academic Failure; Dropout Prevention; Feedback (Response); Student Personnel Services; Skill Development; Academic Achievement; Academic Persistence; Student Motivation; Models; Competence; Student Improvement + Suchen Sie Ihr Suchwort? Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Auswertung; Soziale Anerkennung; Informationsspeicherung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Studienanfänger; Studienerfahrung; Schülerakte; Learning process; Lernprozess; Prädiktor; Kompetenzentwicklung; Qualifikationsentwicklung; Schulleistung; Schulische Motivation; Analogiemodell; Kompetenz
Abstract	Learning analytics and digital badges are emerging research fields in educational science. They both show promise for enhancing student retention in higher education, where withdrawals prior to degree completion remain at about 30% in Organisation for Economic Cooperation and Development member countries. This integrative review provides an overview of the theoretical literature as well as current practices and experience with learning analytics and digital badges in higher education with regard to their potential impact on student retention to enhance students' first-year experience. Learning analytics involves measuring and analyzing dynamic student data in order to gain insight into students' learning processes and optimize learning and teaching. One purpose of learning analytics is to construct predictive models to identify students who risk failing a course and thus are more likely to drop out of higher education. Personalized feedback provides students with information about academic support services, helping them to improve their skills and therefore be successful in higher education. Digital badges are symbols for certifying knowledge, skills, and competencies on web-based platforms. The intention is to encourage student persistence by motivating them, recognizing their generic skills, signaling their achievements, and capturing their learning paths. This article proposes a model that synthesizes learning analytics, digital badges, and generic skills such as academic competencies. The main idea is that generic skills can be represented as digital badges, which can be used for learning analytics algorithms to predict student success and to provide students with personalized feedback for improvement. Moreover, this model may serve as a platform for discussion and further research on learning analytics and digital badges to increase student retention in higher education. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01