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Why learning analytics need to care for motivational dispositions of students
Schumacher, Clara
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Ifenthaler, Dirk
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Dokumenttyp:
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Präsentation auf Konferenz
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Erscheinungsjahr:
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2018
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Veranstaltungstitel:
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AERA Annual Meeting 2018
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Veranstaltungsort:
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New York, NY
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Veranstaltungsdatum:
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13.04.2018
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Betriebswirtschaftslehre > Wirtschaftspädagogik, Technologiebasiertes Instruktionsdesign (Ifenthaler 2015-)
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Fachgebiet:
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150 Psychologie
370 Erziehung, Schul- und Bildungswesen
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Abstract:
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Depending on their motivational dispositions, students choose different learning strategies and vary in their persistence in reaching learning outcomes. As learning is increasingly facilitated by technology, analytics approaches allow analyzing and optimizing learning processes and environments. However, research on motivation in learning analytics is at an early stage. Thus, the purpose of this study is to investigate the relation of students’ motivational dispositions on perceived learning analytics benefits. Findings indicate that facets of students’ goal orientations and academic self-concept impact students’ expectations on learning analytics benefits. Findings emphasize the need for designing highly personalized and adaptable learning analytics systems considering students’ dispositions and needs. The present study is a first step of linking empirical evidence, motivational theory, and learning analytics.
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