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Investigating students' perceived benefits of system- vs. teacher-based learning analytics feedback
Schumacher, Clara
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Ifenthaler, Dirk
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URL:
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https://earli.org/sites/default/files/2021-08/EARL...
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Dokumenttyp:
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Präsentation auf Konferenz
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Erscheinungsjahr:
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2021
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Seitenbereich:
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57
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Veranstaltungstitel:
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19th Biennial EARLI Conference 2019 : Education and Citizenship: Learning and Instruction and the Shaping of Futures
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Veranstaltungsort:
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Online
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Veranstaltungsdatum:
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23.-27.08.2021
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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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004 Informatik
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Abstract:
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Feedback is considered to be essential for supporting learning processes. As learning is increasingly facilitated through digital learning environments new methods such as learning analytics enable additional insights into learning processes. These data can be used as a source for offering feedback to learners
using dashboards, messages, recommendations or prompts. However, research on benefits students perceive from feedback based on learning analytics is limited. Thus, this study investigates students’ perceptions of system- vs. teacher-based learning analytics feedback either with or without recommendations
using a quasi-experimental approach. Findings indicate that benefits associated with the different feedback representations were perceived significantly different. Feedback with recommendations was perceived more beneficial than system-based feedback without recommendations. Future research might
investigate students’ perceptions of and reactions to learning analytics feedback in authentic learning settings.
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