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Ariadne Pfad:



Towards highly informative learning analytics
OriginalveröffentlichungHeerlen : Open Universiteit 2023, 62 S.
Dokument  (7.803 KB)
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Schlagwörter (Deutsch)
DokumentartMonographie, Sammelwerk oder Erstveröffentlichung
ISBN978-94-6469-372-0; 9789464693720
BegutachtungsstatusReview-Status unbekannt
Abstract (Englisch):Among various trending topics that can be investigated in the field of educational technology, there is a clear and high demand for using artificial intelligence (AI) and educational data to improve the whole learning and teaching cycle. This spans from collecting and estimating the prior knowledge of learners for a certain subject to the actual learning process and its assessment. AI in education cuts across almost all educational technology disciplines and is key to many other technological innovations for educational institutions. The use of data to inform decision-making in education and training is not new, but the scope and scale of its potential impact on teaching and learning have silently increased by orders of magnitude over the last few years. The release of ChatGPT was another driver to finally make everyone aware of the potential effects of AI technology in the digital education system of today. We are now at a stage where data can be automatically harvested at previously unimagined levels of granularity and variety. Analysis of these data with AI has the potential to provide evidence-based insights into learners’ abilities and patterns of behaviour that, in turn, can provide crucial action points to guide curriculum and course design, personalised assistance, generate assessments, and the development of new educational offerings. AI in education has many connected research communities like Artificial Intelligence in Education (AIED), Educational Data Mining (EDM), or Learning Analytics (LA). LA is the term that is used for research, studies, and applications that try to understand and support the behaviour of learners based on large sets of collected data.
Eintrag erfolgte am23.06.2023
QuellenangabeDrachsler, Hendrik: Towards highly informative learning analytics. Heerlen : Open Universiteit 2023, 62 S. - URN: urn:nbn:de:0111-pedocs-267875 - DOI: 10.25656/01:26787
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