Towards highly informative learning analytics.

Autor/in	Drachsler, Hendrik
Titel	Towards highly informative learning analytics.
Quelle	Heerlen: Open Universiteit (2023), 62 S. PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2); PDF als Volltext kostenfreie Datei (3); PDF als Volltext kostenfreie Datei (4) Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Monographie
ISBN	978-94-6469-372-0
DOI	10.25656/01:26787
URN	urn:nbn:de:0111-pedocs-267851 urn:nbn:de:0111-pedocs-267875
Schlagwörter	Bildung; Bildungsforschung; Learning Analytics; Künstliche Intelligenz; Lernen; Lernforschung
Abstract	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.
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2024/1