Recommender in AI-enhanced learning. An assessment from the perspective of instructional design.

Autor/inn/en	Kerres, Michael; Buntins, Katja
Titel	Recommender in AI-enhanced learning. An assessment from the perspective of instructional design.
Quelle	In: Open education studies, 2 (2020) 1, S. 101-111 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben
Zusatzinformation	Forschungsdaten, Studiendetails und Erhebungsinstrumente
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	2544-7831
DOI	10.1515/edu-2020-0119
Schlagwörter	Soziale Interaktion; Digitale Medien; Digitale Revolution; Künstliche Intelligenz; Künstliche Intelligenz; Lernen; Computerunterstützter Unterricht; Digitalisierung; Lernerfahrung; E-Learning; Lernbedingungen; Lernberatung; Lernen; Lernerfahrung; Computerunterstützter Unterricht; Lernmaterial; Technologieunterstütztes Lernen; Digitalisierung; E-Learning; Digitale Revolution; Lernangebot; Lernberater
Abstract	As tools for AI-enhanced human learning, recommender systems support learners in finding materials and sequencing learning paths. The paper explores how these recommenders improve the learning experience from a perspective of instructional design. It analyzes mechanisms underlying current recommender systems, and it derives concrete examples of how they operate: Recommenders are either expert-, criteria-, behavior-, or profile-based or rely on social comparisons. To verify this classification of five different mechanisms, we analyze a set of current publications on recommenders and find all the identified mechanisms with profile-based approaches as the most common. Social recommenders, though highly attractive in other sectors, reveal some drawbacks in the context of learning. In comparison, expert-based recommendations are easy to implement and often stand out as simple but effective ways for suggesting learning materials and learning paths to learners. They can be combined with other approaches based on social comparisons and individual profiles. The paper points out challenges in studying recommenders for learning and provides suggestions for future research. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2022/2