Open Learner Models Working in Symbiosis with Self-Regulating Learners: A Research Agenda

Autor/in	Winne, Philip H.
Titel	Open Learner Models Working in Symbiosis with Self-Regulating Learners: A Research Agenda
Quelle	In: International Journal of Artificial Intelligence in Education, 31 (2021) 3, S.446-459 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Winne, Philip H.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-020-00212-4
Schlagwörter	Models; Student Characteristics; Access to Information; Learning Processes; Learning Analytics; Independent Study + Suchen Sie Ihr Suchwort? Analogiemodell; Learning process; Lernprozess; Selbststudium
Abstract	Learner modeling systems so far formulated model learning in three main ways: a learner's "position" within a lattice of declarative and procedural knowledge about highly structured disciplines such as geometry or physics, a learner's path through curricular tasks compared to milestones, or profiles of a learner's achievements on a set of tasks relative to mastery criteria or a peer group. Opening these models to learners identifies for them factors and relations among factors. Open learner models tacitly invite learners to regulate learning. However, contemporary learner models omit data about how learners have and should process information to learn, understand, consolidate and transfer new knowledge and skills. What to do with information opened to learners is opaque. I propose incorporating trace data about learning processes in learner models. Trace data allow generating learning analytics that inform self-regulating learners about potentially productive adaptations to processes they have used to learn. In a context of big data, such elaborated learner models are positioned to collaborate with self-regulating learners. Together, they can coordinate symbiotically, creating a platform for the system to improve its models of learners and for learners to more productively self-regulate learning. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01