Logo des Repositoriums
 
Zeitschriftenartikel

Learning Feedback in Intelligent Tutoring Systems

Vorschaubild nicht verfügbar

Volltext URI

Dokumententyp

Text/Journal Article

Zusatzinformation

Datum

2015

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Verlag

Springer

Zusammenfassung

Intelligent Tutoring Systems (ITSs) are adaptive learning systems that aim to support learners by providing one-on-one individualized instruction. Typically, instructing learners in ITSs is build on formalized domain knowledge and, thus, the applicability is restricted to well-defined domains where knowledge about the domain being taught can be explicitly modeled. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. As part of the DFG priority programme “Autonomous Learning”, the FIT project has been conducted over a period of 3 years pursuing the goal to develop novel ITS methods, that are also applicable for ill-defined problems, based on implicit domain knowledge extracted from educational data sets. Here, machine learning techniques have been used to autonomously infer structures from given learning data (e.g., student solutions) and, based on these structures, to develop strategies for instructing learners.

Beschreibung

Gross, Sebastian; Mokbel, Bassam; Hammer, Barbara; Pinkwart, Niels (2015): Learning Feedback in Intelligent Tutoring Systems. KI - Künstliche Intelligenz: Vol. 29, No. 4. Springer. PISSN: 1610-1987. pp. 413-418

Zitierform

DOI

Tags