Perceived Task-Difficulty Recognition from Log-File Information for the Use in Adaptive Intelligent Tutoring Systems

Autor/inn/en	Janning, Ruth; Schatten, Carlotta; Schmidt-Thieme, Lars
Titel	Perceived Task-Difficulty Recognition from Log-File Information for the Use in Adaptive Intelligent Tutoring Systems
Quelle	In: International Journal of Artificial Intelligence in Education, 26 (2016) 3, S.855-876 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-016-0097-9
Schlagwörter	Artificial Intelligence; Intelligent Tutoring Systems; Technology Uses in Education; Educational Technology; Emotional Response; Affective Behavior; Cognitive Processes; Statistical Analysis; Classification; Difficulty Level + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Intelligentes Tutorsystem; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Unterrichtsmedien; Emotionales Verhalten; Affective disturbance; Active behaviour; Affektive Störung; Cognitive process; Kognitiver Prozess; Statistische Analyse; Classification system; Klassifikation; Klassifikationssystem; Schwierigkeitsgrad
Abstract	Recognising students' emotion, affect or cognition is a relatively young field and still a challenging task in the area of intelligent tutoring systems. There are several ways to use the output of these recognition tasks within the system. The approach most often mentioned in the literature is using it for giving feedback to the students. The features used for that approach can be high-level features like linguistics features which are words related to emotions or affects, taken e.g. from written or spoken inputs, or low-level features like log-file features which are created from information contained in the log-files. In this work we aim at supporting task sequencing by perceived task-difficulty recognition on low-level features easily extracted from the log-file. We analyse these features by statistical tests showing that there are statistically significant feature combinations and hence the presented features are able to describe students' perceived task-difficulty in intelligent tutoring systems. Furthermore, we apply different classification methods to the log-file features for perceived task-difficulty recognition and present a kind of higher ensemble method for improving the classification performance on the features extracted from a real data set. The presented approach outperforms classical ensemble methods and is able to improve the classification performance substantially, enabling a perceived task-difficulty recognition satisfactory enough for employing its output for components of a real system like task independent support or task sequencing. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01