Adaptive structure metrics for automated feedback provision in intelligent tutoring systems

NEUROCOMPUTING

卷:192

Article; Proceedings Paper

Paassen, Benjamin¹  Mokbel, Bassam¹  Hammer, Barbara¹ 

[1] Univ Bielefeld, Ctr Excellence Cognit Interact Technol CITEC, Inspirat 1, D-33619 Bielefeld, Germany

关键词: Metric learning;    Intelligent tutoring systems;    Sequential data;    Learning vector quantization;    Algebraic dynamic programming;   

DOI  :  10.1016/j.neucom.2015.12.108

来源: Elsevier

【 摘 要 】

Typical intelligent tutoring systems rely on detailed domain-knowledge which is hard to obtain and difficult to encode. As a data-driven alternative to explicit domain-knowledge, one can present learners with feedback based on similar existing solutions from a set of stored examples. At the heart of such a data-driven approach is the notion of similarity. We present a general-purpose framework to construct structure metrics on sequential data and to adapt those metrics using machine learning techniques. We demonstrate that metric adaptation improves the classification of wrong versus correct learner attempts in a simulated data set from sports training, and the classification of the underlying learner strategy in a real Java programming dataset. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free