Adaptive structure metrics for automated feedback provision in intelligent tutoring systems

Paaßen B, Mokbel B, Hammer B (2016)
Neurocomputing 192(SI): 3-13.

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Journal Article | Published | English
Abstract
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.
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Paaßen B, Mokbel B, Hammer B. Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing. 2016;192(SI):3-13.
Paaßen, B., Mokbel, B., & Hammer, B. (2016). Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing, 192(SI), 3-13.
Paaßen, B., Mokbel, B., and Hammer, B. (2016). Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing 192, 3-13.
Paaßen, B., Mokbel, B., & Hammer, B., 2016. Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing, 192(SI), p 3-13.
B. Paaßen, B. Mokbel, and B. Hammer, “Adaptive structure metrics for automated feedback provision in intelligent tutoring systems”, Neurocomputing, vol. 192, 2016, pp. 3-13.
Paaßen, B., Mokbel, B., Hammer, B.: Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing. 192, 3-13 (2016).
Paaßen, Benjamin, Mokbel, Bassam, and Hammer, Barbara. “Adaptive structure metrics for automated feedback provision in intelligent tutoring systems”. Neurocomputing 192.SI (2016): 3-13.
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Java Sorting Programs
Paaßen B (2016)
Bielefeld University.

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