TY  - JOUR
AB  - 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.
DA  - 2016
DO  - 10.1016/j.neucom.2015.12.108
KW  - metric learning
KW  - intelligent tutoring systems
KW  - sequential data
KW  - learning vector quantization
KW  - algebraic dynamic programming
LA  - eng
IS  - SI
M2  - 3
PY  - 2016
SN  - 0925-2312
SP  - 3-13
T2  - Neurocomputing
TI  - Adaptive structure metrics for automated feedback provision in intelligent tutoring systems
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27832247
Y2  - 2024-11-24T04:22:22
ER  -