Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian

Alessio Miaschi, Chiara Alzetta, Franco Alberto Cardillo, Felice Dell’Orletta


Abstract
We present a new concept prerequisite learning method for Learning Object (LO) ordering that exploits only linguistic features extracted from textual educational resources. The method was tested in a cross- and in- domain scenario both for Italian and English. Additionally, we performed experiments based on a incremental training strategy to study the impact of the training set size on the classifier performances. The paper also introduces ITA-PREREQ, to the best of our knowledge the first Italian dataset annotated with prerequisite relations between pairs of educational concepts, and describe the automatic strategy devised to build it.
Anthology ID:
W19-4430
Volume:
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
285–295
Language:
URL:
https://www.aclweb.org/anthology/W19-4430
DOI:
10.18653/v1/W19-4430
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-4430.pdf