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://aclanthology.org/W19-4430
DOI:
10.18653/v1/W19-4430
Bibkey:
Cite (ACL):
Alessio Miaschi, Chiara Alzetta, Franco Alberto Cardillo, and Felice Dell’Orletta. 2019. Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 285–295, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian (Miaschi et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-4430.pdf