“Majorly Adapted Translator”: Towards Major Adaptation in ITS

Tina Daaboul, Hicham Hage

Abstract

Culturally Aware Learning Systems are intelligent systems that adapt learning materials or techniques to the culture of learners having different “country, hobbies, experiences, etc.”, helping them better understand the topics being taught. In higher education, many learning sessions involve students of different majors. As observed, many instructors tend to manually modify the exercises several times, once for every major to adapt to the culture, which is tedious and impractical. Therefore, in this paper we propose an approach to making learning sessions adaptable to the major of the learner. Specifically, this work introduces an Artificial Intelligent system, “Majorly Adapted Translator (MAT)”, which aims at translating and adapting exercises from one major to another. MAT has two main phases, the first identifies the parts of an exercise that needs changing and creates an exercise template. The second translates and adapts the exercise. This work, highlights the first phase, the Feature Extract phase, which relies on our own relation extraction method to identify variables which extracts relations specific to named entities by using dependency relations and shallow parsing. Moreover, we report the performance of the system that was tested on a number of probability exercises.

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Paper Citation


in Harvard Style

Daaboul T. and Hage H. (2019). “Majorly Adapted Translator”: Towards Major Adaptation in ITS.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 451-457. DOI: 10.5220/0007296904510457


in Bibtex Style

@conference{icaart19,
author={Tina Daaboul and Hicham Hage},
title={“Majorly Adapted Translator”: Towards Major Adaptation in ITS},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={451-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007296904510457},
isbn={978-989-758-350-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - “Majorly Adapted Translator”: Towards Major Adaptation in ITS
SN - 978-989-758-350-6
AU - Daaboul T.
AU - Hage H.
PY - 2019
SP - 451
EP - 457
DO - 10.5220/0007296904510457