Authors:
Tina Daaboul
and
Hicham Hage
Affiliation:
Department of Computer Science, Notre Dame University - Louaize, Zouk Mosbeh, Keserwan and Lebanon
Keyword(s):
Culturally Aware Learning System, Major Adaptation, Information Extraction, Intelligent Tutoring Systems, Relation Extraction.
Related
Ontology
Subjects/Areas/Topics:
AI and Creativity
;
Applications
;
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Soft Computing
;
Symbolic Systems
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 F
eature 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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