A Recommendation System Framework for Educational Content Reinforcement in Virtual Learning Environments

Adson R. P. Damasceno; Lucas C. Carneiro; João Victor F. T. De Sampaio; Allberson B. O. Dantas; Eudenia Magalhães; Paulo Henrique M. Maia; Francisco C. M. B. Oliveira

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Paper

A Recommendation System Framework for Educational Content Reinforcement in Virtual Learning Environments

Topics: Adaptive Educational Systems; Educational Data Mining; Intelligent Learning and Teaching Systems; Intelligent Modelling; Intelligent Tutoring Systems; Learning with AI Systems; Machine Learning; Natural Language Processing

In Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU, 228-235, 2022

Authors: Adson R. P. Damasceno ; Lucas C. Carneiro ; João Victor F. T. De Sampaio ; Allberson B. O. Dantas ; Eudenia Magalhães ; Paulo Henrique M. Maia and Francisco C. M. B. Oliveira

Affiliation: UECE - State University of Ceará, Fortaleza, Brazil

Keyword(s): Recommendation of Educational Content, Educational Content Reinforcement, Recommendation Systems, Virtual Learning Environments.

Abstract: In Virtual Learning Environments, tutors play a vital role by supporting students and improving their learning through the courses. One important task is to identify content with which the students struggle and give them suggestions for educational resources to reinforce their learning and overcome difficulties. However, providing individualized suggestions for each student may be infeasible, especially for courses with many enrolled students. In this work, we propose and validate a framework for building recommendation systems of educational content for Virtual Learning Environments. Our proposed system identifies the content that a student needs to reinforce based on the results of his assessments and recommends resources that best relate to the questions that he answered incorrectly, using Information Retrieval, Machine Learning, and Natural Language Processing techniques. We validate our proposed solution by taking as a case study data collected from DAL - Dell Accessible Learnin g, an distance learning platform. We built a dataset with content from 8 courses to compare the performance of different methods and text representations in our framework. Our best result achieved an accuracy of 0.89 using a Nearest Neighbor method with TF-IDF representation. (More)

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Paper citation in several formats:

Damasceno, A.; Carneiro, L.; T. De Sampaio, J.; Dantas, A.; Magalhães, E.; Maia, P. and Oliveira, F. (2022). A Recommendation System Framework for Educational Content Reinforcement in Virtual Learning Environments. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU; ISBN 978-989-758-562-3; ISSN 2184-5026, SciTePress, pages 228-235. DOI: 10.5220/0011032000003182

@conference{csedu22,
author={Adson R. P. Damasceno. and Lucas C. Carneiro. and João Victor F. {T. De Sampaio}. and Allberson B. O. Dantas. and Eudenia Magalhães. and Paulo Henrique M. Maia. and Francisco C. M. B. Oliveira.},
title={A Recommendation System Framework for Educational Content Reinforcement in Virtual Learning Environments},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU},
year={2022},
pages={228-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011032000003182},
isbn={978-989-758-562-3},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU
TI - A Recommendation System Framework for Educational Content Reinforcement in Virtual Learning Environments
SN - 978-989-758-562-3
IS - 2184-5026
AU - Damasceno, A.
AU - Carneiro, L.
AU - T. De Sampaio, J.
AU - Dantas, A.
AU - Magalhães, E.
AU - Maia, P.
AU - Oliveira, F.
PY - 2022
SP - 228
EP - 235
DO - 10.5220/0011032000003182
PB - SciTePress