Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing

Personalized recommendation as a practical approach to overcoming information overloading has been widely used in e-learning. Based on learners individual knowledge level, we propose a new model that can predict learners needs for recommendation using dynamic graph-based knowledge tracing. By applyi...

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Detalles Bibliográficos
Autores principales: Chanaa, Abdessamad, El Faddouli, Nour-Eddine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334686/
http://dx.doi.org/10.1007/978-3-030-52240-7_9
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author Chanaa, Abdessamad
El Faddouli, Nour-Eddine
author_facet Chanaa, Abdessamad
El Faddouli, Nour-Eddine
author_sort Chanaa, Abdessamad
collection PubMed
description Personalized recommendation as a practical approach to overcoming information overloading has been widely used in e-learning. Based on learners individual knowledge level, we propose a new model that can predict learners needs for recommendation using dynamic graph-based knowledge tracing. By applying the Gated Recurrent Unit (GRU) and the Attention model, this approach designs a dynamic graph over different time steps. Through learning feature information and topology representation of nodes/learners, this model can predict with high accuracy of 80,63% learners with low knowledge acquisition and prepare them for further recommendation.
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spelling pubmed-73346862020-07-06 Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing Chanaa, Abdessamad El Faddouli, Nour-Eddine Artificial Intelligence in Education Article Personalized recommendation as a practical approach to overcoming information overloading has been widely used in e-learning. Based on learners individual knowledge level, we propose a new model that can predict learners needs for recommendation using dynamic graph-based knowledge tracing. By applying the Gated Recurrent Unit (GRU) and the Attention model, this approach designs a dynamic graph over different time steps. Through learning feature information and topology representation of nodes/learners, this model can predict with high accuracy of 80,63% learners with low knowledge acquisition and prepare them for further recommendation. 2020-06-10 /pmc/articles/PMC7334686/ http://dx.doi.org/10.1007/978-3-030-52240-7_9 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Chanaa, Abdessamad
El Faddouli, Nour-Eddine
Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing
title Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing
title_full Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing
title_fullStr Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing
title_full_unstemmed Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing
title_short Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing
title_sort predicting learners need for recommendation using dynamic graph-based knowledge tracing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334686/
http://dx.doi.org/10.1007/978-3-030-52240-7_9
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