Abstract
The advancement of technologies has modernized learning within smart campuses and has emerged new context through communication between mobile devices. Although there is a revolutionary way to deliver long-term education, a great diversity of learners may have different levels of expertise and cannot be treated in a consistent manner. Nevertheless, multimedia documents recommendation in Arabic language has represented a problem in Natural Language Processing (NLP) due to their richness of features and analysis ambiguities. To tackle the sparsity problem, smart learning recommendation-based approach is proposed for inferring the format of the suitable Arabic document in a contextual situation. Indeed, the user-document interactions are modeled efficiently through deep neural networks architectures. Given the contextual sensor data, the suitable document with the best format is thereafter predicted. The findings suggest that the proposed approach might be effective in improving the learning quality and the collaboration notion in smart learning environment
Highlights
In the modern world, diffuse or ubiquitous computing enables the interconnection of all areas of life using new technologies to foster the creation of intelligent environments, such as smart campuses
The findings suggest that the proposed approach might be effective in improving the learning quality and the collaboration notion in smart learning environment
This paper is structured as follows: First, we present the general context of recommendation systems in smart campus
Summary
Diffuse or ubiquitous computing enables the interconnection of all areas of life using new technologies to foster the creation of intelligent environments, such as smart campuses. International Journal of Web-Based Learning and Teaching Technologies Volume 16 Issue 6 November-December 2021 language is rich of morph-syntactic and semantic features that complicate its analysis (Mahmoud and Zrigui 2019a) It represents a fundamental problem in Natural Language Processing (NLP) due to the wide variety of applications associated with it (e.g., information retrieval, question-answer, temporal information retrieval, etc.) (Haffar et al, 2020a; Haffar et al, 2020b). Faced with these problems, Arabic documents recommendation-based approach is proposed. The last section describes a conclusion and our future works
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