<p class="0papertitle">In both the private and public sectors, human resource management processes face considerable challenges in terms of skills management within organizations. In fact, during the recruitment process, it is difficult to find the right profile for certain functions. To cope with this constraint and thus streamline this process, organizations tend to implement intelligent management of jobs and skills. Most systems of matching a job with a profile face the difficulty of developing and maintaining resources specific to each field. In view of this, ontologies are not only a tool for professional management and strategic management of human resources, but they also make it possible to base the relationship between the couple job / profile. Thus, we propose a construction approach of three ontologies that will play a key role in knowledge management in the context of the Secrétariat Chargé De L’eau but which remains valid for later use in broader contexts.</p>
We witness, today, a strong evolution of learning environments. In parallel, a problem has emerged, consisting in how to capitalize the production of resources when switching from one environment to another. The heterogeneity of the environments, the evolution of the platforms and the will to reuse the educational resources already produced pushed us to design an intelligent system based on cases. In this study, we will focus on the need for resource indexing to facilitate the task of researching and recommending educational resources for authors regardless of the learning environment used. In the literature, this representation can take two forms: Standards or ontologies. The use of standards has partially solved our problem since it is very beneficial for systems that are under construction. On the other hand, it is more interesting to go through the ontologies for systems that are already designed, that we wish to reuse, especially for those that have shown, through the authors, a great satisfaction in the field of knowledge management. Indeed, their use does not require an investment in the environments concerned by the reuse.
In order to identify the existence of an ontology of dependability, we conducted a bibliographic review. However, we encountered a real problem while researching and selecting articles that address this issue. Indeed the research process in various scientific databases with standard techniques involve the application of a number of manual and duplicative steps. This makes it a fairly costly and faulty process. To surmount these challenges, we offer a solution that automates search and selection items. The proposed solution is essentially derived from the popular method called systematic mapping. In this article, we suggest a Web Service as an implementation of our proposed approach. This Web Service will allow users to query scientific databases to obtain the metadata of selected articles. Our proposal will make the selection of scientific articles easier and faster.
Interests play an essential role in the process of learning, thereby enriching learners ‘interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this paper, we aim to identify and extract the topical interest from the text content shared by learners on social media to enrich their course preferences in MOOCs. We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. The results of our experimentation have shown that BERTopic performed better on the scrapped dataset.
Integrating Artificial Intelligence (AI) technologies implied significant growth in variousdomains. Furthermore, many companies integrate AI technologies into their products toenhance the quality of their services. Chatbots are among the AI technologies widely used inseveral areas, especially E-learning. Chatbots support learners in their learning processes byhelping them to find the appropriate answers to their questions. We aim to conduct a systematicliterature review (SLR) to uncover the use of AI chatbots to offload teachers from repetitive andmassive tasks. This article surveys the literature over the period 2016–2022 on the use of AIchatbots in the E-learning domain as they automatically answer learners’ questions. Thus, weidentify, collect, and synthesize multiple research studies on the application of AI chatbots in theE-learning field. Based on the renowned frameworks, PRISMA and PICO, we have succeeded in(1) Developing our research questions and (2) Automatically implementing a solution based onPython language to analyze selected papers, highlighting research gaps, and opening new windowsto guide our future works. Our study shows that chatbots effectively interact with learners.However, there are some drawbacks: (1) Educational chatbots are still limited in their localKnowledge Base (KB), which makes them unable to answer students’ questions correctly. Thus,Chatbot’s KB needs to be extended through external sources, enabling the chatbot to update itsKB over time, making it rich and saving time. (2) Lack of reliable external sources to enrich thechatbot’s KB and make it up to date. (3) Lack of educational chatbots with smart services suchas speech recognition and sentiment analysis to boost the user experience and make learningeasier. In our SLR, we discuss these limitations and propose some solutions to fill the gap.
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