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Guiding high school students toward suitable educational paths is a complex challenge, particularly influenced by academic performance. In Morocco, first-year high school students in the scientific branch face a crucial decision when selecting between science mathematics (SM), physics (SF), and Science of Life and Earth (SVT) paths. This decision is critical as it can significantly impact their future academic and professional success. To address the issue of suboptimal student orientation, this study proposes an automated, personalized approach leveraging big data technology. By employing ensemble learning techniques, including random forest and neural network models, we developed a classification system to predict students' optimal paths based on their academic performance. Our models achieved exceptional performance, with precision, accuracy, recall, and F-measure scores of approximately 98.59%, 98.60%, 98.60%, and 98.58%, respectively. This research demonstrates the potential of our approach to enhance educational support and decision-making, ultimately improving student outcomes and reducing dropout rates caused by wrong orientation. [ABSTRACT FROM AUTHOR]

The blockchain-based electronic voting (e-voting) system, offers universities a safe, easy-to-use platform that enhances accuracy and integrity. Despite that, it is challenging to integrate the blockchain-based e-voting system with current platforms and private data. Managing latency is another requirement during the blockchain transactions (votes/elections). In this work, we suggested a novel system that uses smart contracts on the consortium blockchain to address these constraints. The voters and electors in a university can vote and elect respecting the rules established in smart contracts. The miners validate transactions using proof of work (PoW) and proof of stake (PoS). Data integrity and voter validity are ensured via the SHA-256 hash algorithm and the ECDSA signature. The implementation results demonstrate that the suggested method works better than the state-ofthe-art. exceeds the state-of-the-art in terms of gas cost and execution time. [ABSTRACT FROM AUTHOR]

The experience lived by students during the COVID-19 pandemic will remain etched in their memory because they suddenly found themselves confined and they were forced to continue their studies entirely online from home. This article focuses on the study of factors that may influence students of the National School of Business and Management of Oujda in Morocco to continue using e-learning in the post COVID-19 period. The research model is based on the Technology Acceptance Model and is validated using structural equation methods. Data analysis showed the importance of the online course design factor on perceived ease of use and perceived usefulness. It revealed the direct influence of these two factors on the use of the e-learning system, which is also influenced indirectly by social influence through the mediatization of perceived usefulness. [ABSTRACT FROM AUTHOR]

Abstract Background The disabled population constitutes a class of people needing special care and necessitating important economic and social effort. Methods In this paper, using specific parameter settings, partial differential equations are used to model the temporal change of the proportion of the disabled population in Morocco. Results Combining different forms and values of the parameters, a numerical method is proposed and three scenarios are considered. These forms and values are determined by data fitting and simulation. Conclusions The experiments show clearly the dynamical evolution of the disabled population with time and age according to each scenario.