Abstract
The world has seen major developments in the field of e-learning and distance learning, especially during the COVID-19 crisis, which revealed the importance of these two types of education and the fruitful benefits they have offered in a group of countries, especially those that have excellent infrastructure. At the Faculty of Sciences Semlalia, Cadi Ayyad University Marrakech, Morocco, we have created a simple electronic platform for remote practical work (RPW), and its results have been good in terms of student interaction and even facilitating the employment of a professor. The objective of this work is to propose a recommendation system based on deep quality-learning networks (DQNs) to recommend and direct students in advance of doing the RPW according to their skills of each mouse or keyboard click per student. We are focusing on this technology because it has strong, tremendous visibility and problem-solving ability that we will demonstrate in the result section. Our platform has enabled us to collect a range of students’ and teachers’ information and their interactions with the learning content we will rely on as inputs (a large number of images per second for each mouse or keyboard click per student) into our new system for output (doing the RPW). This technique is reflected in an attempt to embody the virtual teacher’s image within the platform and then adequately trained with DQN technology to perform the RPW.
Highlights
To create a smart agent, the researchers combined deep learning with Reinforcement learning (RL) to obtain the deep reinforcement learning that is virtually unbeatable in a series of video Atari games [13, 14]
deep quality-learning networks (DQNs) [18] connect the dots between deep neural networks with RL by subduing the intractable problems in traditional RL methods
E reason that we focus on deep reinforcement learning is its high efficiency with which we can solve some problems that are sometimes difficult. is technology includes all the problems involved in making its series of decisions
Summary
In 2017, Ouatik et al [1] created an electronic platform for remote practical work that can describe the concept, methodology, and the results of the advanced step supported by the E-lab project of L’AUF (L’Agence Universitaire de la Francophonie). e goal of this platform is combining e-technologies and e-pedagogies to create online undergraduate courses in engineering like practical experience.is project is a part of the improvement and development of the distance learning system (e-learning), especially remote laboratories, which are new technologies that allow learners or researchers to create and conduct scientific experiments and deepen their experimental knowledge in a remote lab through the web. e good and important characteristics of this e-learning system are using and respecting the educational and pedagogical standards followed in teaching and the performance of this system concerning the speed and precision of interaction with the laboratory. E good and important characteristics of this e-learning system are using and respecting the educational and pedagogical standards followed in teaching and the performance of this system concerning the speed and precision of interaction with the laboratory. This system must make the work and the tasks easier for the laboratory manager in matters of preparing and managing the access to experiments and manipulation. DQNs create a smart agent that outperforms the best RL method so far in the test series of Atari games
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