The Implementation of Deep Reinforcement Learning in E-Learning and Distance Learning: Remote Practical Work

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.

Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

“…Aside from research of RL in robotics and game theory, Abedali El Gourari et al also delved into the implementation of Deep RL in e-learning and distance learning [26]. They adopted a framework for promoting learning and focused on the implementation of Deep Q-Networks to train the agent or a virtual teacher to do Remote Practical Work (RPW) in a short period.…”

“…Agrebi et. al [67] obtained 48% accuracy with DQN and lastly Shahbazi and Byun [68] used Double Deep Q-Network which produced 21.46% accuracy [26]. In Robotics, Alvaro et al implemented Meta RL for the optimal design of legged robots.…”

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

“…The following problems have been studied in selected PRISMA research papers: Higher education quality assurance ( Alakbarov 2021 ; Allayarova 2019 ; Asare et al 2021 ); Knowledge-based recommender system ( Barabash et al 2021 ; Barón et al 2015 ; Bin-Noor et al 2021 ; Brunello and Wruuck 2021 ); E-learning and distance learning ( Bukralia et al 2015 ; Burman et al 2021 ; Casselman 2021 ; Chahal et al 2020 ); Inter-professional education ( Cheng 2017 ); Network course recommendation system ( Cinquin et al 2019 ; Dhar and Jodder 2020 ; Dolgikh 2021 ; Ehimwenma and Krishnamoorthy 2021 ); Modern information communication technologies in the higher education sector ( El Gourari et al 2021 ; Elahi et al 2022 ; Ellyatt 2021 ; Elumalai et al 2019 ); Transformation of education during the COVID-19 pandemic ( Erridge 2006 ; Ezz and Elshenawy 2020 ; Fedushko and Ustyianovych 2020 ); Digitalization in higher education ( Gao et al 2021 ; Garay-Jiménez et al 2021 ; Gil et al 2019 ); Personalized recommendation system for learning resources ( Habib et al 2021 ; Ibrahim et al 2019 ; Karan and Asgari 2021 ; Khan and Ramzan 2018 ; Khosravi et al 2021 ); Artificial intelligence techniques for education tasks solving ( Kim Rosemary et al 2014 ; ...…”

“…Modern information communication technologies in the higher education sector ( El Gourari et al 2021 ; Elahi et al 2022 ; Ellyatt 2021 ; Elumalai et al 2019 );…”

Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients

“…We have implemented a reinforcement learning-based model inspired by Boloka et al [76], who suggested an environment-independent deep reinforcement learning (DRL) framework for transferring information from a model-based teacher to a task-specific modelfree learner in order to avoid executing a randomly initialized policy in the early stages of learning. DRL is a subgenre of reinforcement learning that employs deep neural networks to allow DRL algorithms to function in continuous and high-dimensional settings [77] and is suitable to monitor the learner's progress and adjust the training curve [78].…”