Intelligent Adapted e-Learning System based on Deep Reinforcement Learning

Fouki, Mohammed El; Aknin, Noura; Kadiri, K. Ed El.

doi:10.1145/3167486.3167574

Cited by 8 publications

(9 citation statements)

References 10 publications

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“…TITLE-ABS-KEY( ("reinforcement learning" OR "contextual bandit") AND ("personalization" OR "personalized" OR "personal" OR "personalisation" OR "personalised" OR "customization" OR "customized" OR "customised" OR "customised" OR "individualized" OR "individualised" OR "tailored")) Table 7 Table containing all included publications. The first column refers to the data items in Table 2 # Value Publications 1 n [1,4,10,11,13,16,[18][19][20][24][25][26][27][28][29]31,32,35,36,38,[40][41][42][43][44][45]48,49,52,56,63,66,68,70,74,82,85,86,88,91,93,94,99,101,104,[106]…”

Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work.

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Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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“…The benefits categorized as intelligent services are: learning management system (Lavoie and Proulx, 2019), semantic recommendation using ontology (Sharma and Ahuja, 2016); hybrid recommendation based on student profile (Kapembe and Quenum, 2019); deep reinforcement learning structure (Huang et al, 2019); decision-making system (El Fouki et al, 2017), content-based recommendation system (Venugopalan et al, 2016), domain-specific language (Balderas et al, 2013), WAVE architecture (Manhães et al, 2014), intelligent teaching assistant system (Wang et al, 2019), profile analysis system (El Moustamid et al, 2017), algorithm based on the technique of optimizing ant colonies (Kozierkiewicz-Hetmańska and Zyśk, 2013), prototype indicators (Florian et al, 2011), online learning systems based on big data technologies (Dahdouh et al, 2018), agentbased recommendation system, Java2D technology-based e-learning system (Hamada, 2012), ID based recommendation system (Zakrzewska, 2012;Anaya et al, 2013), capture system (Lagman and Mansul, 2017), custom model (Chanaa and Faddouli, 2018), ontology model (Joy et al, 2019), evaluation tool (Dimopoulos et al, 2013), adaptive recommendation method (Chen et al, 2020), Kernel Context Recommendender System algorithm (Iqbal et al, 2019), distributed course recommendation systems (Dahdouh et al, 2019), custom user interface (Kolekar et al, 2018), recommendation system techniques for educational data mining (Thai-Nghe et al, 2010), individualized artificial intelligence tutor and LBA model (Kim and Kim, 2020) based on a system called SBAN (Zaoudi and Belhadaoui, 2020). The application of methods and techniques of data analysis provide student grade prediction, behavior pattern detection, academic progress forecasting, modeling, course dropout risk prediction, also providing student performance feedback to teachers.…”

Section: Gq2 -What Benefits Have Been Obtained For Students Teachers and Managersmentioning

confidence: 99%

“…The identification and presentation of clusters point to the main terms explored in the final corpus comprising 51 articles. After analyzing the 48 grouping terms, 6 terms were identified that stand out as a search trend, being: recommendation system (Thai-Nghe et al, 2010;Anaya et al, 2013;Venugopalan et al, 2016;Kapembe and Quenum, 2019;Iqbal et al, 2019;Dahdouh et al, 2019), educational data mining (Thai-Nghe et al, 2010;Lara et al, 2014;Olivé et al, 2018), analysis (Zorrilla et al, 2010;Graf et al, 2011;Olanrewaju et al, 2016;Ros et al, 2017;Peñafiel et al, 2018;Uzir et al, 2020), learning management system (Okubo et al, 2017;Olivé et al, 2018;Chanaa and Faddouli, 2018;Lavoie and Proulx, 2019), e-learning and platform e-learning (Chang and Chu, 2010;Zorrilla et al, 2010;Hamada, 2012;Anaya et al, 2013;Olanrewaju et al, 2016;Omar and Abdesselam, 2017;El Fouki et al, 2017;Lagman and Mansul, 2017;Clarizia et al, 2018;Joy et al, 2019;Dahdouh et al, 2019). These terms are within the red, blue and green clusters, and they have connections with each other and connections to the other clusters.…”

Section: Fq3 -What Are the Perceived Trends?mentioning

confidence: 99%

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Silva¹,

Dias²,

Barbosa³

et al. 2021

Informatics in Education

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Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.

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“…El Fouki et al [29] proposed a decision-making system that helps instructors respond to problems using intelligent general techniques applied to data collected on e-learning platforms. The adapted system explores parameters such as learning styles and teaching styles, based on deep neural network algorithms and reinforcement learning, and takes into account the use of the teacher to improve the accuracy of the recommendation system.…”

Section: Authorsmentioning

confidence: 99%

A Literature Review on Intelligent Services Applied to Distance Learning

Silva¹,

Dias²,

Rigo³

et al. 2021

Preprint

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Distance learning has assumed a relevant role in the Educational scenario. The use of Virtual Learning Environments contributes to obtain a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1,316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed to observe that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance and identify probabilities of dropouts from courses.

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Intelligent Adapted e-Learning System based on Deep Reinforcement Learning

Cited by 8 publications

References 10 publications

Reinforcement learning for personalization: A systematic literature review

Reinforcement learning for personalization: A systematic literature review

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

A Literature Review on Intelligent Services Applied to Distance Learning

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