Adaptive Educational Software by Applying Reinforcement Learning

Bennane, Abdellah

doi:10.15388/infedu.2013.02

Cited by 20 publications

(7 citation statements)

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“…RL and MDPs have been successfully applied to personalized learning systems [ 54 , 57 ], intelligent tutoring systems [ 8 , 65 ], adaptive serious games for ASD [ 33 ] and robot assisted therapy [ 72 ]. For instance, Bennane [ 12 ] automated the selection of the content of a tutoring system and its pedagogical approach to provide differentiated instruction. Similarly, Shawky and Badawi [ 57 ] used RL to build an intelligent environment to provide learners with suitable content as well as adapt to the learner’s evolving states.…”

Section: Related Workmentioning

confidence: 99%

Toward automatic motivator selection for autism behavior intervention therapy

Siyam

Abdallah²

2022

Univ Access Inf Soc

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Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

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Section: Related Workmentioning

confidence: 99%

Toward automatic motivator selection for autism behavior intervention therapy

Siyam

Abdallah²

2022

Univ Access Inf Soc

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“…The use of the intelligent multi-agents system in e-learning field allows to solve some pedagogic problems by taking advantage of some characteristics. Examples: adaptation of the courses of learning( [14]; [15]); the design of collaborative learning platform ( [16]; [17]); the individualization of the learning [18]; the support of the learners and the tutor.…”

Section: Mas and E-learningmentioning

confidence: 99%

Development of Intelligent Multi-agents System for Collaborative e-learning Support

Matazi¹,

Messoussi²,

Bellmallem³

et al. 2018

Bulletin EEI

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The aim of this paper is the introduction of intelligence in e-learning collaborative system. In such system, the tutor plays an important role to facilitate collaboration between users and boost less active among them to get more involved for good pedagogical action. However, the problem lies in the large number of platform users, and the tutor tasks become difficult if not impossible. Therefore, we used fuzzy logic technics in order to solve this problem by automating tutor tasks and creating an artificial agent. This agent is elaborate in basing on the learners activities, especially the assessment of their collaborative behaviors. After the implementation of intelligent collaborative system by using Moodle platform, we have tested it. The reader will discover our approach and relevant results.

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“…(2) collecting and sharing LOs; (3) metadata page; (4) repository homepage; (5) keeping users updated; (6) personal settings on the profile page. Learning personalisation and related issues have been very popular in scientific literature in recent years (Bennane 2013;Kim, Lee 2013;Walldén, Mäkinen 2014); (7) metadata editor; (8) implementation of user-generated metadata; and (9) adding descriptive metadata.…”

Section: Model Of the Quality-in-use Of Learning Repositoriesmentioning

confidence: 99%

New McEqls Fuzzy Ahp Methodology for Evaluating Learning Repositories: A Tool for Technological Development of Economy

Kurilovas

Vinogradova

Kubilinskienė

2015

Technological and Economic Development of Economy

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The paper aims to present a new methodology to evaluate the quality of features and functionality of learning object repositories (LORs). The quality of features and functionality of LORs is analysed in terms of engaging LOR users and content producers. Thus, it can be referred to as quality-in-use of LORs. This methodology consists of creation and consequent application of methods and the model for the quality-in-use of LORs. The model of the quality-in-use of LORs is presented in this paper. The methodology for evaluating the quality-in-use of LORs is based on the general MCEQLS (Multiple Criteria Evaluation of the Quality of Learning Software) approach to evaluate the quality of learning software. The essential part of the novel methodology is the application of improved Fuzzy AHP method to establish criteria weights of the quality-in-use of LORs. It is shown that the created methodology is suitable and stable for evaluating the quality of LOR features and its functionality. A more detail presentation is given on the results of the expert evaluation of the quality-in-use of three LORs that are most popular in Lithuania against the proposed methodology. The novelty of the presented research is achieved through the innovative instrument consisting of the model of the quality-in-use of LORs and the Fuzzy AHP method. The presented methodology could serve as a technological tool for decision making in education as well as in different areas of economy.

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Adaptive Educational Software by Applying Reinforcement Learning

Cited by 20 publications

References 0 publications

Toward automatic motivator selection for autism behavior intervention therapy

Toward automatic motivator selection for autism behavior intervention therapy

Development of Intelligent Multi-agents System for Collaborative e-learning Support

New McEqls Fuzzy Ahp Methodology for Evaluating Learning Repositories: A Tool for Technological Development of Economy

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