Implemented and Tested Conception Proposal of Adaptation Model for Adaptive Hypermedia

Tmimi, Mehdi; Benslimane, Mohamed; Berrada, Mohammed; Ouazzani, Kamar

doi:10.3991/ijet.v14i02.8230

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…For its proper functioning, this model needs in addition of its own information, a set of data regarding the learner and the course content. And like the other models mentioned above, we have already studied and published our own adaptation model proposal [29], which we have developed in response to several problems and shortcomings of existing models, and also by proving its ability to be implemented that we illustrated in several screenshots of our developed web application.…”

Section: Adaptation Modelmentioning

confidence: 99%

Proposal of a complete model and architecture of an intelligent adaptive hypermedia

Tmimi¹

2019

IJATCSE

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This article is part of our work on the development of adaptive hypermedia systems. these systems have a minimal architecture composed of three models that we have already studied, developed and proposed in different papers. As the goal of our research is the development of our adaptive hypermedia system, we must first interconnect and merge these three models into one single model, and then design a global architecture describing all its processes, engines and technical implementation preferences.So in this article, we will start by interconnecting and merging all the three fundamental models. Then we will present our proposed global architecture. And finally, we will discuss all the basic processes and engines necessary for the functioning of our adaptive hypermedia system.

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Section: Adaptation Modelmentioning

confidence: 99%

Proposal of a complete model and architecture of an intelligent adaptive hypermedia

Tmimi¹

2019

IJATCSE

Self Cite

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“…These elements could be videos, articles, assignments, quizzes [11] etc. And as we proposed in our previous work [10], we are convinced that the domain model should remain as abstract as Innovative Technologies Laboratory, EST, USMBA, Morocco, mehditmimi@live.fr 2 Innovative Technologies Laboratory, EST, USMBA, Morocco, kaoutar.bouskine@gmail.com 3 Innovative Technologies Laboratory, EST, USMBA, Morocco, khartoch@gmail.com 4 Transmission Innovative Technologies Laboratory, EST, USMBA, Morocco, benslimane_mohamed@live.fr 5 Innovative Technologies Laboratory, EST, USMBA, Morocco, kamar_ouazzani@yahoo.fr possible, in the sense that it should just describe the course structures, knowledge and concepts, while ignoring all what is structure and layout of the content to be delivered to the learner. This vision of abstraction is not supported by the two famous reference models for adaptive hypermedia: AHAM [12] and MUNICH [13] because in their domain model they go beyond the context of knowledge and concepts by introducing also the structure of the page to be delivered to the learner in the same model.…”

Section: Introductionmentioning

confidence: 76%

“…So, as the main goal of our research is the design and development of an adaptive hypermedia [8] -which is one of the adaptive e-learning systems-, we proposed in our previous work both learner model [9] and adaptation model [10], then we oriented our research towards the domain model. In general, the domain model describes how the elements (knowledge and concepts) of the course are structured.…”

Section: Introductionmentioning

confidence: 99%

A Proposed Conception of the domain Model for Adaptive Hypermedia

Tmimi¹

2020

IJATCSE

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In the context of adaptive learning systems, we are working on designing and developing the domain model, which is one of the three fundamental models that constitute the main architecture of adaptive hypermedia systems.The domain model describes how the elements (knowledge and concepts) of the course are structured. Although there is a lot of domain model proposed by several reference models like (MUNICH and AHAM), and by E-learning technical standards like (SCORM and CMI5). These proposed models do not respect the context and go beyond the definition of the domain model in the sense that they introduce elements managed by other models that lead to a non-optimized structure for adaptation processes. So, in this paper we will present our proposal of the domain model that respect and remains in the context of the definition of the model and also based on a completely new structure optimized for adaptation processes that we named "objective-oriented structure".

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“…The purpose of the adaptation model is to deliver learning objects and activities to M-learners according to their learning features defined in the M-learner model [58], [59]. In our proposed model, the adaptation model consists of a Deep ANN algorithm that takes M-learner's features as input, processes them and based on their values, classify M-learners into different performance categories.…”

Section: M-learner Adaptation Modelmentioning

confidence: 99%

Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights

et al. 2020

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Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive Mlearning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse Mlearners thus helping M-learners in enhancing their study behavior.

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Implemented and Tested Conception Proposal of Adaptation Model for Adaptive Hypermedia

Cited by 5 publications

References 5 publications

Proposal of a complete model and architecture of an intelligent adaptive hypermedia

Proposal of a complete model and architecture of an intelligent adaptive hypermedia

A Proposed Conception of the domain Model for Adaptive Hypermedia

Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights

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