SMART Education Framework for Adaptation Content Presentation

Janati, Salma El; Maach, Abdelilah; Ghanami, Driss El

doi:10.1016/j.procs.2018.01.141

Cited by 27 publications

(17 citation statements)

References 7 publications

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“…Moreover, the educational process thanks to modern intelligent solutions can be carried out anytime, anywhere in the smart education context by using the intelligent devices [13]. In turn, from a managerial point of view, the development of smart technologies leads to the emergence of new educational strategies and models as well as development of smart and adaptive learning environment [14] in order to increase the quality of education. Different countries design various strategies of smart education development with the purpose to improve position in the global digital competitive environment [15].…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Exploration of Knowledge Engineering Paradigms for Smart Education: Techniques, Tools, Benefits and Challenges

2020

WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION

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Knowledge engineering paradigms (KEPs) deal with the development of intelligent systems in which reasoning and knowledge play pivotal role. Recently, KEPs receive increasing attention within the fields of smart education. Researchers have been used the knowledge engineering (KE) techniques, approaches and methodologies to develop a smart tutoring systems (STSs). The main characteristics of such systems are the ability of reasoning, inference and based on static and heuristic knowledge. On the other side, the convergence of artificial intelligence (AI), web science (WS) and data science (DS) is enabling the creation of a new generation of web-based smart systems for all educational and learning tasks. This paper discusses the KEPs techniques and tools for developing the smart educational and learning systems. Four most popular paradigms are discussed and analyzed namely; case-based reasoning, ontological engineering, data mining and intelligent agents. The main objective of this study is to determine and exploration the benefits and advantages of such computational paradigms to increase the effectiveness and enhancing the efficiency of the smart tutoring systems. Moreover, the paper addresses the challenges faced by the application developers and knowledge engineers in developing and deploying such systems. In addition to institutional and organizational aspects of smart educational technologies development and application

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Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Exploration of Knowledge Engineering Paradigms for Smart Education: Techniques, Tools, Benefits and Challenges

2020

WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION

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“…[10]. The adaptation of navigation is based on the links presented in the pages, these links allow the navigation between pages, personalize view in the page, and facilitate the adaptation process [11]. The last adaptation of collaboration support allows the communication between learners and the collaborative application using network-based communication [12].…”

Section: A Adaptive E-learning System (Ae-ls)mentioning

confidence: 99%

Learning Analytics Framework for Adaptive E-learning System to Monitor the Learner’s Activities

Janati¹,

Maach²,

Ghanami³

2019

IJACSA

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The adaptive e-learning system (AE-LS) research has long focused on the learner model and learning activities to personalize the learner's experience. However, there are many unresolved issues that make it difficult for trainee teachers to obtain appropriate information about the learner's behavior. The evolution of the Learning Analytics (LA) offers new possibilities to solve problems of AE-LS. In this paper, we proposed a Business intelligence framework for AE-LS to monitor and manage the performance of the learner more effectively. The suggested architecture of the ALS proposes a data warehouse model that responds to these problems. It defines specifics measures and dimensions, which helps teachers and educational administrators to evaluate and analyze the learner's activities. By analyzing these interactions, the adaptive e-learning analytic system (AE-LAS) has the potential to provide a predictive view of upcoming challenges. These predictions are used to evaluate the adaptation of the content presentation and improve the performance of the learning process.

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“…e-Learning (online training) is a perfect example with the digitization of learning. This distance learning technique eliminates the physical presence of a trainer [2]. It has many advantages and is become a part of the learner journey.…”

Section: Introductionmentioning

confidence: 99%