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Today, the use of computers in education is booming and the use of digital learning resources and educational websites invite educators and computer scientists to reflect more on the design of such tools. In fact, this paper outlines a number of criteria and recommendations that can guide and direct any teaching tool design be it campus-based or online (e-learning). This work is at the heart of this issue. It suggests examining academic, pedagogical, didactic and technological criteria to conduct this study which aims to develop an assessment and analysis grid of the quality of educational programs and applications. The approach adopted consists in addressing the specific and relevant factors of each assessment criterion. Then, the article explain the detailed structure of the grid. Finally, on the basis of the description given, all data are collected in the evaluation grid and its methods of use are discussed..

Abstract-Today, the use of digital educational resources in teaching and learning is considerably expanding. Such expansion calls educators and computer scientists to reflect more on the design of such products. However, this reflection exposes a number of criteria and recommendations that can guide and direct any teaching tool design be it campusbased or online (e-learning). Our work is at the heart of this issue. We suggest, through this article, examining academic, pedagogical, didactic and technical criteria to conduct this study which aims to evaluate the quality of digital educational resources. Our approach consists in addressing the specific and relevant factors of each evaluation criterion. We will then explain the detailed structure of the evaluation instrument used : "evaluation grid". Finally, we show the evaluation outcomes based on the conceived grid and then we establish an analytical evaluation of the state of the art of digital educational resources.

Today, Massive Open Online Courses (MOOCs) have the potential to enable free online education on an enormous scale. However, a concern often raised about MOOCs is the consistently high dropout rate of MOOC learners. Although many thousands of learners enroll on these courses, a very small proportion actually complete the course.This work is at the heart of this issue. It is interested in contributing on multi-agents systems and ontologies to describe the learning preferences and adapt educational resources to learner profile in MOOCs platforms. The primary aim of this work is to exploit the potential of multi-agents systems and ontologies to improve learners' engagement and motivation in MOOCs platforms and therefore reduce the drop-out rates.As part of the contribution of this work, the paper proposes a model of Multi-Agent System (MAS), based on ontologies for adapting the learning resources proposed to a learner in a MOOCs platform according to his learning preferences. To model an adequate online course, the determination of learner's preferences is done through the analysis of learner behavior relying on his indicator MBTI (Myers Briggs Type Indicator). The proposed model integrates the main functionalities of an intelligent tutoring system: profiling, updating of the profile, selection, adaptation and presentation of adequate resources. The architecture of the proposed system is composed on two main agents, four ontologies and a set of modules implemented. by integrating learner model and content adaptation concepts in order to promote engagement and motivation of such learners. Finally, the paper proceeds to describe the proposed architecture to implement the framework and concludes with future works.Stimulate Engagement and Motivation in MOOCs Using an Ontologies Based Multi-Agents System participant (learner) in four levels. These levels are described as Sub-Ontologies (SO): OLearner = {SOIdentity, SOPreferences , SOCapacity, SOHistory} (Fig. 2).