Design of educational scenarios of activities in a learning situation for online teaching

Through this work, based on research works concerning e-learning in particular works dealing with e_learning project management systems, adaptive hypermedia systems and decision-making systems on the one hand. Work on the impact of project-based learning on self-regulation in teaching and the design of pedagogical scenarios for classical education and in particular for modular online education on the other hand. Our work concerns the design and scripting of a modular online education by proposing different possible scenarios according to the nature of the learning or assessment activities linked to a learning situation without taking into account the nature of the discipline and the nature of the concept to be addressed.

Adaptive Hypermedia Systems

Adaptive hypermedia systems are systems that modify the different visible aspects based on the user profile. To provide this adaptation, the system is modeled according to a user model, which stores the information about each user. This information can include knowledge, interests, goals and tasks, background and skills, behavior, interaction preferences, individual traits, and context of the user. This chapter's goal is to introduce adaptive hypermedia systems fundamentals and trends. In this context, this chapter identifies some methods and techniques used to adapt the content, the presentation, and the navigation of the system. In the end, some applications (ELM-ART, Interbook, AHA!, AdaptWeb®) and trends (standardization, data mining, social web, device adaptation, and gamification) are exposed. As a result, this chapter highlights the importance of the improvement and the use of adaptive systems.

Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems

Modeling the learner in adaptive systems involves different information. There are several methods to manage the learner model. They do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this chapter is the management of the learner model based on multi-entity Bayesian networks. This chapter focuses on modeling the learner model in a dynamic and probabilistic way. The authors propose in this work the use of the notion of fragments and m-theory to lead to a Bayesian multi-entity network. The use of this Bayesian method can handle the whole course of a learner as well as all of its shares in an adaptive educational hypermedia.

A Comparative Study of the Modeling Techniques and Models of a Learner Model in Adaptive Hypermedia Educational Systems

The main objective of the learner model is to modify the interaction between the system and the learner in a dynamic way to address the needs of each learner on an individual basis. To obtain a complete learner model, we need the proper techniques and methods to initialize it and update it. This chapter present a comparative study of different adaptive hypermedia systems and the methods and techniques used in with them. This study lies within the range of modeling the learner in adaptive educational system as a conceptual modeling of the learner. Although there are several methods that deal with the learner model, like stereotypes methods or learner profile, they are likely unable to handle the uncertainty embedded in the dynamic modeling of the learner. The chapter aims studies different models and approaches to model the learner in an adaptive educational system and comes up with the most appropriate method based on the dynamic aspect of this model.

Adaptive Design for Educational Hypermedia Environments and Bio-Educational Adaptive Design for 3D Virtual Learning Environments

Adaptive learning environments design has been originally influenced by the adaptive learning environments model, which has been recently re-shaped by the bio-educational adaptive approach. Adaptive and bio-educational models share the common main idea that education should be adaptive. Since the 90’s the adoption of an adaptive educational point of view have been at the base of adaptive educational hypermedia systems, which design joins interest towards learners’ individual differences with adaptive learning environments research. Educational hypermedia systems have been overcome by the technology of 3D Virtual Learning Environments. Some emerging questions are related to the design criteria of adaptive learning environments. Which lessons learned from adaptive hypermedia systems design could be now applied to VLEs’ design? How a virtual learning environment should be designed to be adaptive? This research tries to answer to these questions by describing Federico 3DSU, an educational University 3D Virtual Learning Environment which has been designed with adaptive criteria, according to bio-educational model

The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture of their proposed system. The first step involves choosing a learning style model. The selection of this style is done by using a dedicated questionnaire answered by a learner. Then a modeling of the learner is completed based on his learning style. Finally, content that is to be presented to the learner is managed by a content generator module, depending on the model of the learner. Built on methods and techniques proposed for modeling and adaptation, the adaptive hypermedia system based on learning styles provides optimized adaptations. The authors' approach has been experimentally validated and the results are encouraging.

The open corpus challenge in eLearning

Learning has transcended into a life-long endeavor in the information age. It is no longer restricted to confines of formal classrooms. Consequently, a student is not restricted to traditional learning resources like teachers, textbooks or printed content. Digital resources available on the Internet form a very significant component of self-learning. Copious volumes of learning resources without legal barriers to self-learning reside in digital repositories, educational institution portals and on numerous websites. Learners wishing to utilize the web for personalized learning are faced with a daunting array of content to wade through and select the suitable ones to fulfill his/her learning objectives. Therefore, it is not a question of availability; it is one of relevance and suitability. Typically, in addition to time constraints, learners lack the expertise to screen content for effective eLearning. Adaptive hypermedia systems (AHSs) offer a path to harnessing this large volume of learning resources for personalized learning. This review paper provides a concise and coherent discussion about the evolution of AHSs along with the challenges that need to be addressed for effectively harnessing openly available educational resources referred to as open corpus resources (OCRs).