ENHANCING EDUCATORS’ CREDIBILITY IN INTELLIGENT LEARNING ENVIRONMENTS

The deployment and use of Artificially Intelligent tools and techniques has proved to be effective and convenient, rendering them highly popular and desirable within any application. The benefits that such smart services provide are increasingly becoming popular within virtual learning environments as educators and learners continue to revert to such online portals during the academic process of higher education programmes. However, numerous educators have been sceptical of the inferences made by the machine learning techniques that implicitly at the background of the learning environment are monitoring the learner and collecting data to optimize the educational process as well as the learning experience. In an effort to enhance such a credibility we present an intelligent learning environment that justifies its decisions and propositions through an explainable interface that tracks back its conclusions to reasonable and plausible justifications. In this paper we present our research work together with our experiences in developing and deploying such a ground-breaking concept.

VLE Meets VW

The use of artificial intelligence (AI) within a learning environment has been shown to enhance the learning environment, improve its effectiveness, and enrich the entire educational experience. The next generation of intelligent learning environments incorporates the immersion of learners within virtual worlds while still offering the educational affordances and benefits of the online environment as a teaching medium. In this chapter, the current implementation of the virtual learning world (VLW) is presented bringing together a number of previous initiatives that integrated AI within a virtual learning environment (VLE) as well as the employment of a virtual world (VW) as learning environments. The realisation of the first VLW prototype provided numerous insights that provide valuable recommendations and significant conclusions to assist in taking the virtual learning environment to the next level.

A data-driven approach for the identification of misconceptions in step-based tutoring systems

Math errors are an important part of the learning process. For this reason, diagnosing them can help teachers and intelligent learning environments to choose the most appropriate type of assistance for the learner. In particular, the identification of learner misconceptions can be of special importance because they represent a misunderstanding of math concepts. In this context, this paper proposes the use of clustering algorithms to automatically identify algebra misconceptions from learners' algebra problem-solving steps in an intelligent learning environment. The computing platform is an intelligent tutoring system that assists students when solving linear equations step by step, by giving minimal and error feedback. The results showed that the model was able to identify some misconceptions already known in the literature, which illustrates the appropriateness of our approach. The automatic identification of misconceptions can help in the identification of new conceptual misunderstanding from large datasets of math problem solving, besides give valuable information for teachers and intelligent learning environments to adapt their instruction and assistance.

A New Semantic Description for Multimedia and Learning Objects

This chapter explains a new description of multimedia and intelligent learning objects. The authors mention the benefits of integrating multimedia content into e-learning. Then they develop the intelligent learning environments on the one hand and the pedagogical objects on the other hand. Then, they fix the new elements of their application profile; the latter is crowned with a semantic description in the form of an ontology. Finally, they detail the generation components of multimedia and intelligent learning objects.

A New Metadata Scheme for Multimedia and Intelligent Learning Objects

The purpose of this contribution is to improve the interoperability of educational and multimedia metadata in the context of a new application profile based on the LOM standard, without affecting their educational purpose. our metadata analysis led to the creation of new elements and new categories by strengthening the semantic representation of pedagogical objects and the different structures of multimedia documents, namely: spatial, temporal and hypermedia structures, this proposal also includes the characteristics of description visual. This contribution was essential given the absence of a metadata schema capturing multimedia and educational characteristics at the same time. the choice to gather descriptive elements based on the LOM standard, has proven to be wise since this standard is the most recognized and known in the field of eLearning. Throughout this article, we cite the advantages of pedagogical use of Multimedia, more specifically in eLearning. We then present intelligent learning environments on the one hand and educational objects on the other. Finally, we fix the new elements of our application profile, the latter is crowned with a semantic description in the form of an ontology.