PROGRESSIVE CHALLENGE SELECTION AND PROGRESS ESTIMATION IN THE CONTEXT OF E-LEARNING
Mihail Petrov
Pages: 127-138
Published: 13 Oct 2019
Views: 885
Downloads: 74
Abstract: Not all students can progress equally, not every single educational using can be grasped with equal dept and speed. Usually, the level of understanding varies between students/classes or even an entire school. The common approach to this problem is to average the learning material based on the audience and to ensure that the concepts are understandable enough for the students in general. This approach is generally speaking a workable solution but does not fit the needs of every single student. The E-learning platform can provide a plethora of tools for managing the level of confides of the different users regarding the learning material. The main goal of this kind of education is not only to provide a useful interface between the student and the supervisor but to make the learning process more suitable for the individual needs of the student. The ability to ignore the learning paste of the other students and to configure your own activities is one of the selling points of the electronic education, so it is necessary to approach this problem with an appropriate methodology and tools for providing the proper experience for every single student that take part of online education program In this article, we are going to describe a concept for dynamically and progressively tailoring education program based on the student achievement in the context of the online learning platform UniPlayground used for training and analyzing the student’s behavior at Plovdiv University.
Keywords: elearning, intelligent agents, itl, virtual education space, behavior analysis
Cite this article: Mihail Petrov. PROGRESSIVE CHALLENGE SELECTION AND PROGRESS ESTIMATION IN THE CONTEXT OF E-LEARNING. Journal of International Scientific Publications: Educational Alternatives 17, 127-138 (2019). https://www.scientific-publications.net/en/article/1001978/
Download full text
Back to the contents of the volume
© 2024 The Author(s). This is an open access article distributed under the terms of the
Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This permission does not cover any third party copyrighted material which may appear in the work requested.
Disclaimer: The opinions and claims presented in this article are solely those of the authors and do not necessarily reflect the views of their affiliated organizations, the publisher, editors, or reviewers.