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2021 | OriginalPaper | Chapter

Collaborative Learning Platform Using Learning Optimized Algorithms

Authors : Beatriz Flamia Azevedo, Yahia Amoura, Gauhar Kantayeva, Maria F. Pacheco, Ana I. Pereira, Florbela P. Fernandes

Published in: Optimization, Learning Algorithms and Applications

Publisher: Springer International Publishing

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Abstract

Aware that the lack of mathematical knowledge and skills is a major problem for the development of a modern, inclusive and informed society, the MathE partnership has developed a tool that is aimed at bridging the gap that moves students away from courses that rely on a mathematical core. The MathE collaborative learning platform offers higher education students a package of scientific and pedagogical resources that allow them to be active agents in their learning pathway, by self-managing their study. The MathE platform is currently being used by a significant number of users, from all over the world, as a tool to support and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills and therefore increasing their confidence and capacities. In order to enhance this platform, a visual representation of the performance of the students is already implemented, based on the recorded performance historic data for each student. This paper contains a literature review about the implementation of data mining techniques in education, followed by a description of the features of the MathE learning system and suggestions of data parameters to support the improvement of the students’ performance. Future work includes the application of optimization and learning algorithms so that the MathE platform will have a dynamical structure and act as a virtual tutor for the users.

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Title: Collaborative Learning Platform Using Learning Optimized Algorithms
Authors: Beatriz Flamia Azevedo
Yahia Amoura
Gauhar Kantayeva
Maria F. Pacheco
Ana I. Pereira
Florbela P. Fernandes
Publisher: Springer International Publishing
Book: Optimization, Learning Algorithms and Applications
Print ISBN: 978-3-030-91884-2

Electronic ISBN: 978-3-030-91885-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-91885-9_52

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