Mobile e-groupwork is playing an increasingly fundamental role in the field of mobile learning and cooperative work. One of the most outstanding problems in group management over time is the evolution of groups themselves, in terms of initial membership, profile analysis, as well as membership changes according to new interests, skills, expertise and goals. All such information can hardly be handled by individuals, especially in geographically distributed large teams. To cope with this problem, an architecture is proposed based on Deep Reinforcement Learning, a powerful machine learning technique for the automated management of resources.

MACHINE LEARNING-SUPPORTED MOBILE E-GROUPWORK MANAGEMENT

C De Castro
2020

Abstract

Mobile e-groupwork is playing an increasingly fundamental role in the field of mobile learning and cooperative work. One of the most outstanding problems in group management over time is the evolution of groups themselves, in terms of initial membership, profile analysis, as well as membership changes according to new interests, skills, expertise and goals. All such information can hardly be handled by individuals, especially in geographically distributed large teams. To cope with this problem, an architecture is proposed based on Deep Reinforcement Learning, a powerful machine learning technique for the automated management of resources.
2020
Mobile e-Learning
Mobile e-Groupwork
Artificial Intelligence
Deep Reinforcement Learning
Automated Resource Management.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/406413
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