A new approach and system for attentive mobile learning based on seamless migration

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 36; no. 1; pp. 75 - 89
• Main Author: Zhang, De-gan
• Format: Journal Article
• Language: English
• Published: Boston Springer US 2012; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Computation; Computer Science; Educational software; Failure; Fuzzy logic; Learning; Machines; Manufacturing; Mechanical Engineering; Migration; Networks; Neural networks; Online instruction; Operating systems; Processes; Tasks; Pervasive computing; Mobile learning; Fuzzy neural; Seamless mobility; Web-based migration; Attentive
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-010-0245-0

Seamless migration is one of pervasive computing applications. The function of seamless mobility is suitable for mobile services such as mobile Web-based learning. In this paper, we propose an approach that supports an attentive mobile learning paradigm. This mobile learning dynamically follows the user from place to place and machine to machine without user’s awareness or intervention by active service. This capability can be obtained by component-based smart system and agent-based migrating mechanism. To demonstrate the approach, the theoretical background of fuzzy-neural network for attentive service will be explained. The proposed fusion decision method is based on fuzzy-neural network which can make the input signal data to fuse better. Using online tuning, the fusion processing can be accelerated and the fusion belief degree can be improved. Description of mobile learning task and migrating granularity of the task is suggested. The design of the seamless migration mechanism is introduced. This includes solving several important sub-problems, such as transferring delay, transferring failure, and residual computation dependency. Our implemented system for attentive mobile learning based on seamless migration is presented. The validity comparison and evaluation of this kind of mobile learning paradigm is shown by experimental demos. This suggested attentive mobile learning paradigm based on seamless migration is useful and convenient to mobile learners.