Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/105491
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Title: Enhancing student learning through mobile learning groups
Authors: Chan, HCB 
Fung, TT 
Issue Date: 2020
Source: 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 8-11 December 2020, Online, p. 99-105
Abstract: With the advent of mobile phones, particularly smartphones, there has been consideration interest in mobile e-learning. In particular, mobile phones provide an effective channel to complement existing channels to enhance the student learning experience. Coupled with artificial intelligence (e.g., chatbot), more innovative learning functions can be provided. In this paper, we make two contributions to the aforementioned development. First, we present an innovative AI5 model with five important learning elements: Inception, Interest, Instruction, Information and Inspiration, to facilitate mobile collaborative learning. Second, we present a mobile app prototype for students to form mobile learning groups. In each learning group, students can interact with one another and with a chatbot. Furthermore, the mobile app can facilitate the sharing of open educational resources.
Keywords: AI
Chatbot
E-learning
Mobile learning
OER
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-7281-6942-2 (Electronic)
978-1-7281-6943-9 (Print on Demand(PoD))
DOI: 10.1109/TALE48869.2020.9368416
Rights: ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication H. C. B. Chan and T. T. Fung, "Enhancing Student Learning Through Mobile Learning Groups," 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Takamatsu, Japan, 2020, pp. 99-105 is available at https://doi.org/10.1109/TALE48869.2020.9368416.
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