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Promote Self-efficacy in Learning of Mobile App and Security with real-world Relevant Laboratory (Abstract Only)

Published: 17 February 2016 Publication History

Abstract

In this paper we present an innovative authentic learning approach of mobile security through real-world-scenario case studies. in computing field. The primary goal of this learning approach is to create an engagement and motivating learning environment that encourages all students in learning emerging technologies such as mobile software developments and its security concepts and practices. This approach provides students with hands-on laboratory practices on real-world mobile app developments and security. The laboratory consists of multiple modules covering Mobile App development, mobile security on cryptography, secure programming, network, database, Web app, mobile malware and spyware analysis. Each topic consists of a series of progressive sub-labs: a pre-lab, lab activities, and a student add-on post-lab. The preliminary feedback from students is positive. Students have gained hands-on real world experiences on mobile security with Android mobile devices, which also greatly promoted students' self-efficacy and confidences in their mobile security learning.

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cover image ACM Conferences
SIGCSE '16: Proceedings of the 47th ACM Technical Symposium on Computing Science Education
February 2016
768 pages
ISBN:9781450336857
DOI:10.1145/2839509
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 February 2016

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Author Tags

  1. authentic learning
  2. lab
  3. mobile
  4. real world

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SIGCSE '16
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SIGCSE '16 Paper Acceptance Rate 105 of 297 submissions, 35%;
Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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