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Software Requirements to Support QoS in Collaborative M-Learning Activities

  • Didac Gil de La Iglesia
  • Marcelo Milrad
  • Jesper Andersson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7493)

Abstract

The use of collaborative activities in education has proven to be an effective way to enhance students’ learning outcomes by increasing their engagement and motivating discussions on the learning topics under exploration. In the field of Technology Enhanced Learning (TEL), the use of information and communication technologies has been extensively studied to provide alternative methods to support collaborative learning activities, combining different applications and tools. Mobile learning, a subset of TEL, has become a prominent area of research as it offers promising tools to enhance students’ collaboration and it provides alternative views for teaching and learning subject matter in relevant and authentic scenarios. While many studies have focused on the pedagogical opportunities provided by mobile technologies, fewer are the efforts looking at technological related aspects. Hardware and software issues in this field still remain as challenges that require a deeper level of study and analysis. This paper presents and discusses the findings of a deep analysis based on the outcomes of three mobile collaborative learning activities and their requirements. These results have helped us to identify a number of arising challenges that need to be addressed in order to warranty Quality of Service (QoS) in these collaborative M-learning activities. Moreover, the paper offers a view on current practices in M-learning activities, which evidences the lack of research addressing software engineering aspects in mobile collaborative learning.

Keywords

Mobile Device Mobile Technology Mobile Learning Software Requirement Ubiquitous Learning Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Didac Gil de La Iglesia
    • 1
  • Marcelo Milrad
    • 1
  • Jesper Andersson
    • 1
  1. 1.DFMLinnaeus UniversityVäxjöSweden

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