Just as GPS guides navigation today, the UniSA location-based mobile learning framework provides an ‘internal GPS compass’ for guiding the identification, implementation and evaluation of strategies for contextually-based mobile learning and decision making about our practice. It will give us confidence that our professional practice incorporates the most recent evidence and understandings about location-based mobile learning that maximises student learning, engagement and achievement.

The framework (prototype April 2017) is for use primarily for academic staff designing a LBMLG in higher education but is equally as valid for student designers and K-12 teachers. It is relevant and shareable across disciplines and has low technical barriers for usage. It has long term maintainability and adaptability to new mobile technologies.

The framework will be delivered as both an eBook and hardcopy in 2017.

It incorporates 5 aspects, in which each is divided into 3 Strands.

Framework Overview

The framework will:

  • help academics to map their LBMLG’s against others through a design presence rubric
  • identify priorities for realistic short and longer term improvement goals
  • identify processes for achieving their goals
  • identify evaluation strategies to refine goals and actions

It will build the capacity of academics across all disciplines to plan, create and evaluate location-based mobile games used in teaching and learning programs to improve learning outcomes for students in a way that is strategic and manageable.

Using mobile devices requires different pedagogic approaches and didactic methods, a spatial cognitive competency and passion!

The framework (as of September 2016) is a prototype model only as is in development. It 
is independent of the online platform used to design and develop and host the LBMLG’s. Our university used the Mobile Learning Academy. It was chosen due to its applicability to higher education, ease of use, strong customer support and popularity; however there are several other online platforms (open source and commercial) that can be used to deliver similar outcomes.