ASCILITE Mobile Learning SIG
About the Mobile Learning SIG
The aim of the ASCILITE Mobile Learning SIG (ML-SIG) is to explore the intersection of mobile learning, new pedagogies, the Scholarship of Technology Enhanced Learning (SOTEL), Design-Based Research (DBR), and authentic learning.
Mobile device ownership is ubiquitous, leading many HE institutions to explore a BYOD approach to mobile learning. However, most Mlearning projects are device centric and focus upon repurposing content for delivery to small screens and substitution of pre-existing pedagogical strategies. The potential of mobile learning is to enable new collaborative connected pedagogies and professional portfolios.
The ML-SIG will explicitly explore the boundaries of current knowledge and approaches to mobile learning, and develop a global collaborative network of mobile learning researchers interested in exploring and implementing the frontiers of mobile learning. It will also explore the unique affordances of mobile devices for student-generated content and student-generated contexts via such technologies as collaborative media production and sharing, VR, AR, geolocative and contextual sensors, drones and wearable technologies.
Research in Learning Technology (RLT) MMR Special Collection Update 2019
Mobile Mixed Reality (MMR) is a rapidly developing technology that is being implemented in many different learning environments. A lot has changed already since the publication of our 2018 Special Collection on MMR, and the 2019 update to the 2018 special collection on MMR highlights the latest research in this domain. You’ll find full details here.
- ML-SIG blog
- Twitter hastag: #ascilitemlsig
- Webinar series archived on YouTube
- Conference Symposia
- Special issue of AJET (Australasian Journal of Educational Technology)
Join the SIG
Complete this simple sign-up form to be kept up to date about the ML-SIG’s plans and activities. Membership is open to ascilite members and non-members and there are no membership fees to join the SIG.
SIG Leader: Thom Cochrane
You will find profiles of the ML-SIG core members here.