Welcome. We are Aarón Alzola Romero and Elton Barker, from the Open University's Department of Classical Studies. This blog is part of a broader research project exploring the uses (and abuses) of mobile learning in the Arts. Our aim is to examine mobile learning applications, assess their strengths and weaknesses (in terms of user interaction, contribution to learning outcomes, cost and popularity), identify areas of opportunity and challenges in their future implementation and assess the impact that mobile learning solutions have on the delivery of Arts courses.

Sunday, 27 November 2011

An interview with Professor Agnes Kukulska-Hulme

Agnes is one of the leading Mobile Learning exponents at the OU, with a particular interest in enquiry-based learning. (The IET wiki lists all the projects that IET have been engaged for more than 10 years.) She has been involved in a Landscape Study of mobile and wireless learning, which documents current practice within UK Further and Higher Educational Institutions of using mobile devices in teaching and learning. She has also worked on various European projects, such as: mobilearn, which experimented with providing visitors to art galleries with tablets to acquire additional information for themselves and share it with each other; motill, which has over 50 ‘reviews’ of various projects, focused on explaining them and making them accessible to others; and MASELTOV, which is exploring issues of social inclusion and empowerment of immigrants through use of mobile learning technologies and social network services.
Agnes identifies the cost of ML as the biggest obstacle to adoption. Within many institutions, including the OU, ML is still regarded as a non-essential additional activity that takes time and does not warrant the investment, particularly when many budgets are being squeezed. A further problem is that the people being asked to include ML in their teaching often have little experience of the technology themselves. Both points indicate the importance of embedding ML in learning processes, as well as outcomes, and in training.
One final point: current implementations of ML often rely on context aware environments. That is to say, places are set up specifically for promoting particular tasks. But the challenge is how do you get from there to a world in which you can interact with things on an everyday basis?