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 May 2012

Whitewashing the ML fence

One of the reasons behind some of the scepticism towards ML is the popular perception that this form of education leads to an over-reliance on the very resources that make it possible. Students, some argue, are reaching a stage where they aren't able to achieve anything for themselves without the use of their smartphone or tablet.

On one end of the spectrum, there are those who argue with horror that modern pupils do not bother to learn facts any more, because they can simply google anything up on the spot if and when they need the information. The implications are that ML students don't really know anything themselves -- they just know where to find someone (usually in the form of a digital resource) who does know.

On the other end of the spectrum, there are those who don't see a problem with the idea of not learning facts. We don't memorise all our friends' phone number or all the times and venues of the meetings we need to attend this month (we simply use a calendar or a diary to remind us), so why should education be any different?

Tom Sawyer whitewashing the fence.

When the problem is approached from a pedagogical point of view, most people take a middle ground. In many ways, developments in ML do not challenge modern attitudes to teaching -- in fact, they complement approaches to pedagogy that were already well established way before tablets and mobile phones were around.

In the 1960s, there was a radical change in instruction strategy: a shift from test-driven transmission teaching (conceived as a top-down, mental transfusion from the teacher to the pupil) to exploratory, hands-on and problem-centred approaches. The maxim became: learning is not the same as memorising. Encouraging students to develop useful learning skills became more important than hammering raw data into their brains. Aspects like observation, critical thinking and communication started making their way to the top of the curriculum.

This led to decades of debate among educational researchers. People like Paolo Friere and John Dewey argued against rote learning and for an active, dialectical, student-centred approach, while E.D. Hirsch, Jr. argued against what he termed their naturalist perspectives and for the core root of facts necessary for domain specific knowledge. Today, most teachers see these apparently conflicting positions as two sides of the same coin -- complementary aspects of the learning process. Developing knowledge is still an important part of education, but learning to develop that knowledge and what to do with it (i.e. the 4 Cs -- critical thinking, creative thinking, communicating, and collaborating) is also crucial. This aspect of education is what educational researchers call 'learning skills'.

As well as providing access to online repositories full of facts, ML resources allow students to develop a broad range of transferable learning skills as they classify, analyse and evaluate content (assessing the reliability of particular online resources, collating useful data from a range of platforms and learning how to adequately reference online and offline materials); they participate in problem solving activities; imagine, design and create their own resources; and collaborate with groups of people, setting goals and exercising team building skills.

Although ML is a very young field in education, its underlying principles are  not a radical departure from the teaching methods that educational researchers have been developing since the 1960s. Contrary to popular perception, in many ways ML complements and extends a long established pedagogical tradition.

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