AN ANALYTICAL NETWORK PROCESS MODEL TO RANK ALTERNATIVE LEARNING TECHNOLOGY INTERVENTIONS IN DEVELOPING COUNTRIES
American University of Sharjah (UNITED ARAB EMIRATES)
About this paper:
Appears in: EDULEARN14 Proceedings
Publication year: 2014
Conference name: 6th International Conference on Education and New Learning Technologies
Dates: 7-9 July, 2014
Location: Barcelona, Spain
Abstract:Education in developing countries is plagued with many problems that include lack of adequate infrastructure and capital, antiquated teaching practices, non-authentic curricula, ill-trained teachers, and high teacher-pupil ratios etc. A number of stake holders including governments, teachers’ associations, and donor agencies typically set the agenda for educational reforms in these countries. However, the use of learning technologies in such countries is often guided by what is currently in vogue in their more developed counterparts. This approach is flawed because the infrastructural constraints and contexts of developing countries are very different than those of developed countries. What is required is a systematic and well-founded evaluation of learning technology intervention in each country’s specific context. This paper presents such a decision-making model based on the Analytic Network Process (ANP) approach. ANP is a multi-criteria decision making technique that has a sound mathematical foundation, and can be used to make informed decisions in complex decision making environments. This paper develops a multi-stage model for a relative ranking of the various learning technology interventions in a developing country. The top level factors include learning design, adoption, political context and donor alignment. Each of the top level factors is divided into sub-factors. For example, learning design is divided into technology, content and pedagogy; the appropriateness of two learning designs in a developing country will vary along these dimensions. Adoption considers sub-factors like effort expectancy, social influence, facilitating conditions and performance expectancy. For example, some learning interventions may have lesser effort expectancy than others in a specific situation. Similarly, political context considers factors such as campaign promises, transparency, political party policy alignment, and political expediency; some learning interventions will be better aligned with the current political environment than others. Finally, donor alignment includes sub-factors such as access, gender-bias, sustainability, support for early grade literacy and numeracy, etc; these factors are based on current and long-term funding priorities of major donors in a country. Despite low computer and Internet penetration, one silver lining for many developing countries is the rapid rate of adoption of mobile phones and the associated mobile infrastructure. The price of mobile phones and access has fallen enough to allow media like recorded videos to be made available directly on the phone, and the price continues to fall due to economies of scale. Consequently, the ANP model is applied to develop a relative ranking of three mobile learning technology interventions in a developing country. The first is a mobile-based program to teach English to disadvantaged young women in semi-rural areas, the second intervention is a learning van that uses mobile satellite and mobile tablets to deliver learning to primary school children in remote schools, and the third is the SMILE program that uses inquiry-based peer-to-peer learning using mobile phones. Each approach has very different pedagogical approaches and uses different mobile technologies for implementation. Results of the application of the ANP model to rank the three interventions and subsequent implications are discussed.
Keywords: ANP, Developing Countries, Educational Interventions, Decision Making.