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Amy Ogan- Taking Educational Technology Worldwide: Challenging the Assumptions of Personalized Learning
The advent of widespread access to computing offers the promise to transform educational practices worldwide. At present, most massive open online courses (MOOCS) present practice and evaluation opportunities through static lists of multiple-choice questions. Future systems, however, will increasingly rely on sophisticated modeling of student knowledge to provide personalized instruction. Unfortunately, the few systems that currently employ such models tend to be developed for and evaluated in middle-class US schools – a very particular cultural context. This assumption is a broad generalization, as the cultural makeup of the US, and indeed the world, is changing dramatically. A central question educational technologies must confront is how these systems scale to diverse contexts with differing classroom practices and values. To investigate the cultural implications of educational technology use, I studied a math tutoring system that has been shown to be effective in the US. Through observation, interviews, learning assessments, and log data, I explored student and teacher use of the technology in school sites in Mexico, Costa Rica, Brazil, Portugal and Belgium with an international and local team of researchers. An example finding was the much greater propensity of students in our Latin American sites to collaborate closely, engaging in interdependently-paced work and very frequently conducting work away from their own computers. This threatens the very core assumption of personalized learning systems - that learning is individual – and rendered the sophisticated, personalized models inaccurate. The current hope is that as these systems go mainstream, the massive amounts of data collected will help researchers bootstrap and improve these models. However, the models built can only be as good as our assumptions about the data. In order to achieve the full benefits of personalized learning with MOOCs, we must employ human-computer interaction methodologies to help us rethink the underlying expectations for data collection and system design. For instance, our studies suggest that incorporating low-cost sensors can aid systems in better determining the context of use and active users. Technology designers can then implement multi-user versions of their personalization algorithms (e.g., knowledge-tracing, help-seeking, and adaptive scaffolding) that take advantage of this contextual understanding. As technologists increasingly become the gatekeepers of widespread education, further research is warranted that examines our assumptions about student identity, classroom practices, and cultural contexts of use.