COMPUTERS ARE changing our world: how we work, how we shop, how we entertain ourselves, how we communicate, how we engage in politics, how we care for our health. The list goes on and on. But will computers change the way we learn? The short answer is yes. Computers are already changing the way we learn -and if you want to understand how, just look at video games. Not because the games that are currently available are going to replace schools as we know them any time soon, but because they give a glimpse into how we might create new and more powerful ways to learn in schools, communities, and workplaces -new ways to learn for a new Information Age. Look at video games because, while they are wildly popular with adolescents and young adults, they are more than just toys. Look at video games because they create new social and cultural worlds -worlds that help us learn by integrating thinking, social interaction, and technology, all in service of doing things we care about.We want to be clear from the start that video games are no panacea. Like books and movies, they can be used in
This paper provides a tutorial on epistemic network analysis (ENA), a novel method for identifying and quantifying connections among elements in coded data and representing them in dynamic network models. Such models illustrate the structure of connections and measure the strength of association among elements in a network, and they quantify changes in the composition and strength of connections over time. Importantly, ENA enables comparison of networks both directly and via summary statistics, so the method can be used to explore a wide range of qualitative and quantitative research questions in situations where patterns of association in data are hypothesized to be meaningful. While ENA was originally developed to model cognitive networks—the patterns of association between knowledge, skills, values, habits of mind, and other elements that characterize complex thinking—ENA is a robust method that can be used to model patterns of association in any system characterized by a complex network of dynamic relationships among a relatively small, fixed set of elements.
ABSTRACT:It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the special section on learning analytics and learning theory, we describe some critical problems in the analysis of large-scale data that occur when theory is not involved. These questions revolve around what variables a researcher should attend to and how to interpret a multitude of micro-results and make them actionable. We conclude our comments with a discussion of how the collection of empirical papers included in the special section, and the commentaries that were invited on them, speak to these challenges, and in doing so represent important steps towards theory-informed and theory-contributing learning analytics work. Our ultimate goal is to provoke a critical dialogue in the field about the ways in which learning analytics research draws on and contributes to theory.
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