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New Learning—Old Methods? How E-research Might Change Technology-Enhanced Learning Research

Chapter

Abstract

That computer have changed how students learn and teachers teach—for better or worse—is well documented, amongst other sources by research reported in this book. In this chapter, we look at how computers (more generally, information technology) are beginning to change how learning research itself is conducted. An important driver of this change is the nature of the data that learning researchers analyze. While data used to be textual or numerical, they are becoming increasingly multimodal. And while data used to be gathered at one point in time or at a few distinct measurement points, learning researchers have now access to continuous streams of data. This raises for instance the basic question of when a research project is “finished.” Furthermore, IT is changing the methods of data analysis, the social orchestration of research, and the dissemination of research results—what used to be called publishing. For instance, research on a single project can now be distributed easily to involve many people with multiple specializations in many locations. Technology affords also to blur the distinction between “subjects” and researchers, enabling all kinds of new forms of participatory research. The ease of recording and storing data is the main driver of all these changes, but this convenience is a mixed blessing: an immediate problem is data deluge. Already today, only a small percentage of the data recorded in the modal design-based research study gets analyzed. Based on a review of how IT has been used to harness the deluge of data in other sciences, we describe new approaches to deal with massive amounts of complex data in the learning sciences (stemming, e.g., from log file recordings or from video recordings). Then, we will outline some aspects of research dissemination that are particularly relevant and important for the learning science research. We will conclude by discussing how new methods might affect the notion of theory in learning research and by outlining key challenges and a research agenda for the application of e-research methods in the learning sciences.

Keywords

Cloud Computing Educational Innovation Provenance Information Collaboration Script Educational Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Faculty of Education and Social WorkThe University of SydneySydneyAustralia

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