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Exploring the Utilization of the Big Data Revolution as a Methodology for Exploring Learning Strategy in Educational Environments

Introduction

Over the late 20th century and early 21st century, rapid advancements in technology have greatly modified what it means to be a competent citizen in contemporary society Today, technology permeates almost every sector of our lives. We cannot select a movie on our television without algorithms telling us which shows we should be interested in. Looking to buy a product on Amazon? Here are 20 other products consumers just like you have purchased. Let’s also not overlook the constant bombardment of advertising that is customized to our buying histories, online conversations, and internet connection habits. There is no question that industry has leveraged technology to offer personalized and directed services in a very effective, albeit somewhat intrusive manner.

The education sector has not been immune to these changes of course. More and more, new technologies are changing where and how we conduct instruction, engage in learning, and assess outcomes. The rise of online education impacts the geographic footprint of our student catchments. Gamification attempts to harness the motivation and flow states of commercially available games for the purposes of learning. Moreover, the vastness of the internet has caused us to rethink what we teach in the classroom and how we do so. Yet, in all of these changes within the enterprises of teaching and learning, what is still clear is that we in education have not kept up with many of the affordances of technology in the way that industry has. This is critically important as we think about the strategies that students need to acquire for both the current and future worlds they will inhabit and contribute to. It is no longer sufficient to think about how we strategically interact with and learn from off-line resources, or even isolated online resources. As a field, we need to understand how we can strategically interact with technology to allow us to think, learn, and produce in an increasingly complex world that is supported more and more by machine-generated algorithms that automate many of our daily tasks. Moreover, these strategies need to become a larger focal area of how we orchestrate instruction, provide supports for learning, and implement assessment practices that truly target the intended outcomes of education.

This is not to say that no research has been done in the area of technology-mediated strategy learning, instruction, and assessment. Certainly there are studies that have examined the use of learning strategies in the deployment of online learning environments (e.g., Knight, 2010; Tsai & Shen, 2009), computer-supported learning environments (e.g., Mathan & Koedinger, 2005; McNamara, Levinstein, & Boonthum, 2004; Moos & Azevedo, 2009), intelligent tutoring systems (e.g., Biswas, Roscoe, Jeong, & Sulcer, 2009; Koedinger & Aleven, 2007), and even game-based learning environments (Barab et al., 2009; Nietfeld, Hoffmann, McQuiggan, & Lester, 2008). The problem with many of these areas of work, however, is that they remain isolated from the larger enterprise of education. They lack the scale and scope necessary to generate a more generalizable understanding of its implementation, the necessary changes we must consider regarding what and how learning occurs, and how we continue to evolve our perspective on how learners will need to adjust as technology and our world landscape continue to change in the future. Education has yet to fully leverage the plethora of data available on students’ learning and strategic processes in instructional environments as a means to automate and leverage the adaptable affordances of technology for in situ teaching, learning, and assessment.

Because of the increasing complexity of the internet ecosystem today, the intent of this chapter is not to be a “how to” for engaging in the use of new forms and copious amounts of data for research on the strategic knowledge and processes that students employ during the learning process. Rather, its intent is to provide inspiration regarding where the future of research in education in general and strategic processing, in specific, needs to go. Further, this chapter seeks to heighten awareness of the potential affordances of this trajectory to better understand and support learning strategy research and pedagogy as the field moves forward. To this end, this chapter begins by examining the capture and analysis of data in various commercial applications, and how industry has successfully leveraged this data to better understand user engagement and shaping of consumer behaviors toward a targeted set of outcomes. Next, this chapter presents a summary of research from educational researchers’ attempts to generalize data strategies used in industry to inform teaching, learning, and assessment practices. Finally, the chapter concludes with a discussion of where we as a field need to move in terms of our research and the challenges the use of these techniques presents to the enterprise of education.

 
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