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Privacy Issues

When conducting research, we understand the issues related to institutional review and the safe conduct of research to protect the rights of subjects. However, the use of EDM/LA falls into the grey area between educational evaluation and research (Van Wei & Royakkers, 2004). Within the business sector, most practitioners are knowledgeable about the implications of the collection of data to shape consumer behavior, but can the same be said about the collection of student data when EDM/LA is implemented? How transparent should schools and educational institutions be with respect to the collection, aggregation, and use of data? While the use of student data for these purposes is often accepted as part and parcel of education in the 21st century (Land & Bayne, 2005), others have criticized the use of digital student data, questioning its legitimacy (Coll, Glassey, & Baileys, 2011). Dominguez, Chiluiza, Echeverria, and Ochoa (2015) have begun work to develop operable frameworks to deal with issues of student privacy within the architecture of EDM/LA, but there remains a great deal ofwork to do, especially as we move closer to using EDM/LA for real-time instructional adaptation and more targeted learner supports and feedback (Rodriguez-Triana et al., 2017).

Related to issues of surveilling students’ educational trajectories through data, Pariser (2011) argues that the results of these identification algorithms can inadvertently imprison learners in cages designed by their past choices. This phenomenon is not isolated to EDM/LA, but is prevalent across the entire Big Data enterprise. The term “filter bubble” refers to the results of Big Data algorithms that dictate what we encounter online, where users are increasingly unable to access or be exposed to content outside of the bubble that the filtering algorithm has determined is most applicable. In practice, filter bubbles essentially create a set of algorithmically generated restrictions that personalize content for users based on previous interactions without any deliberate user choice. A filter bubble, therefore, can cause users to get significantly less contact with contradicting viewpoints or exposure to the full range of opportunities for interaction, causing the user to potentially become intellectually isolated. Moreover, when used in the context of an educational setting, the automation of instructional scaffolds via EDM/LA methods may also prevent students from taking ownership of various strategies or even learning when specific strategies are appropriate, when to change an unsuccessful strategy and cause learners to become less metacognitively aware and self-regulated in their learning pursuits.

De-Siloing Educational Research

As the educational research community moves forward with the quest to demystify strategic processing, knowledge acquisition and application, and the optimal conditions needed to foster robust learning, we must come to terms with what is becoming an inescapable truth. In this highly complex and interconnected world, where data and information are everywhere, it is no longer sufficient to address educational research issues in isolated, disciplinary silos. The problems we face in education today require a multitude of perspectives and domains of expertise to identify, propose, and test solutions. To understand how strategic processes are employed and how instructional elements can shape and influence them, we must move toward a team-based approach, especially as we venture into the deep waters of Big Data and ED/LA. We need to begin to cultivate diverse teams of experts across education, the content disciplines, and computer science. The opportunity, the technology, and the methodologies to make big strides in both understanding and supporting learning exist, but in isolation, the best we can hope for is to chip away at an iceberg.

 
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