Technology as a gateway to a philosophy of the learning sciences

Technology has always played an important role in the facilitation and research of learning. In the last decade, we have seen digital, data-intensive, and adaptive technologies take center stage and proliferate rapidly. What is different about these technologies is that they enable facilitating learning for more and more diverse students than ever before and researching learning for more and more diverse scholars than ever before. This dissertation focuses on a subset of these technologies, described through six features as educational data infrastructures, which: (1) collect digital data and (2) adapt the learning experience (3) on a large scale (4) in higher education environments (5) to serve as infrastructural building blocks (6) for enabling, among other goals, research into learning. By analyzing the assumptions with which these technologies constrain and enable learning and research, we can use these technologies as a lens with which to investigate the assumptions about learning that inform the current and future state of the learning sciences. This work showcases findings from three distinct analyses to pursue this goal. The first is a first-ever epistemological analysis of the technologies and complementary scholarship from the first four years of the Learning At Scale conference to reveal that the scholarship concentrates on a narrow slice of available learning theory. I call this concentration within the range of available epistemological options the epistemological diversity gap. The second is an analysis of 11 interviews with builders of educational data infrastructures to reveal that the epistemological diversity gap is further corroborated, not random, and not something the builders set out to realize from the outset. The builders report a desire to build a productive science, navigate ethics responsibly, and achieve real human impact. They describe the building process as a constraint optimization exercise to find the path of least resistance given the epistemological foundations and code-friendliness of the available theories, the data-collection limitations of today's technologies, and the priorities of today's higher education institutions. The third is a historical account of the development of the learning sciences from an epistemological lens that highlights the main theoretical trends, dead ends, and research case studies that work across multiple epistemologies. The combined findings reveal that productively coordinating multiple epistemologies can contribute practically as well as philosophically to the learning sciences, creating an exciting scientific opportunity. Multi-epistemological coordination brings multiple areas for promising scientific contributions. A multi-epistemological focus can expand the scope of theories and models employed in existing educational data infrastructures, which in turn can expand the scope of epistemology in downstream research. This focus can also increase the probability of research and collaborations that explore new knowledge and also new insights into the epistemologies learning researchers leverage. To aid in this endeavor, the dissertation argues for establishing a field of philosophy of science of learning that helps with formalizing the methodologies, catalyzing new projects, and leveraging philosophical literature. Ultimately, the goal of the dissertation is to promote ways to utilize educational data infrastructures to advance the learning sciences via practical as well as philosophical contributions

Keywords: Education.
Advisor: Schwartz Daniel L. 
Thille Candace
Mitchell John C.