A Foundation For Educational Research at Scale: Evolution and Application


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The complexities of how people learn have plagued researchers for centuries. A range of experimental and non-experimental methodologies have been used to isolate and implement positive interventions for students' cognitive, meta-cognitive, behavioral, and socio-emotional successes in learning. But the face of learning is changing in the digital age. The value of accrued knowledge, popular throughout the industrial age, is being overpowered by the value of curiosity and the ability to ask critical questions. Most students can access the largest free collection of human knowledge (and cat videos) with ease using their phones or laptops and omnipresent cellular and Wi-Fi networks. Viewing this new-age capacity for connection as an opportunity, educational stakeholders have delegated many traditional learning tasks to online environments. With this influx of online learning, student errors can be corrected with immediacy, student data is more prevalent and actionable, and teachers can intervene with efficiency and efficacy. As such, endeavors in educational data mining, learning analytics, and authentic educational research at scale have grown popular in recent years; fields afforded by the luxuries of technology and driven by the age-old goal of understanding how people learn. <br> <br> This dissertation explores the evolution and application of ASSISTments Research, an approach to authentic educational research at scale that leverages ASSISTments, a popular online learning platform, to better understand how people learn. Part I details the evolution and advocacy of two tools that form the research arm of ASSISTments: the ASSISTments TestBed and the Assessment of Learning Infrastructure (ALI). An NSF funded Data Infrastructure Building Blocks grant (#1724889, $494,644 2017-2020), outlines goals for the new age of ASSISTments Research as a result of lessons learned in recent years. Part II details a personal application of these research tools with a focus on the framework of Self Determination Theory. The primary facets of this theory, thought to positively affect learning and intrinsic motivation, are investigated in depth through randomized controlled trials targeting Autonomy, Belonging, and Competence. Finally, a synthesis chapter highlights important connections between Parts I & II, offering lessons learned regarding ASSISTments Research and suggesting additional guidance for its future development, while broadly defining contributions to the Learning Sciences community.

  • English
  • etd-042418-205859
Defense date
  • 2018
Date created
  • 2018-04-24
Resource type
Rights statement
Last modified
  • 2021-02-01


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