A Foundation For Educational Research at Scale: Evolution and Application

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\u27 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. 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

A Multifaceted Consideration of Motivation and Learning within ASSISTments

An approach to education gaining popularity in the modern classroom, adaptive tutoring systems offer interactive learning environments in which students can access immediate feedback and rich tutoring while teachers can achieve organized assessment for targeted interventions. Yet despite the benefits that these systems provide, a number of questions remain regarding the optimal inner workings of adaptive platforms. What is the recipe for optimal student performance within these platforms? What elements should be taken into consideration when designing these learning environments? Can facets of these platforms be harnessed to increase students’ motivation to learn and to improve both immediate and robust learning gains? This thesis combines work conducted over the past two years through versatile approaches toward the goal of enhancing student motivation and learning within the ASSISTments platform. Approaches considered include a) enhancing motivation and performance through altered feedback using hypermedia elements, b) instilling motivational messages alongside media enhanced content and feedback, c) allowing students to choose their feedback medium, thereby exerting control over their assignment, d) altering content delivery by interleaving skills to enhance solution strategy development, and e) establishing partial credit assessments to drive motivation and proper system usage while enhancing student modeling. After a brief introduction regarding the main tenants of this research, each chapter highlights a randomized controlled trial focused around one of these approaches. All studies presented have been conducted or are still running within ASSISTments. Much of this work has already been published at peer reviewed conference venues, some with stringent acceptance rates as low as 25% for full papers. Two of the studies presented here are second iterations of previously published work that are still in progress, and only preliminary analyses are available. A chapter on conclusions and future work is included to discuss the contributions that have been made to the Learning Sciences community thus far, and to briefly discuss potential directions for my continued research