by Korinn S. Ostrow, Neil T. Heffernan & Joseph Jay Williams - 2017
Background/Context: Large-scale randomized controlled experiments conducted in authentic learning environments are commonly high stakes, carrying extensive costs and requiring lengthy commitments for all-or-nothing results amidst many potential obstacles. Educational technologies harbor an untapped potential to provide researchers with access to extensive and diverse subject pools of students interacting with educational materials in authentic ways. These systems log extensive data on student performance that can be used to identify and leverage best practices in education and guide systemic policy change. Tomorrow�s educational technologies should be built upon rigorous standards set forth by the research revolution budding today.
Purpose/Objective/Research Question/Focus of Study: The present work serves as a call to the community to infuse popular learning platforms with the capacity to support collaborative research at scale.
Research Design: This article defines how educational technologies can be leveraged for use in collaborative research environments by highlighting the research revolution of ASSISTments (www.ASSISTments.org), a popular online learning platform with a focus on mathematics education. A framework described as the cycle of perpetual evolution is presented, and research exemplifying progression through this framework is discussed in support of the many benefits that stem from infusing EdTech with collaborative research. Through a recent NSF grant (SI2-SSE&SSI: 1440753), researchers from around the world can leverage ASSISTments� content and user population by designing and implementing randomized controlled experiments within the ASSISTments TestBed (www.ASSISTmentsTestBed.org). Findings from these studies help to define best practices within technology-driven learning, while simultaneously allowing for augmentation of the system�s content, delivery, and infrastructure.
Conclusions/Recommendations: Supplementing educational technologies with environments for sound, collaborative science can result in a broad range of benefits for students, researchers, platforms, and educational practice and policy. This article outlines the successful uptake of research efforts by ASSISTments in hopes of advocating a research revolution for other educational technologies.
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Korinn Ostrow
KORINN S. OSTROW is a Ph.D. candidate in Learning Sciences & Technologies at Worcester Polytechnic Institute. Her concentrations include applied educational statistics and cognitive psychology, with research interests in learning interventions, experimental methods at scale, learning analytics within adaptive technologies, and enhancing student motivation and engagement. She expects to graduate in 2018. Recent publications include �The Future of Adaptive Learning: Does the Crowd Hold the Key?� in the International Journal of Artificial Intelligence in Education, and �The Assessment of Learning Infrastructure (ALI): The Theory, Practice, and Scalability of Automated Assessment,� in the Proceedings of the 6th International Conference on Learning Analytics and Knowledge.
Neil Heffernan
NEIL T. HEFFERNAN is a Professor of Computer Science and the director of the Learning Sciences and Technologies Graduate Program at Worcester Polytechnic Institute. He is best known for creating ASSISTments. He has used the platform to conduct and publish two dozen randomized controlled experiments and now strives to expand the platform as a tool for others to do the same. In addition, he has published three dozen papers on predictive analysis, using large educational datasets to predict student performance on standardized state tests, affective states like boredom and frustration, and even college admission years later. He cares deeply about helping others learn about personalized learning in a methodically rigorous way.
Joseph Williams
JOSEPH JAY WILLIAMS is a Research Fellow in the Vice Provost for Advances in Learning Research Group at Harvard, where he conducts human-computer interaction and statistical machine learning research on digital education. This bridges his postdoctoral work at Stanford's Graduate School of Education conducting randomized experiments in MOOCs and Khan Academy to motivate learners through psychological interventions. He received his Ph.D. from UC Berkeley, where he focused on computational cognitive science and developed the subsumptive constraints account of why generating self-explanations enhances learning. He also developed the MOOClet Framework for designing intelligent digital lessons that personalize learning through randomized comparisons of crowdsourced content. Recent publications include "Generating Explanations at Scale with Learnersourcing and Machine Learning," in ACM Learning at Scale, and "Revising Learner Misconceptions without Feedback: Prompting for Reflection on Anomalous Facts," in Computer-Human Interaction.