Synthesis and design workshop: Research Priorities in Learning Analytics


PIs: Stephanie Teasley, Rada Mihalcea
University of Michigan Ann Arbor
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This workshop is funded through the “Dear Colleague Letter: Principles for the Design of Digital Science, Technology, Engineering, and Mathematics (STEM) Learning Environments (NSF 18-017).” Post-secondary educational institutions are being buffeted by three major transitions: changes in the nature of the competencies students need and want, changes in the demographic mix of people that they serve, and changes in the technologies and strategies available to build, measure, and communicate competence and expertise. Powerful new information technologies, unprecedented opportunities to gather and analyze large volumes of data, and new insights into how people learn, make it possible to imagine designing learning environments that make learning more productive, more affordable, and more gracefully adaptable to individuals throughout their lifetimes. The new field of learning analytics has made major advances in understanding how educational Big Data can produce insights to improve classroom practices. Yet, a number of critical questions remain unanswered in important areas to ensure all Americans receive the education they need to prosper in a modern economy. The aim of this workshop is to investigate the role of learning analytics in contributing to advances in technological learning environments.

The workshop will bring together interdisciplinary experts to articulate the state-of-the-art and propose research priorities for learning analytics in the coming decade. A central theme will be to explore new ways to use powerful tools in data science (machine learning, social network analysis, analytics and visualization of complex data, temporal, multi-scale and statistical models, integration of heterogeneous data, data scrubbing, wrangling and provenance tracking, data privacy and cybersecurity) to define competence, measure it, and build it using a rich array of new approaches to study learning. The workshop will also explore how the full power of these tools can be applied to the most critical challenges faced in learning analytics. The outcome of the workshop will be a clear definition of the highest priority research needs in learning analytics and a practical roadmap to guide public and private research support in these areas. The resulting white paper will provide insights to improve a wide range of learning environment contexts in in post-secondary education.

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