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Collaborative Research: Learning Linkages: Integrating Data Streams of Multiple Modalities and Timescales: 1417997

Principal Investigator: Danielle McNamara
CoPrincipal Investigator(s):
Organization: Arizona State University

This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from Carnegie-Mellon University, Wested, Arizona State University, and Northwestern University will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. To accomplish this, they will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students’ understanding and capabilities over time.

Two forces are poised to transform research on learning. First, more and more student work is conducted on computers and online, producing vast amounts of learning-related data. At the same time, advances in computing, data mining, and learning analytics are providing new tools for the collection, analysis, and representation of these data. Together, the available data and analytical tools enable smart and responsive systems that personalize learning experiences for individual learners. The PIs aim to collect highly enriched data that go far beyond typical computer data capture, leveraging the latest tools for data processing to generate new insights about STEM teaching and learning. Working to maximize the potential while mitigating the risks of automated data collection and analysis, they will: (1) collect and integrate diverse sources of data including log files, videos, and written artifacts from across eight different two-week enactments of two different computer supported learning environments (one used in middle school math and one in high school science); and (2) compare analyses of log-file data with analyses of integrated datasets to understand the possibilities and limitations in using log-file data for assessment of student learning and proficiency. The collaborators expect their findings will inform both theories and practical recommendations applicable across a wide range of disciplines and settings.

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