Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement

Andrew Edward Krumm
Rachel Beattie
Sola Takahashi
Cynthia D'Angelo
Mingyu Feng
Britte Cheng


This paper outlines the development of practical measures of productive persistence using digital learning system data. Practical measurement refers to data collection and analysis approaches originating from improvement science, and productive persistence refers to the combination of academic and social mindsets as well as learning behaviors that are important drivers of student success within the Carnegie Foundation for the Advancement of Teaching’s Community College Pathways Network Improvement Community. Strategies for operationalizing noncognitive factors using learning system data as well as approaches for using them as improvement measures are described.

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