Open AccessPractical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement
Author(s) -
Andrew E. Krumm,
Rachel L. Beattie,
Sola Takahashi,
Cynthia D’Angelo,
Mingyu Feng,
Britte Haugan Cheng
Publication year - 2016
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.084
H-Index - 7
ISSN - 1929-7750
DOI - 10.18608/jla.2016.32.6
Subject(s) - operationalization , persistence (discontinuity) , learning analytics , computer science , data collection , data science , knowledge management , educational technology , mathematics education , psychology , sociology , engineering , social science , philosophy , epistemology , geotechnical engineering
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.