Work

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

Year: 2016

Type: article

Abstract: 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.

Source: Journal of Learning Analytics

DOI: 10.18608/jla.2016.32.6

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

Institutions: SRI International, Carnegie Foundation for the Advancement of Teaching

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funder: National Science Foundation