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

Item

Title

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

Abstract/Description

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.

Author/creator

Krumm, Andrew E.

Beattie, Rachel

Takahashi, Sola

D'Angelo, Cynthia

Feng, Mingyu

Cheng, Britte H.

Date

2016

In publication

Journal of Learning Analytics

Volume

3

Issue

2

Pages

116-138

Resource type

en Research/Scholarly Media

Resource status/form

en Published Text

Scholarship genre

en Empirical

Language

en

Open access/full-text available

en Yes

Peer reviewed

en Yes

ISSN

1929-7750

DOI

10.18608/jla.2016.32.6

URL

Open Access Journal Webpage (Journal of Learning Analytics)

Citation

Krumm, A. E., Beattie, R., Takahashi, S., D’Angelo, C., Feng, M., & Cheng, B. (2016). Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement. Journal of Learning Analytics, 3(2), Article 2. https://doi.org/10.18608/jla.2016.32.6

Cited in

Measurement for Improvement in Education

Rights

Copyright (c) 2016 Journal of Learning Analytics

Item sets

Handbook Chapter 20 Citations

OB Citations - Measurement for Improvement in Education

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