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

Authors

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

DOI:

https://doi.org/10.18608/jla.2016.32.6

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.

Downloads

Published

2016-09-17

How to Cite

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), 116-138. https://doi.org/10.18608/jla.2016.32.6

Issue

Vol. 3 No. 2 (2016): Multimodal and 21st Century Skills Learning Analytics and Datasets

Section

Special section: Learning Analytics for 21st Century Competencies

License

Copyright (c) 2016 Journal of Learning Analytics

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish with this journal agree to the following terms:
  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Most read articles by the same author(s)