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An application of participatory action research in advising-focused learning analytics

Published: 07 March 2018 Publication History

Abstract

Advisors assist students in developing successful course pathways through the curriculum. The purpose of this project is to augment advisor institutional and tacit knowledge with knowledge from predictive algorithms (i.e., Matrix Factorization and Classifiers) specifically developed to identify risk. We use a participatory action research approach that directly involves key members from both advising and research communities in the assessment and provisioning of information from the predictive analytics. The knowledge gained from predictive algorithms is evaluated using a mixed method approach. We first compare the predictive evaluations with advisors evaluations of student performance in courses and actual outcomes in those courses We next expose and classify advisor knowledge of student risk and identify ways to enhance the value of the prediction model. The results highlight the contribution that this collaborative approach can give to the constructive integration of Learning Analytics in higher education settings.

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Cited By

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  • (2024)Community College Articulation Agreement Websites: Students’ Suggestions for New Academic Advising Software FeaturesCommunity College Journal of Research and Practice10.1080/10668926.2024.2356330(1-17)Online publication date: 4-Jun-2024
  • (2023)A Human-Centered Review of Algorithms in Decision-Making in Higher EducationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580658(1-15)Online publication date: 19-Apr-2023
  • (2023)Learning Dashboards for Academic Advising in PracticePracticable Learning Analytics10.1007/978-3-031-27646-0_4(55-75)Online publication date: 31-Mar-2023

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cover image ACM Other conferences
LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge
March 2018
489 pages
ISBN:9781450364003
DOI:10.1145/3170358
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2018

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Author Tags

  1. advising
  2. intervention evaluation
  3. participatory approaches to learning analytics

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LAK '18
LAK '18: International Conference on Learning Analytics and Knowledge
March 7 - 9, 2018
New South Wales, Sydney, Australia

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LAK '18 Paper Acceptance Rate 35 of 115 submissions, 30%;
Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

View all
  • (2024)Community College Articulation Agreement Websites: Students’ Suggestions for New Academic Advising Software FeaturesCommunity College Journal of Research and Practice10.1080/10668926.2024.2356330(1-17)Online publication date: 4-Jun-2024
  • (2023)A Human-Centered Review of Algorithms in Decision-Making in Higher EducationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580658(1-15)Online publication date: 19-Apr-2023
  • (2023)Learning Dashboards for Academic Advising in PracticePracticable Learning Analytics10.1007/978-3-031-27646-0_4(55-75)Online publication date: 31-Mar-2023

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