Predictive analytics for learning and usage of the plant sciences E-library

Gwen Nugent; Amy Kohmetscher; Houston Lester; Deana Namuth-Covert; Ashu Guru; Sushma Jolly

doi:10.1109/SAI.2017.8252237

Predictive analytics for learning and usage of the plant sciences E-library

Gwen Nugent, Amy Kohmetscher, Houston Lester, Deana Namuth-Covert, Ashu Guru, Sushma Jolly

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

This study examines learning and usage of the Plant Sciences E-Library (PASSEL, passel.unl.edu), a large international, open-source multidisciplinary learning object repository (7793 users from 14 countries). The analyses employ predictive analytics to isolate usage variables which predict learning from the instructional material. Specifically, the study focuses on student engagement as measured by total time online and time spent with different content modality material. This paper describes the analytic process, reports data on usage of learning object modules and module elements, identifies significant predictors of learning, and discusses future research directions.

Original language	English (US)
Title of host publication	Proceedings of Computing Conference 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1165-1168
Number of pages	4
ISBN (Electronic)	9781509054435
DOIs	https://doi.org/10.1109/SAI.2017.8252237
State	Published - Jan 8 2018
Event	2017 SAI Computing Conference 2017 - London, United Kingdom Duration: Jul 18 2017 → Jul 20 2017

Publication series

Name	Proceedings of Computing Conference 2017
Volume	2018-January

Other

Other	2017 SAI Computing Conference 2017
Country/Territory	United Kingdom
City	London
Period	7/18/17 → 7/20/17

Keywords

E-learning
e-learning usage statistics
learning analytics
learning object repositories

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Software
Artificial Intelligence

Access to Document

10.1109/SAI.2017.8252237

Cite this

Nugent, G., Kohmetscher, A., Lester, H., Namuth-Covert, D., Guru, A., & Jolly, S. (2018). Predictive analytics for learning and usage of the plant sciences E-library. In Proceedings of Computing Conference 2017 (pp. 1165-1168). (Proceedings of Computing Conference 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAI.2017.8252237

Predictive analytics for learning and usage of the plant sciences E-library. / Nugent, Gwen; Kohmetscher, Amy; Lester, Houston et al.
Proceedings of Computing Conference 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1165-1168 (Proceedings of Computing Conference 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nugent, G, Kohmetscher, A, Lester, H, Namuth-Covert, D, Guru, A & Jolly, S 2018, Predictive analytics for learning and usage of the plant sciences E-library. in Proceedings of Computing Conference 2017. Proceedings of Computing Conference 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1165-1168, 2017 SAI Computing Conference 2017, London, United Kingdom, 7/18/17. https://doi.org/10.1109/SAI.2017.8252237

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abstract = "This study examines learning and usage of the Plant Sciences E-Library (PASSEL, passel.unl.edu), a large international, open-source multidisciplinary learning object repository (7793 users from 14 countries). The analyses employ predictive analytics to isolate usage variables which predict learning from the instructional material. Specifically, the study focuses on student engagement as measured by total time online and time spent with different content modality material. This paper describes the analytic process, reports data on usage of learning object modules and module elements, identifies significant predictors of learning, and discusses future research directions.",

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Predictive analytics for learning and usage of the plant sciences E-library

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

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