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 proceedingConference contribution

1 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 languageEnglish (US)
Title of host publicationProceedings of Computing Conference 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1165-1168
Number of pages4
ISBN (Electronic)9781509054435
DOIs
StatePublished - Jan 8 2018
Event2017 SAI Computing Conference 2017 - London, United Kingdom
Duration: Jul 18 2017Jul 20 2017

Publication series

NameProceedings of Computing Conference 2017
Volume2018-January

Other

Other2017 SAI Computing Conference 2017
CountryUnited Kingdom
CityLondon
Period7/18/177/20/17

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

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