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Students’ Adoption of Learner Analytics

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Adoption of Data Analytics in Higher Education Learning and Teaching

Part of the book series: Advances in Analytics for Learning and Teaching ((AALT))

Abstract

Learner analytics is an emerging learning and teaching tool which visualises individualised learning data to student users typically via a dashboard or similar platform. In a learner analytics model data are communicated to students directly and often without tutor contact; sense-making is assumed to occur through digital and algorithmic intermediation. Through the collection and analysis of qualitative and quantitative student data, this chapter argues that students’ propensity to adopt analytics is influenced by their existing relationship with data, their discipline, their perception of self and the connections between these factors and the following four benefits: analytics for orientating oneself academically; analytics for improved organization and management; analytics for signposting to support; analytics for fun.

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Notes

  1. 1.

    Analytics was available on students’ individual log-ins to the virtual learning environment by module compared to the class average. Module grades compared to the class average were also included. Automatically captured data from the virtual learning environment were updated every night with the previous day’s activities. Grades data were updated when new grades had been validated on the student’s record.

  2. 2.

    Students may log the number of hours they have studied and measure this relative to their notional workload or track the progress they feel they have made in a module as a percentage. Students can also capture how difficult and stimulating they find each module and their progress relative to eight predefined ‘personal goals’; ‘Money & Finance’, ‘Physical wellbeing’, ‘Employability’, ‘Mental Well-being’, ‘Relationships’, ‘Fun & Social’, ‘Academia’ and ‘Living Space’. Self-captured data update instantly.

  3. 3.

    The project also had to comply with General Data Protection Regulations which are significant if not somewhat ambiguous in the space of operational learning analytics.

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Acknowledgements

With thanks to the students in this research for their participation and candid commentary. With gratitude to Northumbria University colleagues who supported the Educational Analytics project.

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Authors and Affiliations

  1. Northumbria University, Newcastle upon Tyne, UK

    Carly Palmer Foster

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  1. Carly Palmer Foster
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Correspondence to Carly Palmer Foster .

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Editors and Affiliations

  1. Curtin University, Perth, Australia

    Dirk Ifenthaler

  2. University of Mannheim, Mannheim, Germany

    Dirk Ifenthaler

  3. Curtin Learning and Teaching, Curtin University, Perth, Australia

    David Gibson

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Foster, C.P. (2020). Students’ Adoption of Learner Analytics. In: Ifenthaler, D., Gibson, D. (eds) Adoption of Data Analytics in Higher Education Learning and Teaching. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-47392-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-47392-1_8

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  • Print ISBN: 978-3-030-47391-4

  • Online ISBN: 978-3-030-47392-1

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