A1 Journal article (refereed)
Student agency analytics : learning analytics as a tool for analysing student agency in higher education (2021)

Jääskelä, P., Heilala, V., Kärkkäinen, T., & Häkkinen, P. (2021). Student agency analytics : learning analytics as a tool for analysing student agency in higher education. Behaviour and Information Technology, 40(8), 790-808. https://doi.org/10.1080/0144929X.2020.1725130

JYU authors or editors

Publication details

All authors or editors: Jääskelä, Päivikki; Heilala, Ville; Kärkkäinen, Tommi; Häkkinen, Päivi

Journal or series: Behaviour and Information Technology

ISSN: 0144-929X

eISSN: 1362-3001

Publication year: 2021

Volume: 40

Issue number: 8

Pages range: 790-808

Publisher: Taylor & Francis

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1080/0144929X.2020.1725130

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/68635

Abstract

This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and meaningful learning. Within workplace learning research, agency is seen at the core of expertise. However, in the higher education field, agency is an empirically less studied phenomenon with also lacking coherent conceptual base. Furthermore, tools for students and teachers need to be developed to support learners in their agency construction. We study student agency as a multidimensional phenomenon centring on student-experienced resources of their agency. We call the analytics process developed here student agency analytics, referring to the application of learning analytics methods for data on student agency collected using a validated instrument. The data are analysed with unsupervised and supervised methods. The whole analytics process will be automated using microservice architecture. We provide empirical characterisations of student-perceived agency resources by applying the analytics process in two university courses. Finally, we discuss the possibilities of using agency analytics in supporting students to recognise their resources for agentic learning and consider contributions of agency analytics to improve academic advising and teachers' pedagogical knowledge.

Keywords: students; human agency; learning; machine learning

Free keywords: student agency; learning analytics; robust statistics

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

Preliminary JUFO rating: 2