A1 Journal article (refereed)
Explainable Student Agency Analytics (2021)

Saarela, M., Heilala, V., Jääskelä, P., Rantakaulio, A., & Kärkkäinen, T. (2021). Explainable Student Agency Analytics. IEEE Access, 9, 137444-137459. https://doi.org/10.1109/access.2021.3116664

JYU authors or editors

Publication details

All authors or editors: Saarela, Mirka; Heilala, Ville; Jääskelä, Päivikki; Rantakaulio, Anne; Kärkkäinen, Tommi

Journal or series: IEEE Access

eISSN: 2169-3536

Publication year: 2021

Volume: 9

Pages range: 137444-137459

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/access.2021.3116664

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78049


Several studies have shown that complex nonlinear learning analytics (LA) techniques outperform the traditional ones. However, the actual integration of these techniques in automatic LA systems remains rare because they are generally presumed to be opaque. At the same time, the current reviews on LA in higher education point out that LA should be more grounded to the learning science with actual linkage to teachers and pedagogical planning. In this study, we aim to address these two challenges. First, we discuss different techniques that open up the decision-making process of complex techniques and how they can be integrated in LA tools. More precisely, we present various global and local explainable techniques with an example of an automatic LA process that provides information about different resources that can support student agency in higher education institutes. Second, we exemplify these techniques and the LA process through recently collected student agency data in four courses of the same content taught by four different teachers. Altogether, we demonstrate how this process—which we call explainable student agency analytics—can contribute to teachers’ pedagogical planning through the LA cycle.

Keywords: higher education (teaching); studies in an institution of higher education; students; human agency; evaluation; feedback; virtual learning environments; artificial intelligence; decision making

Free keywords: explainable artificial intelligence; decision making; higher education; student agency

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Preliminary JUFO rating: 2

Last updated on 2021-22-10 at 10:54