Explainable Student Agency Analytics
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
Published inIEEE Access
DisciplineKoulutusteknologia ja kognitiotiedeHuman and Machine based Intelligence in LearningKoulutuksen tutkimuslaitosLearning and Cognitive SciencesHuman and Machine based Intelligence in LearningFinnish Institute for Educational Research
© Authors, 2021
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. ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN Search the Publication Forum2169-3536
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingThis work was supported by the Academy of Finland and related to the thematic research area Decision Analytics Utilizing Causal Models and Multiobjective Optimization (DEMO) (jyu.fi/demo), University of Jyväskylä, Finland, under Grant 3118
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