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 in
IEEE AccessAuthors
Date
2021Discipline
Koulutusteknologia ja kognitiotiedeHuman and Machine based Intelligence in LearningKoulutuksen tutkimuslaitosLearning and Cognitive SciencesHuman and Machine based Intelligence in LearningFinnish Institute for Educational ResearchCopyright
© 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.
...


Publisher
Institute of Electrical and Electronics Engineers (IEEE)ISSN Search the Publication Forum
2169-3536Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/101363666
Metadata
Show full item recordCollections
Related funder(s)
Academy of FinlandFunding program(s)
Research profiles, AoF
Additional information about funding
This 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 3118License
Related items
Showing items with similar title or keywords.
-
Expectations for supporting student engagement with learning analytics : an academic path perspective
Silvola, Anni; Näykki, Piia; Kaveri, Anceli; Muukkonen, Hanni (Elsevier, 2021)There has been a growing interest in higher education to explore how learning analytics (LA) could be used to support student engagement. Providing actionable feedback with LA for students is an emerging area of research. ... -
Student agency analytics : learning analytics as a tool for analysing student agency in higher education
Jääskelä, Päivikki; Heilala, Ville; Kärkkäinen, Tommi; Häkkinen, Päivi (Taylor & Francis, 2021)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 ... -
“Sitting at the Stern and Holding the Rudder” : Teachers’ Reflections on Action in Higher Education Based on Student Agency Analytics
Heilala, Ville; Jääskelä, Päivikki; Saarela, Mirka; Kuula, Anna-Stina; Eskola, Anne; Kärkkäinen, Tommi (Palgrave Macmillan, 2022)Digital technologies in teaching and learning in higher education have the potential to enhance student agency. Student agency is an essential resource to nurture, especially at times when students face challenges emerging ... -
Finnish 5th and 6th grade students' pre-instructional conceptions of artificial intelligence (AI) and their implications for AI literacy education
Mertala, Pekka; Fagerlund, Janne; Calderon, Oscar (Elsevier, 2022)In the present paper, we report the findings of a qualitative survey study of 195 Finnish 5th and 6th grade students' pre-instructional conceptions of artificial intelligence (AI). An exploration of these initial conceptions ... -
Towards explainable interactive multiobjective optimization : R-XIMO
Misitano, Giovanni; Afsar, Bekir; Lárraga, Giomara; Miettinen, Kaisa (Springer Science and Business Media LLC, 2022)In interactive multiobjective optimization methods, the preferences of a decision maker are incorporated in a solution process to find solutions of interest for problems with multiple conflicting objectives. Since multiple ...