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Explainable Student Agency Analytics
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DOI：10.1109/ACCESS.2021.3116664.png)
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Abstract
En 中文
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:
Education
Planning
Data models
Licenses
Couplings
COVID-19
Decision making
Explainable artificial intelligence
decision making
higher education
student agency
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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