Scaling and Spacing Can Be Linked to Expectancy and Value Using Learning Analytics

• In Event: Feature Engineering for Learning Analytics in Higher Education Research on Motivation and Self-Regulation

Abstract

A positive correlation between motivation and learning success has been empirically identified for numerous disciplines and has been confirmed in meta-analyses across various fields (e.g. Schiefele et al., 1992). Despite this old and stable finding, however, little is known about why this relationship exists. Based on self-reports, some studies suggest that increased learning effort could be the explanatory mediating factor (e.g. Dayupay et al., 2022). A theoretical model that would causally frame the effect of motivation on learning behavior is Situated Expectancy-Value Theory (SEVT) (Eccles et al., 1987; Eccles & Wigfield, 2020). According to SEVT, expectancies and values can be regarded as components of motivation that affect the (learning) outcome. Contextual factors and behavior serve as frame and mediator. This results in a theoretical model as shown in Figure 1 below. However, there is a lack of studies that integrate learning behavior into the investigation of mediating explanations for the relationship between motivation and learning success in an objective and multidimensional way.

Authors

• Florian Berens, University of Tübingen

• Sebastian Hobert, TH Lübeck