Using the fused graphical lasso to explore the motivational self-system after a multimedia self-regulated learning training: a brief research report

TL;DR

This study explores how a multimedia learning intervention affects students' motivation in biology courses using network modeling.

Contribution

The study introduces Gaussian Graphical Modeling to examine motivational shifts in self-regulated learning interventions.

Findings

Network structures of motivation variables showed non-trivial differences between treatment and control groups.

The intervention activated the role of value and self-efficacy in student motivation.

Small sample size may limit the stability of estimated network models.

Abstract

The purpose of this study is to explore the effects of a randomized control trial designed to test the effect of a brief intervention used to improve self-regulated learning (SRL) in gateway biology courses using joint estimation of graphical models.

Students (N = 265; n = 136) from three sections of a hybrid-format introductory biology course were randomly assigned to participate in the multimedia science of learning to learn or a multimedia control condition. All participants completed a self-report battery of motivational measures. Course performance data was also collected.

Network structures of motivation variables were estimated in two sub-groups (Treatment and Control). These networks showed a high level of correspondence in the relative magnitudes of the edge weights, however there were non-trivial differences in the edge weights between groups that may be attributed to the treatment and differences in predictability. While these findings suggest meaningful differences in motivational structures, the relatively small sample size may limit the stability of the estimated network models. The SRL strategy based interventions may have positioned the students motivationally to approach the challenging exam through activating the role of value and self-efficacy in their learning.

Many of the ways analyses of typical intervention studies are conducted ignore the underlying complexity of what motivates individuals. This study provides preliminary evidence how Gaussian Graphical Modeling may be valuable in preserving the integrity of complex systems and examining relevant shifts in variations between motivational systems between groups and individuals.

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