Wolff Sarah M, Hilpert Jonathan C, Bernacki Matthew L, Greene Jeffrey A, Strong Christy
Department of Educational Psychology, Leadership and Higher Education, University of Nevada, Las Vegas, Las Vegas, NV, United States.
School of Education, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Front Psychol. 2025 Mar 11;16:1414563. doi: 10.3389/fpsyg.2025.1414563. eCollection 2025.
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 ( = 265; = 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.
本研究的目的是通过联合估计图形模型，探索一项随机对照试验的效果，该试验旨在测试一种用于提高基础生物学课程中自我调节学习（SRL）的简短干预措施的效果。
从混合形式的基础生物学课程的三个班级中随机抽取学生（n = 265；m = 136），让他们分别参与多媒体学习科学课程或多媒体对照课程。所有参与者都完成了一系列自我报告的动机测量。同时收集了课程成绩数据。
在两个亚组（实验组和对照组）中估计了动机变量的网络结构。这些网络在边权重的相对大小上显示出高度的一致性，然而，两组之间的边权重存在显著差异，这可能归因于治疗方法和可预测性的差异。虽然这些发现表明动机结构存在有意义的差异，但相对较小的样本量可能会限制估计网络模型的稳定性。基于自我调节学习策略的干预措施可能通过激活价值和自我效能在学习中的作用，使学生在动机上更有准备地应对具有挑战性的考试。
典型干预研究的许多分析方法忽略了激发个体动机的潜在复杂性。本研究提供了初步证据，表明高斯图形建模在保持复杂系统的完整性以及检查组间和个体间动机系统变化的相关转变方面可能具有价值。
