Self-regulated learning and treatment effect heterogeneity in educational interventions : a formal model and simulation study

dc.contributor.advisorYan, Veronica X.
dc.contributor.committeeMemberMuenks, Katherine
dc.contributor.committeeMembervon Hippel, Paul
dc.contributor.committeeMemberWhittaker, Tiffany
dc.creatorSchuetze, Brendan Alexander
dc.creator.orcid0000-0002-5210-6785
dc.date.accessioned2023-07-31T22:39:48Z
dc.date.available2023-07-31T22:39:48Z
dc.date.created2023-05
dc.date.issued2023-04-18
dc.date.submittedMay 2023
dc.date.updated2023-07-31T22:39:49Z
dc.description.abstractResearch has shown that many interventions or field experiments in education result in effects that are either non-significant or conventionally considered small. Perhaps more concerningly, the interventions with the largest sample sizes and strongest grant-funding support tend to show the smallest effects, with an average of only one-twentieth of a standard deviation of benefit across the entire sample. It appears that few, if any, educational interventions have large or uniform effects across student populations. Rather, it is commonly observed that only small sub-samples of students benefit from any one educational intervention. As a result, educational researchers have called for a “heterogeneity revolution” that will illuminate these sources of variation in treatment effects across sub-populations. Given the recency of these calls, there are many open questions about how to best model sources of variation. At present, most analyses of heterogeneity in education use relatively crude analyses, focused on demographic factors, such as socio-economic status or cultural background, as potential moderators of effects. Though demographic factors are not moderators of interventions in-and-of-themselves (they are proxy variables), there is little existing educational theory to explain this heterogeneity in terms of fundamental self-regulated learning processes known to underlie educational success. I propose a formal model of intervention effectiveness grounded in self-regulated learning called the Learning Production Function. This model allows for evaluating the joint effects of motivational, cognitive, and metacognitive interventions on populations of learners differing in terms of motivation, learning speed, metacognitive accuracy, and prior knowledge. Given this model, I conduct a simulation study. Using this simulation to model different intervention-student combinations, I answer questions such as: “which groups of learners would benefit from motivational interventions?” and “why do large effects of study strategies in laboratory studies fail to translate to self-regulated learning contexts?” This model advances the field’s understanding of different classes of educational interventions, why they work, who they benefit, and how to best combine interventions to help both at-risk and high-achieving student populations. Furthermore, this present dissertation improves upon educational theories by moving beyond “box and arrow” linear mediation models to more formalized understanding of the effects of educational interventions.
dc.description.departmentEducational Psychology
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/120714
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/47549
dc.language.isoen
dc.subjectEducational interventions
dc.subjectSelf-regulated learning
dc.subjectTreatment effect heterogeneity
dc.subjectFormal modeling
dc.subjectSimulation
dc.titleSelf-regulated learning and treatment effect heterogeneity in educational interventions : a formal model and simulation study
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentEducational Psychology
thesis.degree.disciplineEducational Psychology
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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