Dissertation

Abstract

Educators and educational researchers constantly strive to find effective instructional methods that meet the needs of struggling students. There is a well-established relationship between self-regulated learning and academic achievement. Therefore, a great deal of work has been done studying interventions designed to develop self-regulated learning sub-processes including self-efficacy, motivation, help-seeking behavior, metacognition, self-monitoring, and causal attributions. Developing a growth mindset has also been a related focus for research and curriculum development. Intelligent tutoring systems have also been incorporated into K-12 education to help differentiate instruction and improve learning outcomes. Recently, there have been several empirical studies that have attempted to develop self-regulated learning with interventions delivered by intelligent tutoring systems.

 This dissertation is a compilation of randomized controlled trials designed to improve student learning. Each study introduced an intervention, based on prior work, designed to address at least one aspect of self-regulated learning and measure the effect on learning. All interventions were delivered using ASSISTments, a web-based intelligent tutoring system, to 7th grade students as part of their math class. Most of the studies were unsuccessful in producing significant changes in either self-regulation or learning therefore, failing to support the findings of prior research. Survey results suggest that students are reluctant to engage in certain self-regulated learning behaviors, like self-recording, because of the anxiety generated by incorrect responses. Based on the findings from all the studies, potential interventions for future research are recommended.  

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