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To understand instruction during the spring 2020 transition to emergency distance learning (EDL), we surveyed a sample of instructors teaching undergraduate EDL courses at a large university in the southwest. We asked them how frequently they used and how confident they were in their ability to implement each of nine promising practices, both for their spring 2020 EDL course and a time when they previously taught the same course face-to-face (F2F). Using latent class analysis, we examined how behavioral frequencies and confidence clustered to form meaningful groups of instructors, how these groups differed across F2F and EDL contexts, and what predicted membership in EDL groupings. Results suggest that in the EDL context, instructors fell into one of three profiles in terms of how often they used promising practices: Highly Supportive, Instructor Centered, and More Detached. When moving from the F2F to EDL context, instructors tended to shift “down” in terms of their profile—for example, among F2F Highly Supportive instructors, 34% shifted to the EDL Instructor Centered profile and 30% shifted to the EDL More Detached Profile. Instructors who reported lower self-efficacy for EDL practices were also more likely to end up in the EDL More Detached profile. These results can assist universities in understanding instructors' needs in EDL, and what resources, professional development, and institutional practices may best support instructor and student experiences.

Self-regulated learning (SRL), the ability to set goals and monitor and control progress toward these goals, is an important part of a positive mathematical disposition. Within SRL, accurate metacognitive monitoring is necessary to drive control processes. Students who display this accuracy are said to be calibrated, and although calibration is a growing area of research within Educational Psychology, unanswered questions remain about the nature of calibration: how it should be measured, its role as a dynamic aspect of metacognition, and how best to improve it. This dissertation uses a rich source of data on student calibration and achievement within an online mathematics curriculum (ST Math) to approach these questions and present results on calibration as representative of a complex system of metacognition.This dissertation presents evidence that calibration is best represented as two separate monitoring processes, one for confidence and one for uncertainty; these processes can be operationalized through the measures of Sensitivity and Specificity. In Study 1, comparisons with other commonly used measures of calibration indicate that Sensitivity and Specificity have a relative robustness to most patterns of missing data and greater strength as predictors. Other commonly used calibration measures suffer greatly from missing data inherent in real-world patterns of question answering. Study 2 characterizes metacognitive monitoring as part of a dynamic system that varies depending on task. In this study, variance in calibration is associated with variance in performance gain within the same student across ST Math quizzes. Both Sensitivity and Specificity are predictors of this gain, but greater confidence when correct (Sensitivity) is more strongly associated with performance gains between quizzes than is greater uncertainty when incorrect (Specificity).Study 3 evaluates the potential of ST Math as a calibration intervention. After a year's practice and feedback with

Self-regulated learning (SRL), the ability to set goals and monitor and control progress toward these goals, is an important part of a positive mathematical disposition. Within SRL, accurate metacognitive monitoring is necessary to drive control processes. Students who display this accuracy are said to be calibrated, and although calibration is a growing area of research within Educational Psychology, unanswered questions remain about the nature of calibration: how it should be measured, its role as a dynamic aspect of metacognition, and how best to improve it. This dissertation uses a rich source of data on student calibration and achievement within an online mathematics curriculum (ST Math) to approach these questions and present results on calibration as representative of a complex system of metacognition.This dissertation presents evidence that calibration is best represented as two separate monitoring processes, one for confidence and one for uncertainty; these processes can be operationalized through the measures of Sensitivity and Specificity. In Study 1, comparisons with other commonly used measures of calibration indicate that Sensitivity and Specificity have a relative robustness to most patterns of missing data and greater strength as predictors. Other commonly used calibration measures suffer greatly from missing data inherent in real-world patterns of question answering. Study 2 characterizes metacognitive monitoring as part of a dynamic system that varies depending on task. In this study, variance in calibration is associated with variance in performance gain within the same student across ST Math quizzes. Both Sensitivity and Specificity are predictors of this gain, but greater confidence when correct (Sensitivity) is more strongly associated with performance gains between quizzes than is greater uncertainty when incorrect (Specificity).Study 3 evaluates the potential of ST Math as a calibration intervention. After a year's practice and feedback with

This study uses data from an urban school district to examine the relation between students' motivational beliefs about mathematics and high- versus low-stakes math test performance. We use ordinary least squares and quantile regression analyses and find that the association between students' motivation and test performance differs based on the stakes of the exam. Students' math self-efficacy and performance avoidance goal orientation were the strongest predictors for both exams; however, students' math self-efficacy was more strongly related to achievement on the low-stakes exam. Students' motivational beliefs had a stronger association at the low-stakes exam proficiency cutoff than they did at the high-stakes passing cutoff. Lastly, the negative association between performance avoidance goals and high-stakes performance showed a decreasing trend across the achievement distribution, suggesting that performance avoidance goals are more detrimental for lower achieving students. These findings help parse out the ways motivation influences achievement under different stakes.