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Using Multiple Imputation To Mitigate The Effects Of Low Examinee Motivation On Estimates Of Student Learning, Kelly J. Foelber
Using Multiple Imputation To Mitigate The Effects Of Low Examinee Motivation On Estimates Of Student Learning, Kelly J. Foelber
Dissertations, 2014-2019
In higher education, we often collect data in order to make inferences about student learning, and ultimately, in order to make evidence-based changes to try to improve student learning. The validity of the inferences we make, however, depends on the quality of the data we collect. Low examinee motivation compromises these inferences; research suggests that low examinee motivation can lead to inaccurate estimates of examinees’ ability (e.g., Wise & DeMars, 2005). To obtain data that better represent what students know, think, and can do, practitioners must consider, and attempt to negate the effects of, low examinee motivation. The primary purpose …
The Effects Of A Planned Missingness Design On Examinee Motivation And Psychometric Quality, Matthew S. Swain
The Effects Of A Planned Missingness Design On Examinee Motivation And Psychometric Quality, Matthew S. Swain
Dissertations, 2014-2019
Assessment practitioners in higher education face increasing demands to collect assessment and accountability data to make important inferences about student learning and institutional quality. The validity of these high-stakes decisions is jeopardized, particularly in low-stakes testing contexts, when examinees do not expend sufficient motivation to perform well on the test. This study introduced planned missingness as a potential solution. In planned missingness designs, data on all items are collected but each examinee only completes a subset of items, thus increasing data collection efficiency, reducing examinee burden, and potentially increasing data quality. The current scientific reasoning test served as the Long …
The Effect Of Examinee Motivation On Value-Added Estimates, Laura M. Williams
The Effect Of Examinee Motivation On Value-Added Estimates, Laura M. Williams
Dissertations, 2014-2019
Questions regarding the quality of education, both in K-12 systems and higher education, are common. Methods for measuring quality in education have been developed in the past decades, with value-added estimates emerging as one of the most well-known methods. Value-added methods purport to indicate how much students learn over time as a result of their attendance at a particular school. Controversy has surrounded the algorithms used to generate value-added estimates as well as the uses of the estimates to make decisions about school and teacher quality. In higher education, most institutions used cross-sectional rather than longitudinal data to estimate value-added. …