Selected theses 2021-22

Individuals consistently judge others based on their faces, potentially biasing their decisions about others. While many studies reveal how people are subject to facial biases, there has been no empirical research on how, if at all, a baseball player's face biases baseball evaluators. I filmed nine different baseball pitchers throwing and photographed head shots of their faces, and then employed three survey experiments to understand how coaches' evaluations are influenced by features of these players' faces. I assessed which pitchers' faces were most associated with a set of four attributes. I added to the results from the first experiment by studying how attractive each pitcher's face was. Employing a principal component analysis, I determined that the first principal component explained roughly half of the variance in how the pitchers' faces looked among four attributes, and players were ranked on this principal component. Lastly, I analyzed how college baseball coaches evaluated four players taken from the first experiment when they saw their faces in videos of the four pitchers throwing compared to when they could not see their faces in the videos. Using an ordinary least squares regression, I found that, for two of the four players evaluated, baseball coaches rated the players significantly differently when they could see their faces in the pitching videos compared to when their faces were blurred out. My findings suggest that pitchers' faces can bias baseball scouts' evaluations of pitchers.

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With the rise of intelligent tutoring systems, otherwise known as cognitive tutors, education has seen a new and effective format for humanless, individualized instruction. However, most cognitive tutoring fails to implement information called biofeedback, or being able to sense human emotion as students learn. Additionally, cognitive tutors exist primarily within STEM fields and have not been studied in-depth within music education. Previous research has not created nor studied a cognitive tutor that utilizes biofeedback to provide personalized instruction in teaching rhythm. To add to the literature, I create an affective cognitive tutor as a web app that utilizes the webcam to teach drumming notation rhythms by pressing keys on the keyboard on specified beats as a proof of concept. I test 59 participants in a 45 minute, randomized controlled trial to answer two questions: first, does displayed affect correlate with rhythm performance; and second, does incorporating biofeedback in a cognitive tutor to teach music aid in the process of student learning? There are three main findings: the emotions of fear and surprise are correlated the strongest with rhythm performance, both correlated in the positive direction; incorporating biofeedback from a webcam into cognitive tutors for rhythm education had little effect on overall performance in the short-term; and a cognitive tutor within the field of music education seems to be successful and helpful to student learning.

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The existing literature on the effect of the COVID-19 pandemic on elementary and secondary school learning losses finds that students have experienced greater learning losses than would be expected in a normal year, but the specific cause of these losses remains an open question. Researchers have demonstrated the existence of the learning losses among American students and explored the moderating effect of remote learning at the district level. In this study, I complement prior analysis by using the SafeGraph phone-tracking database as a proxy for school openness. This provides two advantages: first, a more precise estimation of openness, without having to resort to using the nebulous term "hybrid" to encapsulate all states between open and remote learning, and secondly, allowing for a school-level analysis, which is more granular than the district level.

The result of my study is a group of linear regression analyses of learning loss regressed on school openness with demographic, fiscal, and state fixed effects of approximately 7,500 schools in 10 states. The results of the analyses indicate that schools which were more open during the 2020-21 school year experienced significantly smaller learning losses in math, but that openness did not have a significant effect on English language arts learning loss when controls were included. Additionally, there were significant racial, economic, and state fixed effects, with schools that have higher percentages of black and Hispanic students, as well as higher percentages of students eligible for free and reduced-price lunch, experiencing larger learning losses. These results are consistent with prior literature suggesting that math skills suffer more than reading during periods in which school is not in session, and that racial minorities and students of low socioeconomic status are disproportionately harmed by interruptions to learning.

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