Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item APPLICANT REACTIONS TO ARTIFICIAL INTELLIGENCE SELECTION SYSTEMS(2022) Bedemariam, Rewina Sahle; Wessel, Jennifer; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Practitioners have embraced the use of AI and Machine Learning systems for employeerecruitment and selection. However, studies examining applicant reactions to such systems are lacking in the literature. Specifically, little is known about how job applicants react to AI-based selection systems. This study assessed fairness perceptions of hiring decisions made by AIdriven systems and whether significant differences existed between different groups of people. To do so, a two-by-two experimental study where participants in a selection scenario are randomly assigned to a decision-maker condition (human vs AI) and outcome variability condition (hired vs rejected) was utilized. The results showed that the condition had a significant effect on the interactional justice dimension. The interaction effect of outcome and condition had an impact on job-relatedness, chance to perform, reconsideration opportunity, feedback perceptions, and interactional justice. The three-way interaction of outcome, race and condition influences general fairness reactions and emotional reactions. Given these findings, HR personnel should weigh the pros and cons of AI, especially towards applicants that are rejected.Item CHILDREN’S CONCEPTIONS OF FAIRNESS: THE ROLE OF MENTAL STATE UNDERSTANDING AND GROUP IDENTITY(2021) D'Esterre, Alexander; Killen, Melanie; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Children’s everyday experiences occur against a backdrop that is rich in social information andwhich requires decisions involving considerations about fairness, intentionality, and social groups. With age, children improve in their ability to utilize intentional information in their judgments and have been shown to demonstrate preferences for fairness over group benefit. What has not been fully investigated is how children coordinate and weight these considerations at different ages. Moreover, mistaken intentions and a tendency to benefit the in-group over others can be seen even in adulthood – suggesting that these issues are not so easily overcome and have the potential to affect the evaluations and behaviors of individuals more than have been previously considered. Research designed to carefully investigate the impact of these social and cognitive factors on children’s fairness concepts can provide insight into the ways in which biases may begin to form and potentially inform our understanding of the underlying mechanisms present in prejudicial attitudes. The present dissertation contains a series of three empirical papers that are designed to investigate children’s responses to unintentional and intentional transgressions based on their cognitive ability to infer beliefs of others and their relationship to the group identity of the target. Empirical Study 1 demonstrated the value of using a morally-relevant theory of mind measure embedded directly into the context when predicting children’s responses to unintentional and intentional transgressions. Empirical Study 2 investigated the ways in which children’s assessment of fair and unfair advantages were influenced by the group identity of the character who created the advantage. Empirical Study 3 explored the types of retributive justice that children would endorse in light of various types of intentional and unintentional transgressions, revealing differences based on group identity and the impact that the retributive justice would present to the functioning of the group. The results of these studies together suggest that children’s fairness concepts are heavily influenced by the context in which children find themselves and are far from static. Better understanding the relationship between these factors will provide increased insight into the ways in which prejudice and bias may develop in childhood and suggest potential areas for intervention.Item Strategy and bias in comprehension of multiple texts: How do readers with topic beliefs use strategies when reading controversial documents?(2014) Kim, Jong-Yun; Afflerbach, Peter P; Curriculum and Instruction; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Research on multiple text comprehension reveals key principles and elements of comprehension: readers' mental representation, cognitive text processing, and strategy use while reading multiple texts (Goldman, 2004; Rouet, 2006). However, many studies of multiple text comprehension fail to investigate the influence of reader bias. Grounded in both the literature on reading strategies and the social psychology literature on bias (e.g., Edwards & Smith, 1996), this study investigated how readers' topic beliefs influence comprehension strategies in relation to bias. The participants for this study were 15 undergraduate students, chosen as they represented three distinct topic beliefs related to the Israeli-Palestinian conflict. There were 5 pro-Israel, 5, pro-Palestine, and 5 neutral participants. While thinking-aloud, participants read two maps and five texts about Israeli settlements in the West Bank, and the ongoing Palestinian-Israeli conflict. The texts and maps were presented in the iMTC (internet-embedded Multiple-Text Comprehension measurement tool) environment (Kim & Cho, 2011). In addition, measures of participants' prior knowledge and topic beliefs were gathered, while their reading times and Internet searches were recorded by the iMTC. Participants' verbal reports were coded based on existing coding schemes for reading strategies (Goldman et al., 2012; Pressley & Afflerbach, 1995). Five families of strategy were determined: Considering text content, Acceptance and resistance, Monitoring, Evaluation, and Information need and search. The study has three major findings. First, initial belief differences between groups of different beliefs increased after reading, meaning that participants showed biased assimilation processing during reading. Second, the participants' biased processing was not detected in the three types of reading measures: reading times, reading orders, and Internet searches. Finally, the study found that participants with different topic beliefs showed different strategic patterns in relation to bias. In particular, acceptance and resistance distinguished the three participant groups' strategic processing. Participants accepted belief-consistent text information and resisted belief-inconsistent text information. In addition, three cases of participants' biased strategy use were qualitatively analyzed. The analyses demonstrated that participants' topic beliefs played a role in creating an interpretive framework that evaluated, accepted, or resisted information during reading. The findings, limitations, implications for future research and instructional practices are discussed.Item EFFECTS OF UNMODELED LATENT CLASSES ON MULTILEVEL GROWTH MIXTURE ESTIMATION IN VALUE-ADDED MODELING(2011) Yumoto, Futoshi; Hancock, Gregory R; Mislevy, Robert J; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fairness is necessary to successful evaluation, whether the context is simple and concrete or complex and abstract. Fair evaluation must begin with careful data collection, with clear operationalization of variables whose relationship(s) will represent the outcome(s) of interest. In particular, articulating what it is in the data that needs to be modeled, as well as the relationships of interest, must be specified before conducting any research; these two features will inform both study design and data collection. Heterogeneity is a key characteristic of data that can complicate the data collection design, and especially analysis and interpretation, interfering with or influencing the perception of the relationship(s) that the data will be used to investigate or evaluate. However, planning for, and planning to account for, heterogeneities in data are also critical to the research process, to support valid interpretation of results from any statistical analysis. The multilevel growth mixture model is a new analytic method specifically developed to accommodate heterogeneity so as to minimize the effect of variability on precision in estimation and to reduce bias that may arise in hierarchical data. This is particularly important in the Value Added Model context - where decisions and evaluations about teaching effectiveness are made, because estimates could be contaminated, biased, or simply less precise when data are modeled inappropriately. This research will investigate the effects of un-accounted for heterogeneity at level 1 on the precision of level-2 estimates in multilevel data utilizing the multilevel growth mixture model and multilevel linear growth model.