Matching Items (3)
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- All Subjects: Random Forest
- All Subjects: Middle school students
- Creators: Broatch, Jennifer
Description
This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’ test scores as outcome variables and teachers’ contributions as random effects to ascribe changes in student performance to the teachers who have taught them. The VAMs teacher score is the empirical best linear unbiased predictor (EBLUP). This approach is limited by the adequacy of the assumed model specification with respect to the unknown underlying model. In that regard, this study proposes alternative ways to rank teacher effects that are not dependent on a given model by introducing two variable importance measures (VIMs), the node-proportion and the covariate-proportion. These VIMs are novel because they take into account the final configuration of the terminal nodes in the constitutive trees in a random forest. In a simulation study, under a variety of conditions, true rankings of teacher effects are compared with estimated rankings obtained using three sources: the newly proposed VIMs, existing VIMs, and EBLUPs from the assumed linear model specification. The newly proposed VIMs outperform all others in various scenarios where the model was misspecified. The second study develops two novel interaction measures. These measures could be used within but are not restricted to the VAM framework. The distribution-based measure is constructed to identify interactions in a general setting where a model specification is not assumed in advance. In turn, the mean-based measure is built to estimate interactions when the model specification is assumed to be linear. Both measures are unique in their construction; they take into account not only the outcome values, but also the internal structure of the trees in a random forest. In a separate simulation study, under a variety of conditions, the proposed measures are found to identify and estimate second-order interactions.
ContributorsValdivia, Arturo (Author) / Eubank, Randall (Thesis advisor) / Young, Dennis (Committee member) / Reiser, Mark R. (Committee member) / Kao, Ming-Hung (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
The purpose of this study was to compare perceptions of success and failure, attributions of success and failure, predictions of future success, and reports of out-of-class engagement in composition among middle school band students composing in open task conditions (n = 32) and closed task conditions (n = 31). Two intact band classes at the same middle school were randomly assigned to treatment groups. Both treatment groups composed music once a week for eight weeks during their regular band time. In Treatment A (n = 32), the open task group, students were told to compose music however they wished. In Treatment B (n = 31), the closed task group, students were given specific, structured composition assignments to complete each week. At the end of each session, students were asked to complete a Composing Diary in which they reported what they did each week. Their responses were coded for evidence of perceptions of success and failure as well as out-of-class engagement in composing. At the end of eight weeks, students were given three additional measures: the Music Attributions Survey to measure attributions of success and failure on 11 different subscales; the Future Success survey to measure students' predictions of future success; and the Out-of-Class Engagement Letter to measure students' engagement with composition outside of the classroom. Results indicated that students in the open task group and students in the closed task group behaved similarly. There were no significant differences between treatment groups in terms of perceptions of success or failure as composers, predictions of future success composing music, and reports of out-of-class engagement in composition. Students who felt they failed at composing made similar attributions for their failure in both treatment groups. Students who felt they succeeded also made similar attributions for their success in both treatment groups, with one exception. Successful students in the closed task group rated Peer Influence significantly higher than the successful students in the open task group. The findings of this study suggest that understanding individual student's attributions and offering a variety of composing tasks as part of music curricula may help educators meet students' needs.
ContributorsSchwartz, Emily, 1985- (Author) / Stauffer, Sandra L (Thesis advisor) / Tobias, Evan (Committee member) / Schmidt, Margaret (Committee member) / Broatch, Jennifer (Committee member) / Sullivan, Jill (Committee member) / Arizona State University (Publisher)
Created2014
Description
As threats to Earth's biodiversity continue to evolve, an effective methodology to predict such threats is crucial to ensure the survival of living species. Organizations like the International Union for Conservation of Nature (IUCN) monitor the Earth's environmental networks to preserve the sanctity of terrestrial and marine life. The IUCN Red List of Threatened Species informs the conservation activities of governments as a world standard of species' risks of extinction. However, the IUCN's current methodology is, in some ways, inefficient given the immense volume of Earth's species and the laboriousness of its species' risk classification process. IUCN assessors can take years to classify a species' extinction risk, even as that species continues to decline. Therefore, to supplement the IUCN's classification process and thus bolster conservationist efforts for threatened species, a Random Forest model was constructed, trained on a group of fish species previously classified by the IUCN Red List. This Random Forest model both validates the IUCN Red List's classification method and offers a highly efficient, supplemental classification method for species' extinction risk. In addition, this Random Forest model is applicable to species with deficient data, which the IUCN Red List is otherwise unable to classify, thus engendering conservationist efforts for previously obscure species. Although this Random Forest model is built specifically for the trained fish species (Sparidae), the methodology can and should be extended to additional species.
ContributorsWoodyard, Megan (Author) / Broatch, Jennifer (Thesis director) / Polidoro, Beth (Committee member) / Mancenido, Michelle (Committee member) / School of Humanities, Arts, and Cultural Studies (Contributor) / School of Mathematical and Natural Sciences (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05