Matching Items (2)
Description
Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, in press). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown. If the apparent strengths of DBNs are to be leveraged, then the body of literature surrounding their properties and use needs to be expanded upon. To this end, the current work aimed at exploring the properties of DBNs under a variety of realistic psychometric conditions. A two-phase Monte Carlo simulation study was conducted in order to evaluate parameter recovery for DBNs using maximum likelihood estimation with the Netica software package. Phase 1 included a limited number of conditions and was exploratory in nature while Phase 2 included a larger and more targeted complement of conditions. Manipulated factors included sample size, measurement quality, test length, the number of measurement occasions. Results suggested that measurement quality has the most prominent impact on estimation quality with more distinct performance categories yielding better estimation. While increasing sample size tended to improve estimation, there were a limited number of conditions under which greater samples size led to more estimation bias. An exploration of this phenomenon is included. From a practical perspective, parameter recovery appeared to be sufficient with samples as low as N = 400 as long as measurement quality was not poor and at least three items were present at each measurement occasion. Tests consisting of only a single item required exceptional measurement quality in order to adequately recover model parameters. The study was somewhat limited due to potentially software-specific issues as well as a non-comprehensive collection of experimental conditions. Further research should replicate and, potentially expand the current work using other software packages including exploring alternate estimation methods (e.g., Markov chain Monte Carlo).
ContributorsReichenberg, Raymond E (Author) / Levy, Roy (Thesis advisor) / Eggum-Wilkens, Natalie (Thesis advisor) / Iida, Masumi (Committee member) / DeLay, Dawn (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this mixed-methods study, I sought to design and develop a test delivery method to reduce linguistic bias in English-based mathematics tests. Guided by translanguaging, a recent linguistic theory recognizing the complexity of multilingualism, I designed a computer-based test delivery method allowing test-takers to toggle between English and their self-identified dominant language. This three-part study asks and answers research questions from all phases of the novel test delivery design. In the first phase, I conducted cognitive interviews with 11 Mandarin Chinese dominant speakers and 11 Spanish speaking dominant undergraduate students while taking a well-regarded calculus conceptual exam, the Precalculus Concept Assessment (PCA). In the second phase, I designed and developed the linguistically adaptive test (LAT) version of the PCA using the Concerto test delivery platform. In the third phase, I conducted a within-subjects random-assignment study of the efficacy the LAT. I also conducted in-depth interviews with a subset of the test-takers.
Nine items on the PCA revealed linguistic issues during the cognitive interviews demonstrating the need to improve the linguistic bias on the test items. Additionally, the newly developed LAT demonstrated evidence of reliability and validity. However, the large-scale efficacy study showed that the LAT did not appear to make a significant difference in scores for dominant speakers of Spanish or dominant speakers of Mandarin Chinese. This finding held true for overall test scores as well as at the item level indicating that the LAT test delivery system does not appear to reduce linguistic bias in testing.
Additionally, in-depth interviews revealed that many students felt that the linguistically adaptive test was either the same or essentially the same as the non-LAT version of the test. Some participants felt that the toggle button was not necessary if they could understand the mathematics item well enough. As one participant noted, “It's math, It's math. It doesn't matter if it's in English or in Spanish.” This dissertation concludes with a discussion about the implications for test developers and suggestions for future direction of study.
ContributorsClose, Kevin (Author) / Zheng, Yi (Thesis advisor) / Amrein-Beardsley, Audrey (Thesis advisor) / Anderson, Kate (Committee member) / Arizona State University (Publisher)
Created2021