Doctor of Philosophy, The Ohio State University, 2023, Educational Studies

As mobile learning (m-learning) becomes increasingly prevalent in education, it is recognized for its potential to enhance the overall quality of teaching and learning. Despite the many benefits, m-learning apps often experience low retention rates, which directly impede learners' benefit from using them and cause a waste of resources in app design, development, and maintenance. To investigate the critical factors influencing learners' acceptance of m-learning outside the classroom, this study introduced a novel model, the Mobile Learning Acceptance Determination (mLAD) Model, based on the Technology Acceptance Model and the updated DeLone and McLean Information System Success Model. Through the mLAD model, the study identified the critical app quality factors that influence learners' acceptance of m-learning. The moderating effects of the type of m-learning apps on learners' acceptance of m-learning were also revealed. An online questionnaire named the m-Learning Acceptance Questionnaire (mLAQ) was developed and disseminated through Amazon Mechanical Turk. A total of seven hundred forty-seven adult learners in the U.S. participated in the study. The descriptive statistical results of the examined factors revealed that m-learning apps available in the market demonstrate high mobility and content quality. Still, their interactivity and service quality could be improved. Furthermore, the results of the structural equation modeling analysis indicated that learners' two beliefs, perceived usefulness, and perceived ease of use, are the two essential determinants of learners' intention to use m-learning apps outside the classroom. Quality factors, such as content quality, interface design, mobility, and service quality, are the antecedents of learners' m-learning acceptance, given that they significantly and directly influence perceived usefulness and ease of use and indirectly impact learners' intention to use m-learning apps through learners' two beliefs. Through (open full item for complete abstract)

Committee: Ana-Paula Correia (Advisor); Minjung Kim (Committee Member); Richard J Voithofer (Committee Member) Subjects: Education; Educational Software; Educational Technology; Information Systems; Information Technology; Technology

Ph.D., Antioch University, 2024, Antioch Seattle: Counselor Education & Supervision

Supporting the development of an antiracist identity in counselors could facilitate change toward equity, justice, and opportunity within the counseling profession and increase awareness of white counselors in working with clients of color. Understanding obstacles to and enablers of antiracist attitudes in white women counselors holds the potential to bring change to the profession as a whole, given their position in the majority. This quantitative study used instruments to assess white racial affects of white fear, anger, and guilt along with antiracist self-efficacy as influencing antiracist accountability in a sample of white women counselors in the United States (N = 64). White fear was shown to have a moderate inverse relationship with antiracist accountability, and white anger was demonstrated to have a moderate positive relationship with antiracist accountability. White guilt did not show a statistically significant influence. Both white fear and white anger were mediated by antiracist self-efficacy, and a strong positive relationship was shown between antiracist self-efficacy and antiracist accountability. Implications for the counseling profession, the practice of counseling, and counselor education are presented. This dissertation is available in open access at AURA (https://aura.antioch.edu) and OhioLINK ETD Center (https://etd.ohiolink.edu).

Committee: Shawn Patrick (Committee Chair); Stephanie Thorson-Olesen (Committee Member); Katherine Fort (Committee Member) Subjects: Behavioral Sciences; Counseling Education; Mental Health

Master of Science, The Ohio State University, 2020, Public Health

Legionella pneumophila (L. pneumophila) has become a significant public health issue due to its growth in water distribution systems. In natural water systems L. pneumophila is often found in relatively low concentrations. However, in distribution systems it is able to thrive through the use of biofilms and invasion of larger host organisms such as protozoa. Additionally, the altered microbial ecology of water distribution systems seems to play a role in facilitating its ability to proliferate and persist. L. pneumophila can cause respiratory infections when contaminated water is aerosolized as it exits from distribution or premise plumbing systems and is then inhaled. Research has shown that some tap water organisms can exhibit inhibitory or commensal effects on L. pneumophila. Understanding more about these relationships will allow us to better estimate L. pneumophila concentrations in premise plumbing. A systematic literature review was conducted to gather relevant information regarding the interactions of L. pneumophila with tap water biofilm microbial ecology. From the resulting information a stochastic model has been produced to simulate (1) these interactions within a tap water biofilm and (2) the inhibitory or commensal effects on L. pneumophila concentrations. The model simulates the interactions of L. pneumophila within a tap water biofilm. These interactions are used to calculate the resulting L. pneumophila concentrations in the biofilm and bulk tap water. Theses concentrations are then used in a quantitative microbial risk analysis (QMRA) of a 15-minute showering event and used to determine the exposure hazard to humans and associated risk of L. pneumophila infection based off this novel ecological modeling method. The models that my method develops are a means of improving the precision of estimates for exposure of bacteria after its growth in premise plumbing. From this, we can better understand how communities of microorganisms in biofilms affect (open full item for complete abstract)

Committee: Mark Weir (Advisor); Michael Bisesi (Committee Member); Kerry Hamilton (Committee Member); Natalie Hull (Committee Member) Subjects: Biology; Demography; Ecology; Environmental Engineering; Environmental Health; Environmental Studies; Epidemiology; Health; Health Care; Microbiology; Public Health

Doctor of Philosophy, The Ohio State University, 2014, Statistics

Physical experiments play an important role in agriculture, industry, and medical research. However, physical experiments can sometimes be difficult or even impossible to run. In these situations, computer experiments are becoming desirable surrogates for physical experiments. This dissertation considers designs and the predictive models for computer experiments with both quantitative and qualitative input variables. The existing framework for building Gaussian stochastic process (GaSP) models with quantitative and qualitative inputs is to treat a given set of values of the qualitative inputs as determining a response surface in the qualitative inputs. A GaSP model is assumed for each of these response surfaces and the same covariance structure is used for each response surface. A cross-correlation parameter is introduced for each pair of sets of values of the qualitative variables in order to "capture" correlations between response surfaces. To guarantee that one has a legitimate overall covariance structure, certain conditions are imposed on the cross-correlation parameters. In the first part of this dissertation, we introduce two indicator-based GaSP models by transforming the qualitative inputs into quantitative variables and then use traditional correlation functions for quantitative inputs. We also show the equivalence properties between these new models and the existing model. The second part of this dissertation is about the experimental designs with both quantitative and qualitative inputs. The special data structure requires that a "good" design not only capture the cross-correlation information but also spread observations out over the entire quantitative inputs space. We propose two types of designs, the partial SLHD and partial CSLHD, which are modifications of existing designs in the literature, and compare their prediction accuracy with all the other existing designs for quantitative and qualitative. By examining several examples, we find tha (open full item for complete abstract)

Committee: William Notz (Advisor); Peter Craigmile (Committee Member); Matthew Pratola (Committee Member) Subjects: Statistics

Doctor of Philosophy, The Ohio State University, 2009, Physics

The ultimate goal of physics education research (PER) is to develop a theoretical framework to understand and improve the learning process. Developing research-based effective assessment instruments and making meaningful inferences based on these instruments have always been important goals of the PER community. Quantitative studies are often conducted to provide bases for test development and result interpretation.Statistics are frequently used in quantitative studies. The selection of statistical methods and interpretation of the results obtained by these methods shall be connected to the education background. In this connecting process, the issues of educational models are often raised. Many widely used statistical methods do not make assumptions on the mental structure of subjects, nor do they provide explanations tailored to the educational audience. There are also other methods that consider the mental structure and are tailored to provide strong connections between statistics and education. These methods often involve model assumption and parameter estimation, and are complicated mathematically. The dissertation provides a practical view of some advanced quantitative assessment methods. The purpose of the study is to provide a comparison between these advanced methods and the pure mathematical methods. The dissertation includes three parts. The first part involves the comparison between item response theory (IRT) and classical test theory (CTT). The two theories both provide test item statistics for educational inferences and decisions. The two theories are both applied to Force Concept Inventory data obtained from students enrolled in The Ohio State University. Effort was made to examine the similarity and difference between the two theories, and the possible explanation to the difference. The study suggests that item response theory is more sensitive to the context and conceptual features of the test items than classical test theory. The second part of the (open full item for complete abstract)

Committee: Lei Bao Prof. (Advisor); Richard Furnstahl Prof. (Committee Member); Andrew Heckler Prof. (Committee Member); Evan Sugarbaker Prof. (Committee Member) Subjects: Education; Educational Evaluation; Physics