Doctor of Philosophy, The Ohio State University, 2021, Educational Studies

Leveraging big data for student data analytics is increasingly integrated throughout university operations from admissions to advising to teaching and learning. Though the possibilities are exciting to consider, they are not without risks to student autonomy, privacy, equity, and educational value. There has been little research showing how universities address such ethical issues in their student data policies and procedures to date though privacy and security policies are abundant. Though privacy and security policies that students sign cover institutions legally, there is more that can be done to support the ethical use of student data analytics at higher education institutions. This exploratory study addressed why it is important to support the four values of autonomy, privacy, equity, and educational value within university student data analytics policies and procedures. A rationale for focusing on these values was discussed through the lens of Paulo Freire's Pedagogy of the Oppressed. A comparative case analysis of data analytics policies and procedures at two large, public universities provided insight into what they emphasized and where risks to student autonomy, privacy, equity, and educational value existed. This study concluded with recommendations of how institutional leadership can use proposed principles of ethical student data analytics to evaluate their own policies and procedures and amend risks that are uncovered through analysis.

Committee: Bryan Warnick (Advisor); Richard Voithofer (Advisor); Blount Jackie (Committee Member) Subjects: Education Philosophy; Education Policy; Educational Leadership; Educational Technology; Ethics; Higher Education; Higher Education Administration

Honors Theses, Ohio Dominican University, 2023, Honors Theses

This paper describes the process of development and evaluation of an open educational resource (OER) e-module on the Central Limit Theorem written for an Introductory Statistics college-level course. The purpose of this project is two-fold. First, the e-module bridges the knowledge gap between introductory topics and Hypothesis Testing – one of the most challenging concepts in Statistics. Second, the project focuses on developing tools that allow instructors to analyze the effectiveness of the module and reveal student patterns of interaction with the platform. The overall goal of the project is to improve the quality of open educational resources, provide students/instructors with additional study materials in response to rising cost for textbooks and higher education, and provide more data for further research on student behavior while interacting with e-textbooks. The interactive e-module was developed using LaTeX markup language and Overleaf editor, uploaded to the XIMERA platform and tested on two sections of MTH 140, a college-level Statistics course. Once the experiment has been performed and the data collected, the results were analyzed using Python programming language. As a result of the study, some tools for analysis of user data have been developed, and an OER has been created.

Committee: Anna Davis (Advisor); John Marazita (Committee Chair); Kristall Day (Committee Member); Lawrence Masek (Committee Member) Subjects: Computer Science; Education; Mathematics; Psychology; Statistics

Master of Computing and Information Systems, Youngstown State University, 2023, Department of Computer Science and Information Systems

Co-curricular supports have been practice bias, which makes it difficult to understand need-based support for nontraditional students in STEM. Thus, the aim of this study was to use data analytics to understand student support in STEM for Nontraditional Students. Quantitative research method approach was adopted with a longitudinal survey of 366 students in the Fall and 218 students in the Spring. In order to understand the support system for non-traditional students, structural equation modeling was used. RStudio was used to screen and analyze the initial data, and the lavaan package in R was used to conduct latent variable analyses. To examine the latent correlations, all constructs were concurrently integrated in a single Confirmatory Factor Analysis model. Subsequently, the data analysis process moved on to robust full information maximum likelihood (RFIML) estimation of SEM and the non-significant pathways were removed until the final model was developed. The study found that though the omnibus support model, as well as the support model for traditional, were not confirmed in both Fall and Spring semesters, it was confirmed for nontraditional students in the Fall semester. The significant loadings for the nontraditional students in the Fall semester include academic integration, university integration, academic advisory support, faculty support, stem faculty support, student affairs support, and cost-of-attendance support & training. However, it was found that the support model for nontraditional students in the Spring semesters was not confirmed. Therefore, using structural equation modeling, this study provides important insights for understanding support for nontraditional students.

Committee: Cory Brozina PhD (Advisor); Alina Lazar PhD (Committee Member); Arslanyilmaz Abdu PhD (Committee Member) Subjects: Engineering; Higher Education; Statistics

Master of Science, Miami University, 2020, Physics

The use of machine learning is expanding to create new tools for educators. We used a decision tree regression algorithm to train a model that achieved an 80% identification rate of students who were at risk of not passing their introductory physics course. We minimized the false positive rate (students who failed but were predicted to pass) to 5.5% of the total test sample population. The model allowed us to reduce the pool of students who may need additional help to 39% of the course population. The data was sourced from student course management systems and their institutional academic history. We avoided using graded course material and successfully trained the model using data from the first 3 weeks of the semester.

Committee: Jennifer Blue Dr. (Advisor); Steve Alexander Dr. (Committee Member); Imran Mirza Dr. (Committee Member) Subjects: Educational Technology; Information Science; Physics