Doctor of Philosophy
in Education (PhD),
Learning Analytics in
Higher Education

A PhD prepares you to make significant contributions to the body of literature within the education field. This course prepares you for understanding what theory is, recognizing theoretical frameworks within existing literature, connecting your research interests to existing theoretical frameworks, and justifying how your research will add to the wealth of current theories in the field.

Your success as a scholarly professional will largely depend upon your communication skills, particularly in your written work. This course supports your development as a scholar who can publish in different types of research-based publications for a variety of audiences. You’ll practice synthesizing multiple sources, formulating arguments, and integrating feedback through iterative drafts of your work. These are key capabilities you’ll need as you submit your research in published manuscripts and presentations.

In this course, you’ll develop effective search and writing strategies to create a scholarly review of literature. The course emphasizes how to: (a) use effective literature search strategies; (b) develop a scholarly synthesis of research literature; (c) organize research literature around identified themes, including a study problem, purpose, and theoretical perspectives; and (d) focus on developing a scholarly exposition that reflects divergent viewpoints and contrasting perspectives. The overarching goal is for you to understand strategies for surveying scholarly literature that avoid bias, focus on educational, practice-based research problems, and address the requirements of a scholarly literature review.

This course introduces you to the research process by exploring its underpinnings, examining its paradigms, and investigating the foundations of qualitative and quantitative methodologies used in educational studies. You’ll identify criteria for the development of quality research studies that are ethical, accurate, comprehensive, cohesive, and aligned. Specific course topics involve the ethics of conducting research; data collection and analysis techniques; and issues of feasibility, trustworthiness, validity, reliability, transferability, and rigor. The goal is to familiarize yourself with the concepts and skills associated with conducting theoretical and applied research.

This course provides the foundational knowledge to become a critical consumer of statistical-based research and a skilled analyst of non-inferential quantitative data. Coursework focuses on understanding multivariate data, non-inferential and inferential statistical concepts, the conventions of quantitative data analysis, and interpretations and critical inferences in statistical results. You’ll use software applications to complete statistical computations and perform quantitative data analysis. The course culminates in a synthesis project to demonstrate your statistical skills and present your results using APA guidelines.

A focus on qualitative research methodology and the designs and methods used to collect and analyze data in educational research. You’ll examine the principles of qualitative research and explore commonly used designs (also referred to as qualitative traditions or genres) with a focus on application and feasibility. Qualitative data collection and analysis methods will be examined for their suitability with regard to the research design selected. Alignment between qualitative designs and research methods, issues of trustworthiness, and the responsibilities of the qualitative researcher will also be explored.

An exploration of quantitative research methodologies and associated designs and methods. You’ll examine paradigmatic perspectives along with the tenets and conventions of quantitative research. Topics for examination include feasibility, validity, reliability, variable operationalization, inferential designs, and analytic software applications used within the quantitative research paradigm. You’ll also look at the components of quantitative research designs that support meaningful studies within the field of education.

An exploration of advanced statistical principles and how to apply them to quantitative research. This course provides an overview of advanced statistical concepts used in empirical research, including inferential analyses. You’ll use SPSS software to perform advanced computations as you build independent, scholarly statistical skills. Coursework will emphasize multivariate data; the use, comprehension, and evaluation of sophisticated statistical concepts; and the proper presentation of statistical results.

This course builds on a foundational understanding of qualitative designs and measurements to focus on analyses of the data. Coursework takes you deeper into the skills and techniques necessary to ensure the appropriate analyses of qualitative data, including integrating relevant frameworks, verifying trustworthiness of the findings, and selecting suitable methods for presenting analyses and findings.

The doctoral comprehensive assessment is your opportunity to demonstrate your preparation for entering the dissertation phase as a PhD candidate. You’ll synthesize discipline-specific content with research designs and analysis methods to create a prospectus for a theoretically-based research study that focuses on furthering knowledge in the field of education. Whereas EdD research focuses on addressing a researchable problem with practical applications, PhD research has a focus on contribution to theory and the broader discipline of education. This course is begun only after all your foundation, specialization, and research courses have been completed, and your prospectus will likely become the foundation of your PhD dissertation. 

Students in this course will be required to complete chapter one of their dissertation proposal, including the following: a review of literature with substantiating evidence of the problem, the research purpose and questions, the intended methodological design and approach, and the significance of the study. A completed, committee-approved chapter one is required to pass the course. If you don’t receive approval to minimum standards, you’ll be able to take up to three supplementary eight-week courses to finalize and gain approval of chapter one.

In this course, you’ll work on completing chapters one to three of your dissertation proposal and receiving committee approval for the dissertation proposal (DP). Chapter two consists of the literature review, while chapter three covers the research methodology and design, including population, sample, measurement instruments, data collection and analysis, limitations, and ethical considerations. Completed, committee-approved chapters two and three are required to pass the course, as is a final approved dissertation proposal. If you don’t receive approval to minimum standards, you’ll be able to take up to three supplementary eight-week courses to finalize and gain approval of these requirements.

In this course, you’ll prepare, submit, and obtain approval of your Institutional Review Board (IRB) application. You’ll also collect data and submit a final study closure form to the IRB. If you’re still collecting data at the end of the 12-week course, you’ll be able to take up to three supplementary eight-week courses to complete data collection and file your IRB study closure form.

In this dissertation course, you’ll work on completing chapters four, five, and your final dissertation manuscript. Specifically, you’ll complete your data analysis, prepare your study results, and present your findings with an oral defense and a completed manuscript. A completed, committee-approved dissertation manuscript and successful oral defense are required to complete the course and graduate. If you don’t receive approval for either or both, you can take up to three supplementary eight-week courses to finalize and gain approval.

LAHE-7000 Introduction to Learning and Knowledge Analytics 

An introduction to the history and evolution of data analytics, including the prominent foundations of learning analytics and the key theories, leading experts, best practices, and the application of data analytics in education.

LAHE-7001 Using Educational Data

This course introduces the roles of technology and various educational data in learning analytics. You’ll receive an overview of data mining, data integrity, data privacy, and data utility. You’ll also learn and apply popular educational data technology terms and all elements of data capture and management. Different data systems and data sources will be covered, as well as the data mining process and other best practices in learning analytics.

LAHE-7002 A Macro-Level Approach to Learning Analytics in Higher Education

This course explores various theories regarding the use and advancement of learning analytics in higher education at a macro level. You’ll study the role of analytics in areas such as student learning and achievement, human resources, facilities, finance, research, and academic affairs. You’ll examine the implications of learning analytics on higher education administration, students, and teachers. Finally, you’ll look at the kinds of institutional leadership, technology infrastructure, and human capital necessary for learning analytic initiatives, and investigate the challenges and future considerations for this field.

LAHE-7003 Applying Learning Analytics in Higher Education

This course will cite and discuss the historical application of learning analytics, from their early application in the SIGNAL studies at Purdue to more recent applications throughout the country. You’ll review prominent studies to learn the role of technology and learning analytics in specific student outcomes like retention memory, engagement, dropout risk identification, and other targeting metrics. You’ll also study how learning analytics is used to improve curriculum, pedagogy, institutional accountability, and administration. Ethics, data literacy, and the legal aspects of learning analytics will be discussed in detail, and you’ll be encouraged to plan a hypothetical learning analytics initiative of your own based on the knowledge you’ve gained.

LAHE-7004 Learning Analytic Tools

This course introduces you to the various types, functions, and applications of analytics tools. You’ll review prominent studies and explore an analytics strategy that relies on knowing the purpose and types of educational answers sought, as well as the technology infrastructure, the availability of data, and the costs. Consideration in both choosing and applying the correct analytics tools cannot be overlooked, as the requirements of each tool bring pros and cons.

LAHE-7005 Implementing a Higher Education Learning Analytics Project

In this learning analytics capstone course, you’ll design (in theory, rationale, and purpose) a theoretical higher education analytics project that follows a provided, predesigned template. Particular attention will be given to issues of scope, cost, timeliness, and utility. You’ll also work to address the humanistic and soft sides of learning analytics, including leadership, in-house expertise, and ethical and legal issues.