Doctor of Philosophy in
Education (PhD),
Learning Analytics in
K-12 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.

LAK-7000 Introduction to Learning Analytics 

This course explores the evolution of data analytics and its progression into education. Prominent theories and leaders will be explored, and you’ll learn to delineate between learner analytics, academic analytics, and data mining. The coursework outlines the distinction in purpose and function that learning analytics play in the K-12 environment.  You’ll be introduced to the historical forces responsible for driving the growth of K-12 learning analytics, such as federal legislation, high-stakes testing, increased accountability, reduction in resources, and an increase in commercially branded software. Finally, you’ll examine potential learner analytics use in K-12 environments, the criteria for a successful K-12 analytic program, and stakeholder perspectives regarding the implementation of analytics.

LAK-7001 K-12 Educational Data

An introduction to the role of technology and the various forms of education data used in learning analytics. You’ll receive an overview of data mining with special consideration and focus on best practices in learning analytics, such as the use of learning analytics software, learning management systems, and course content systems. Instruction will include the uses, relevance, and practicality of employing K-12 data for predictive analysis. You’ll also learn the difference in viewpoint that data can provide, from a retrospective to a formative assessment to a predictive view.

LAK-7002 K-12 Analytics Decision-Making: An Administrator’s Perspective

This course introduces schools and system administrators to the world of learning analytics and how to design, choose, or model an intended project. You’ll learn to align learning analytic projects to school/district priorities, needs, and areas of inquiry. You’ll also explore the various factors to consider when using data analytics as a “crystal ball,” and the pros and cons of doing so. Several early and recent applications of learning analytics in the K-12 sector will be presented, and you’ll learn how to evaluate and critique each, as well as how to handle stakeholder concerns.

LAK-7003 K-12 Learning Analytic Considerations

This course addresses the common problems, concerns, and oversights with learning analytics that school districts and administrators may encounter. All the soft sides of learning analytics will be addressed, especially student privacy regulations (FERPA) and data ownership and stewardship. You’ll learn the advantages, limitations, and implementation guidelines of predictive analytics in K-12, and engage in analytics activities for prediction (e.g., predicting college readiness) and formative (e.g., real-time gauging of performance for course correction) assessment at the K-12 level. Throughout the course, you’ll gain exposure to many active K-12 learning analytic projects.

LAK-7004 K-12 Analytic Tools

This course introduces you to the various types, functions, and applications of K-12 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. Special attention is given to the use of K-12 statewide student information systems (SIS) and the integration of other multisource data, such as that from the NAEP (National Assessment of Educational Progress).

LAK-7005 Implementing a K-12 Analytics Project

In this learning analytics capstone course, you’ll design (in theory, rationale, and purpose) a theoretical K-12 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.