Doctor of Education (EdD)
in Leadership in Learning
Analytics in K-12 Education

Your communication abilities have a big influence on your professional reputation. In this course, you’ll develop skills to establish yourself as a competent professional with strong communication skills. You’ll learn competencies related to written, oral, and visual forms of communication appropriate to specific media and audiences. You’ll also explore how the iterative nature of preparing communications and integrating feedback into your work products can support your development and advancement as a professional.

Leadership during times of change can be challenging. This course supports your professional development as an effective leader of educational organizations during periods of change. You’ll explore strategies and techniques for self-reflection, evaluating culture, integrating stakeholder feedback, and incorporating data as part of improvement processes. To conclude the course, you’ll synthesize these skills to design a comprehensive improvement plan that addresses a specific problem within an educational organization.

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.

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 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.

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.

The doctoral comprehensive assessment is your opportunity to demonstrate your preparation for entering the dissertation phase as a doctoral candidate. You’ll synthesize discipline-specific content with research designs and methods to create a prospectus for a problem of applied practice within an educational context. This prospectus will likely become the foundation of your dissertation. This course is begun only after all your foundation, specialization, and research courses have been completed.

In this 12-week course, you’ll complete all relevant subsections of Section 1: Foundation. You’ll use your school-specific template and guidance from your chair to determine which subsections apply to your individual work. Section 1 must be completed and approved by your committee in order to pass the course and move forward. If you do not receive committee approval of Section 1, you’ll be able to take up to three supplemental eight-week courses to finalize and gain approval.

In this course, you’ll compose all relevant subsections of Section 2: Methodology and Design, and complete your proposal. Both of these components must be approved by your committee in order to pass the course and move forward. You’ll use your school-specific template and guidance from your chair to determine which subsections apply to your individual work. If you do not receive approval of Section 2 and the completed proposal by the conclusion of this 12-week course, you’ll be able to take up to three eight-week supplementary courses to finalize and gain approval.

In this course, you’ll prepare, submit, and obtain approval of your Institutional Review Board (IRB) application before collecting data and, if applicable, executing your project modeling. You’ll also 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 the required components.

In this 12-week course, you’ll complete the relevant subsections of Section 3: Findings, Implications, and Recommendations, finalize your manuscript, and disseminate your findings. You’ll use your school-specific template and guidance from your chair to determine which subsections apply to your individual work. The final manuscript, including Section 3 and the dissemination of findings, must be approved by your committee in order to pass the course and be eligible to graduate. If you do not receive committee approval on all components, you’ll be able to take up to three supplemental eight-week courses to finalize these requirements and be eligible to graduate.

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 you’ll engage in analytics activities for both 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) your 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.