Credit Hours: 4
Instructional Mode: Online & Asynchronous
Student Collaboration: Weekly hour-long small group virtual meetings (student choice of meeting synchronously or asynchronously)
Faculty Designer: Dr. David Williamson Shaffer, Learning Analytics faculty director
Instructor: Dr. Brendan Eagan, Epistemic Analytics Lab at UW-Madison
“Loved the structure of this course and the mix of ways we got to engage with the material. I also really enjoyed the core reading and found the project extremely useful in applying what we had learned. Feedback was thorough, targeted, and actionable.”
Student end-of-course evaluation, summer 2022
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EdPsych 551 introduces Quantitative Ethnography as a research method that goes beyond the distinctions of quantitative VS qualitative research methods to help us understand how to make sense of our increasingly data-rich world. We do that by looking both at the theoretical foundations and practical use of quantitative ethnography, focusing on new insights in the field of cognitive modeling and automated coding and their use in applied fields such as anthropology, education, market research, product development, assessment, and training.
Students will:
- Explore the foundations and practical use of quantitative ethnography
- Discuss the unification of quantitative and qualitative methodologies in a quantitative ethnographic framework as well as the validity and reliability of learning analytics
- Conduct quantitative ethnographic analyses of their own data including:
- cognitive modeling
- automated coding
- writing research memos
- writing up their quantitative ethnographic study
Why this course?
Learning analysts are asked to tackle a variety of problems and work with data in a variety of modalities. Quantitative Ethnography allows learning analysts to further examine qualitative data using quantitative methods, and discover patterns and connections. EdPsych 551 is the last of the three methods courses in the curriculum (560, 525, 551), and serves as a launch point for students to begin Year 2 of the program where the focus is on application.
How do students learn in 551?
It’s summer! Rather than sit in front of a screen, take a walk outside and listen to the weekly podcast. Students also engage with academic journal articles, individual assignments (reflective and enactive), collaborative group exercises, and a summative final synthesis paper.
Schedule of Topics
| Week | Topic |
| 1 | Inconfidence: Motivation and Statistics |
| 2 | Ugly Sweaters: Ethnography and Data |
| 3 | Subtle Knives: Segmentation |
| 4 | Hatfields VS McCoys: Coding |
| 5 | Eppur si mouve: Modeling |
| 6 | Enough already! Saturation |
| 7 | How not to get stolen or adopted: Connections |
| 8 | What is QE? What really is QE? |
What students say
“The content, structure and presentation by the instructor was clear and challenging (in a good way). I felt like the feedback both challenged my thinking and informed my understanding.”
Student end-of-course evaluation, summer 2023
Instructor Insights
“551 is one of my favorite courses to teach because it is often one of my students’ favorites to take. Quantitative Ethnography brings together computation and interpretive aspects of learning analytics in ways that students often find deeply satisfying and meaningful. Learning to use epistemic network analysis (ENA) connects program readings and theory to a concrete practice for many students. As an instructor, I greatly enjoy engaging with student reflections on this process as we help each other grow in understanding as well as our practical expertise in learning analytics.”
Sample Week
Materials including videos, assignments, and readings will be available at the beginning of each week. Below is a suggested guideline for spacing out assignments in order to provide enough time for work, interactions with the instructors, students, and student group, etc. While the rhythm may change depending on the week, students can generally expect to engage with course materials and each other in this way and thus may plan accordingly. Note the purposeful balance between individual and group assignments. Below is a sample from Week 4 of this course:
| Week 4: | Hatfields VS McCoys | ||||||
| Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday | |
| Learning Objectives | Understand why reliability is important; Examine theoretical and philosophical basis for coding processes; Begin developing reliable and valid codes | ||||||
| Core Reading(s) | Shaffer & Ruis (2021) | ||||||
| Podcast | Danielle Espino & Seung Lee, Pepperdine University | ||||||
| Discussion Post | Post on reading & podcast by Thurs pm | ||||||
| Individual Implementation | Engage in activity and write blind discussion post, submit by Thurs pm | ||||||
| Group Workshop | Meet with group, respond to prompt, 1 member submit by Mon pm | ||||||
What More Students are Saying:
Overall, the success of this course is clearly amplified by the quality of the teaching team. I received some of the most valuable feedback I have ever received in a course. – Student evaluation, 2023
The asynchronous mode (it being a summer course) suited me perfectly. Besides, the overall structure of the course, the content of the readings and the way the exercises and reection questions are framed contribute toward building a strong foundational understanding of QE. – Student evaluation, 2022
This course was extremely well developed and well implemented. The combination of instructor videos, readings, and then related podcasts created a nice mixture of input. I appreciated having both individual and small group assignments at a pace that was both challenging yet t into my established adult life in a way I felt capable of accomplishing well. – Student evaluation, 2022
The QE textbook provided information in a very digestible form and pulls together many of the concepts we have been working with in the program thus far. The podcasts provided excellent vignettes of how QE is being used and by whom. The work with coding and ENA enabled us to practice the art and gain a little insight into how QE is done and what its strengths are. – Student evaluation, 2022
This course followed a similar structure as the rest of the program. It built well on the foundations formed on previous program courses (especially research methods). The course is structured in a way that’s manageable for summer, and breaks up the content so that it’s never too overwhelming. – Student evaluation, 2023
While I am very grateful for the experience of writing qualitative and quantitative research papers in prior courses, I felt like sticking to memos in this course helped me to focus on the complexities of the QE method. The balance of using one dataset to complete a QE analysis without the full research paper was a good balance of depth and reflection. – Student evaluation, 2023
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