Credit Hours: 6
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 & Dr. Percival Matthews, Associate Professor and Associate Dean for Equity, Inclusion, and Diversity
Instructor: Dr. Kelsey Schenck, Assistant Professor at Southern Methodist University and UW-Madison alum (PhD in Educational Psychology – Learning Sciences)
“Rigor is good for learning. It was a tough class and there were times I was really challenged, but there was never a time I couldn’t do something. It was structured very well and we were provided resources to get supported through the challenges.”
Ryan Ward, high school science teacher, class of 2024
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EdPsych 560 presents introductory research procedures in the social sciences, including the exploration of theoretical foundations and practical use of basic tools and programs needed for quantitative and qualitative data analysis. The course examines how different methodologies can complement or compete with the other, and showcases how pertinent quantitative and qualitative methods are applied in the field of learning analytics with particular emphasis on data about – and therefore issues in – learning environments such as classrooms, online courses, apprenticeships and internships, museum exhibits, after school programs, and other formal and informal educational contexts. Students will:
- Draw connections via discovery and analysis of foundational quantitative, qualitative, and mixed methods approaches.
- Discover the theoretical foundations of regression analysis as well as apply it to real datasets.
- Analyze data and draw meaningful conclusions with regard to research questions.
- Critically read and evaluate social science publications that use regression models as analytic tools.
- Collect and qualitatively analyze thick collections of data.
- Engage in a rigorous process of documenting methods, assumptions, and conclusions.
- Apply methods to a variety of learning analytics research via written memos and projects.
Why this course?
We believe that learning analysts should understand the numbers and the context behind the numbers. Good learning analysts need to be able to grapple with a problem from multiple perspectives, and employ a balance of methodological tools to work towards ethical and equitable solutions. Rather than separating quantitative and qualitative methods into two distinct courses, we have combined them into an offering that showcases how they can complement – or compete – with each other.
How do students learn in 560?
Students engage with video textbook chapters (~45 minutes), read academic journal articles, work on scaffolded memos for their individual long-term quantitative project and qualitative project, and meet with their small group to discuss progress and provide peer review. For the quantitative project, students learn R programming software. For the qualitative project, students perform observations and interviews and create qualitative coding schemes.
Schedule of Topics
| Week | Topic |
| 1 | How vs. Whether: Do you what to know how or why something happens, or whether something happens? |
| 2 | Getting Your Hands Dirty: Often the most difficult step in the analysis is actually starting. |
| 3 | Schrödinger’s Cat: This week we discuss predictive power and reflections. |
| 4 | Slipping Into Something a Bit More Comfortable: Get ready to run an analysis on your quantitative data! And learn about frequency and support. |
| 5 | To P or Not to P: Welcome to one of the most fundamental yet misunderstood ideas of statistics: the p-value. |
| 6 | Conduct, Conscience, and Convention: Considering ethical issues relating to data collection, analysis, and storytelling. |
| 7 | Building Your Toolbox: Time to add a few new tools to your toolbox – interviewing, correlation, and regression. |
| 8 | Advanced Navigation on the Sea of Data: Charting our path through the deep water of quantitative methods. |
| 9 | Making Your Case: What stories can you tell with your data, and how are you going to tell them? |
| 10 | Yeah, Sez YOU!: What does it mean to make a story accurate? This week we will discuss validity. |
| 11 | Thanksgiving Break |
| 12 | He Says, She Says, They Say: It’s time to finish up your papers, but we won’t stop there! |
| 13 | Peer Reviews Here! Get Ya Peer Reviews!: Successful writing is rewriting and peer reviews are a critical part of the process. |
| 14 | The Long and Winding Road: It has been a long journey. Reflect on where you came from and where you’re heading next. |
What students say
Huge impact! I knew I loved research, but this course gave me a great foundation and the confidence it will take to further develop my skills. I enjoyed that we learned by truly doing the work.
Student end-of-course evaluation, fall 2022
Instructor Insights
“This course is one of my all-time favorites that I’ve taught in my career. I wish I had a course in my graduate work that showed WHY and WHEN you might pick a quantitative or qualitative approach rather than siloing these approaches as if they are too different to even mention in the same sentence. This course can be challenging (after all, we ask you to develop and complete TWO projects!), but it’s worth it! It’s so rewarding at the end to watch students begin to feel like competent researchers as they gain insights into problems that they’ve identified and build strong methodological backgrounds for their futures.” – Dr. Kelsey Schenck
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: | “Slipping into something a bit more comfortable” | ||||||
| Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday | |
| Learning Objectives | First run of quantitative data; learn more about frequency and support | ||||||
| Core Reading(s) | Glesne; Witte & Witte | ||||||
| Video Lecture | Watch video textbook chapter | ||||||
| Discussion Post | Post on reading & video by Thurs pm | ||||||
| Quant Project Memo | Histograms & Scatterplots by Thurs pm | ||||||
| Qual Project Memo | Initial observations & Fieldnotes | ||||||
| Group Discussion | Meet with group, respond to prompts, 1 member submit by Mon pm | ||||||
What More Students are Saying:
- From someone without a lot of prior statistical experience:
- From someone with a lot of prior statistical experience:
- On your first try:
- On feedback and structure:
- On growth:
I wasn’t confident in my abilities as a quant researcher, but we had so much support – shout out to the instructional team! – Kaycie Barron
The content and assignment requirements were incredibly beneficial to me. I’m feeling like I’m walking away with additional information – even for quantitative, which I already had extensively in my undergrad. – End of semester evaluation, 2022
I’ve always wanted to get into research and this was great exposure to methods. The course is so applicable to so many different fields within education, and yet it is also very specific to Learning Analytics. – Conner Krattiger
Even though it’s asynchronous, I felt like my instructor and TA were available and provided great, timely feedback on the assignments. The broad picture of the course was laid out and it was clear how each week contributed to the scaffolding leading up to our final projects. – End of semester evaluation, 2021
I grew so much in my statistical analysis and ability to decipher quantitative data. The learning-by-doing process of the memos and papers were challenging, but incredibly worthwhile. – End of semester evaluation, 2023
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ACADEMIC CALENDAR & RELIGIOUS OBSERVANCES
ACADEMIC INTEGRITY
By virtue of enrollment, each student agrees to uphold the high academic standards of the University of Wisconsin-Madison; academic misconduct is behavior that negatively impacts the integrity of the institution. Cheating, fabrication, plagiarism, unauthorized collaboration, and helping others commit these previously listed acts are examples of misconduct which may result in disciplinary action. Examples of disciplinary action include, but is not limited to, failure on the assignment/course, written reprimand, disciplinary probation, suspension, or expulsion.
ACCOMMODATIONS FOR STUDENTS WITH DISABILITIES
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DIVERSITY & INCLUSION
Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background – people who as students, faculty, and staff serve Wisconsin and the world. https://diversity.wisc.edu/