Credit Hours: 1
Instructional Mode: Online & Asynchronous
Student Collaboration: Students communicate via asynchronous collaborative annotation software during reading assignments
Faculty Designer:Dr. Julia Rutledge, Learning Analytics director
Instructor: Dr. Julia Rutledge, Learning Analytics director
“It’s amazing to watch students connect with and support each other as they learn the new language of statistics.”
Jaeyoon Choi, program teaching assistant
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Statistics is the first step towards making meaning from quantitative data, and this course introduces the logic and methods of statistics used in the field of learning analytics. Designed to be a preparatory course that emphasizes concepts and application rather than computational details, the materials and assignments in EdPsych 505 focus on how methods are used and interpreted. The first half of the course focuses on materials and assignments that build fundamentals in the areas of inferential statistics. Questions on reliability and validity, scalability, and the implications of inference are introduced to build a solid foundation in applied statistical methodology. The second half of the course introduces students to using a statistical software program R, which they will continue to use in the fall EdPsych 560 course.
Students will:
- Explore the foundations and practical use of statistics in learning analytics practice and research
- Understand how to apply quantitative reasoning to learning analytics topics
- Practice working with introductory descriptive statistics, probability, and statistical inference
- Acquire a working fluency with statistical theories, concepts, and terminology in learning analytics
Why this course?
We developed and added this preparatory course in response to student input. EP505 is an optional course designed for students who have had minimal experience with statistics and/or statistical software. Upon acceptance to the program, students confer with the program director to determine if they should take EdPsych 501 for 4 credits, or EdPsych 501 for 3 credits paired with EdPsych 505 for 1 credit.
How do students learn in 505?
The course is divided into two distinct modules. In module 1, students engage with each other and the teaching team as they collaboratively and asynchronously annotate a text, individually work through practice problems, and engage in knowledge checks. In module 2, students work through prescribed exercises using the R platform in order to familiarize themselves with the software and begin to build skills towards linear regression.
Schedule of Topics
| Week | Topic |
| 1 | Descriptive Statistics |
| 2 | Probability |
| 3 | Discrete & Continuous Probability Distributions |
| 4 | Confidence Intervals |
| 5 | Intro to R, Part I |
| 6 | Intro to R, Part II |
| 7 | Intro to R, Part III |
| 8 | Intro to R, Part IV |
What a past student has said
“I’m so glad I could get my feet wet in this course before taking EdPsych 560: Foundations of Quantitative and Qualitative Methods in the fall.”
Student end-of-course evaluation, summer 2023
Instructor Insights
“I wish I’d had a course like this when I was in graduate school! It’s been wonderful to see students in summer focus on learning basic stats concepts and playing around with software, and then apply those skills when they take a deeper dive into quantitative methods in their next course in the curriculum.” – Dr. Julia Rutledge
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: | Confidence Intervals | ||||||
| Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday | |
| Learning Objectives | Explore how statistical inference is used to draw conclusions about one population based on a sample. | ||||||
| Collaborative Reading(s) | Webb (2021) pp. 1690189 | ||||||
| Peer Response | Respond to at least 1 peer by Mon pm | ||||||
| Practice Set | Practice Set 4 by Mon pm | ||||||
| Knowledge Check | Knowledge Check 2 by Mon pm | ||||||
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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 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/