THEORY AND PRACTICE - MS in Learning Analytics

Credit Hours: 5

Instructional Mode: Online & Asynchronous

Student Collaboration: Weekly hour-long student partner virtual meetings (student choice of meeting synchronously or asynchronously; partners change three times within the semester)

Faculty Designers: Dr. Shamya Karumbaiah, Department of Educational Psychology professor & Dr. Martina Rau, past Department of Educational Psychology professor

Instructor: Dr. Dariane Drake, University of Wisconsin-Madison Division of Instructional Technologies

“The reflection assignments really helped us make sure we understood the terms, and were a great way to pause and check our own understanding.”

Dani Creasey, Office of the Registrar at UW-Madison, class of 2024

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EdPsych 525: Learning Analytics Theory and Practice focuses on exposing students to a broad array of data mining techniques for large educational datasets. Students work through course materials and collaborate in partners to gain important insights into how people learn. Materials and assignments examines the spectrum of prevalent learning analytics methods and applications, from institutional effectiveness, to classroom-level interventions, to standardized assessments, and beyond.

Students will:

Identify the fundamentals of machine learning (pros and cons, a basic vocabulary), large language models (LLMs), and database interventions (What to do with predictive models for student performance? What information is revealed to students/parents/teachers/schools?).
Analyze AI issues including data ethics, regulations, and privacy (What kind of data do schools work with? How is that data consumed? What are schools/businesses doing with it and what could/should they be doing?).
Utilize prediction methods (e.g., predicting dropouts from MOOC courses).
Engage in structure discovery (What features distinguish successful from unsuccessful learners?).
Demonstrate understanding of relationship mining (What factors determine whether a student will perform poorly on an exam?).

Why this course?

There are a wide variety of quantitative methods available to answer a wide variety of learning analytics questions. This course prepares students to think critically about methodology and better understand the ethical and equitable considerations inherent in the different data mining techniques. While the fall course does a deep dive into one method, this spring course is a broader foray into educational data mining techniques.

How do students learn in 525?

Students engage with short (10-15 minute) video lectures, academic journal articles, individual assignments (reflective and enactive), collaborative partner exercises, and three summative short case studies.

Schedule of Topics

Week	Topic
1	The Learning Analytics Landscape
2	Data to Features
3	Prediction Models
4	Classifiers
5	Regressors
6	Case Study 1
7	Clustering
8	Factor Analysis
9	Association Rule Mining
10	Case Study 2
11	(Social) Network Analysis
12	Large Language Models (LLMs)
13	Ethics in AI
14	Case Study 3

What students say

“It was helpful to solve problems with our group and with our different perspectives.”

Yiting Wen, Education Freelancer, class of 2024

Instructor Insights

“Ed Psych 525 provides students the opportunity to apply data mining techniques to large educational datasets to gain important insights into how people learn. Teaching this course is a privilege – students bring a broad array of practitioner passions and insights to course content as they build the confidence to examine and critique ethical implications and equitable solutions like a data scientist. My favorite part about teaching this course is getting to know students and their professional interests through discussions and reflections. This course takes on a new personality every year – reflecting the distinct needs and interests of each cohort.” – Dr. Julie Johnson, 2024 instructor

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 partner assignments. Below is a sample from Week 4 of this course:

Week 4:	Prediction: Regressors
	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
Learning Objectives	Understand how different regression analyses work, different types of regressors, and the pros and cons of methodological choices
Reading(s)	Witten, Frank & Hall (2017); San Pedro, Snow, Baker, McNamara & Heffernan (2015)
Video Lecture	Watch Regressors video
Discussion Board Post			Written reflection on Witten and Mills readings by Thurs pm
Partner Reflection					Work with assigned partner, submit 1 joint response by Mon pm
Individual Practice Worksheet					View demo videos and annotated transcripts; Practice Worksheet using Rapid Miner by Mon pm

What More Students are Saying:

I loved 525, it had a great flow. I liked how it was presented, the workload was appropriate, and I had an amazing group… We were singing! We were able to leverage the tools, work in real time, and communicate with each other. I had been skeptical of doing group work online, but we were sad to have the semester come to the end. – Lauri Asbury

It followed a similar rhythm and fit into the mold that had been provided by the two previous classes in the program, which was a big positive for me. I was never overwhelmed week-to-week. – End-of-semester evaluation, 2023

The course provided a broad overview of many different applications of learning analytics, and I appreciated how the instructor connected each topic to her professional experience to showcase more concrete and personal examples. – End-of-semester evaluation, 2022

Each week’s reflection assignment was also engaging – not repetitive from previous weeks – and prompted us to truly apply the week’s concepts to our lives/interests. – End-of-semester evaluation, 2022

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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

The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document 1071) require that students with disabilities be reasonably accommodated in instruction and campus life. Reasonable accommodations for students with disabilities is a shared faculty and student responsibility. Students are expected to inform faculty [me] of their need for instructional accommodations by the end of the third week of the semester, or as soon as possible after a disability has been incurred or recognized. Faculty [I], will work either directly with the student [you] or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Disability information, including instructional accommodations as part of a student’s educational record is confidential and protected under FERPA.

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