Learning Analytics and Artificial Intelligence, MSEd

The Learning Analytics Online Master’s Degree will empower you to leverage data analytics and artificial intelligence (AI) to drive high-quality decisions within the educational context, and develop adaptive learning systems that leverage analytics and AI to improve student outcomes. The program prepares data scientists to develop advanced skills in measurement, analysis, and predictive modeling, leveraging state-of-the-art methodologies such as machine learning, generative AI, and deep learning, while avoiding algorithmic bias. Students will also develop skills in real-time data analysis and visualization, personalized learning recommendation generation, and data management, equipping them to develop and enhance data-driven educational environments. 

This fully online program prepares graduates to work as data scientists and AI practitioners in research and development in areas such as at-risk prediction, and AI-based adaptive learning systems such as intelligent tutoring systems and educational recommender systems. You will emerge understanding when and why to use different methods for a range of applications in order to make a real-world impact. The program teaches you both the latest AI techniques, learning analytics algorithms and tools as well as how to engineer data streams to turn raw data into features that are interpretable to humans and LLMs. Your use of contemporary methods will be grounded in the rich history of educational thought, with an understanding of how this grounding can support efforts to address challenges such as algorithmic bias to improve educational outcomes at scale.

Curriculum

This program requires a total of 10 CUs: eight of these will involve core required courses and the remaining two CUs will involve concentration courses. Coursework in this program focuses on AI components and AI application in educational settings.

The program includes 9 CUs of instruction and 1 CU of a capstone seminar, where students will develop projects with real-world relevance and of a quality that can be submitted as a demo or short papers to international conferences. The Learning Analytics Capstone Seminar course provides the foundation leading to the Capstone project.

Required Courses
EDUC 5144Dashboards for Discovery and Learning Applications1
EDUC 6116Master's Foundations of Teaching and Learning1
EDUC 6123Big Data, Education, and Society1
EDUC 6190Feature Engineering1
EDUC 6191Core Methods in Educational Data Mining1
EDUC 6195Capstone Seminar: Learning Analytics1
Take 2 additional course units (CUs) of EDUC courses at the 5000 level or above. They should focus on LLMs, Deep Learning, and Transformer Models. See advisor for details.2
Additional Requirements
EDUC 5183Adaptive Learning Systems1
EDUC 6185Databases and Data Management1
Other Requirements
Master's Capstone Project, supported by EDUC 6195
Total Course Units10

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2025 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

Learning Analytics and Artificial Intelligence, MSEd and Computer & Information Technology, MCIT (online) Dual Degree

Dual Degree Requirements
Computer and Information Technology Requirements
CIT 5910Introduction to Software Development1
CIT 5920Mathematical Foundations of Computer Science1
CIT 5930Introduction to Computer Systems1
CIT 5940Data Structures and Software Design1
CIT 5950Computer Systems Programming1
CIT 5960Algorithms and Computation1
Two Electives 12
Learning Analytics and Artificial Intelligence Requirements
EDUC 6191Core Methods in Educational Data Mining1
EDUC 6116Master's Foundations of Teaching and Learning1
EDUC 6190Feature Engineering1
EDUC 6195Capstone Seminar: Learning Analytics1
EDUC 5918Large Language Model Seminar1
EDUC 6XXX - Deep Learning and Transformer Models1
Two EDUC Electives 22
Other Requirements
Masters Capstone Project, supported by EDUC 6195
Total Course Units16
1

Electives cannot be taken from EDUC courses. Students may select other online courses within Engineering. 

For MCIT Online students pursuing the dual degree, they may choose to substitute up to 2 of the following courses from the MCIT Online curriculum to satisfy up to 2 CUs toward the Learning Analytics and AI degree:

  • CIS 5450: Big Data Analytics

  • CIS 5210: Artificial Intelligence

  • CIS 5300: Natural Language Processing

2

Electives are taken from other online EDUC courses offered by the Learning Analytics and Artificial Intelligence program. 

For Learning Analytics and AI students pursuing the dual degree, they may choose to substitute up to two of the following courses from the Learning Analytics and AI curriculum to satisfy up to 2 CUs toward the MCIT Online degree:

  • EDUC 6123 Big Data, Education, and Society

  • EDUC 6185: Databases and Data Management*

  • EDUC 5183: Adaptive Learning Systems

  • EDUC 5144: Dashboard for Discovery and Learning Applications

​*Elective that cannot be taken by dual degree students without them having to take an additional required course.