Campuses:
This is archival data. This system was retired as of August 21, 2023 and the information on this page has not been updated since then. For current information, visit catalogs.umn.edu.
Twin Cities Campus
Learning Analytics Postbaccalaureate CertificateCurriculum & Instruction
College of Education and Human Development
Link to a list of faculty for this program.
Contact Information
Department of Curriculum and Instruction, 125 Peik Hall, 159 Pillsbury Drive S.E., Minneapolis, MN 55455 (612-625-4006; fax: 612-624-8277)
Email:
CIinfo@umn.edu
Along with the program-specific requirements listed below, please read the
General Information section of
this
website for requirements that apply to all major fields.
The Learning Analytics postbaccalaureate certificate aims to develop skills in the use of data to optimize learning and the environments in which it occurs. Learning analytics uses the power of information technology and data science to improve learning and teaching in various contexts.
With the rise of big data, knowing how to effectively and ethically utilize educational data to inform research and practice is crucial. The Learning Analytics Certificate advances these missions by leveraging information technology, data analytics, and learning sciences to better inform educational research and practice.
The certificate is structured to develop understanding in three core areas - foundations, theory, and analytics - while offering flexibility in coursework to accommodate students from different academic backgrounds and programs.
Program Delivery
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program
is 3.00.
A bachelor's degree from an accredited college or university in psychology, education, computer science, math, statistics, engineering, or a related field.
Other requirements to be completed before admission:
The undergraduate degree must include research methods and statistics.
Special Application Requirements:
International students who want to attend this program on a student visa should contact the University's International Student and Scholar Services (ISSS) office at https://isss.umn.edu/.
International applicants must submit score(s) from one of the following tests:
Key to test
abbreviations
(TOEFL, IELTS).
For an online application or for more information about graduate education admissions, see the
General Information section of this
website.
Program Requirements
Use of 4xxx courses towards program requirements is not permitted.
Required Coursework (9 credits)
Select 3 credits from each of the following areas, in consultation with the advisor, for a total of 9 credits.
Foundations (3 credits)
Take the following course:
CI 5371 - Learning Analytics: Theory and Practice
(3.0 cr)
Theory (3 credits)
Select 3 credits from the following in consultation with the advisor:
CI 5331 - Introduction to Learning Technologies
(3.0 cr)
EPSY 5114 - Psychology of Student Learning
(3.0 cr)
EPSY 8113 - The Psychology of Scientific Reasoning
(3.0 cr)
EPSY 8114 - Seminar: Cognition and Learning
(3.0 cr)
EPSY 8116 - Reading for Meaning: Cognitive Processes in the Comprehension of Texts
(3.0 cr)
Analytics (3 credits)
Select 3 credits from the following in consultation with the advisor:
EPSY 5261 - Introductory Statistical Methods
(3.0 cr)
EPSY 8264 - Advanced Multiple Regression Analysis
(3.0 cr)
CSCI 5521 - Machine Learning Fundamentals
(3.0 cr)
CSCI 5523 - Introduction to Data Mining
(3.0 cr)
IE 5561 - Analytics and Data-Driven Decision Making
(4.0 cr)
STAT 5302 - Applied Regression Analysis
(4.0 cr)
Elective Coursework (3 credits)
Select 3 credits from the following in consultation with the advisor. Courses applied to the required coursework requirement cannot also be applied as an elective.
CI 8145 - Using Mixed Methods in Educational Research
(3.0 cr)
CI 8371 - Applied Social Network Analysis in Education
(3.0 cr)
CSCI 5521 - Machine Learning Fundamentals
(3.0 cr)
CSCI 5523 - Introduction to Data Mining
(3.0 cr)
CSCI 5609 - Visualization
(3.0 cr)
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
(3.0 cr)
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Credits: | 3.0 [max 3.0] |
Typically offered: | Every Fall |
Credits: | 3.0 [max 3.0] |
Grading Basis: | A-F or Aud |
Typically offered: | Every Fall |
Credits: | 3.0 [max 3.0] |
Course Equivalencies: | EPsy 3301/EPsy 5114 |
Grading Basis: | A-F only |
Typically offered: | Every Fall |
Credits: | 3.0 [max 3.0] |
Typically offered: | Periodic Spring |
Credits: | 3.0 [max 9.0] |
Typically offered: | Periodic Fall & Spring |
Credits: | 3.0 [max 3.0] |
Typically offered: | Every Spring |
Credits: | 3.0 [max 3.0] |
Course Equivalencies: | EPsy 3264/5231/5261/5263 |
Typically offered: | Every Fall, Spring & Summer |
Credits: | 3.0 [max 3.0] |
Typically offered: | Every Fall |
Credits: | 3.0 [max 3.0] |
Typically offered: | Periodic Fall |
Credits: | 3.0 [max 3.0] |
Typically offered: | Periodic Fall & Spring |
Credits: | 4.0 [max 4.0] |
Typically offered: | Every Spring |
Credits: | 4.0 [max 4.0] |
Typically offered: | Every Fall, Spring & Summer |
Credits: | 3.0 [max 3.0] |
Grading Basis: | A-F or Aud |
Typically offered: | Every Fall & Spring |
Credits: | 3.0 [max 3.0] |
Typically offered: | Spring & Summer Odd Year |
Credits: | 3.0 [max 3.0] |
Typically offered: | Periodic Fall |
Credits: | 3.0 [max 3.0] |
Typically offered: | Periodic Fall & Spring |
Credits: | 3.0 [max 3.0] |
Typically offered: | Fall Even Year |
Credits: | 3.0 [max 3.0] |
Typically offered: | Spring Even Year |