Twin Cities campus

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Twin Cities Campus

Learning Analytics Postbaccalaureate Certificate

Curriculum & 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
  • Program Type: Post-baccalaureate credit certificate/licensure/endorsement
  • Requirements for this program are current for Spring 2022
  • Length of program in credits: 12
  • This program does not require summer semesters for timely completion.
  • Degree: Learning Analytics Postbaccalaureate Certificate
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
  • partially online (between 50% to 80% of instruction is online)
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:
  • TOEFL
    • Internet Based - Writing Score: 23
    • Internet Based - Reading Score: 23
    • Paper Based - Total Score: 550
  • IELTS
    • Total Score: 6.5
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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· College of Education and Human Development

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· Fall 2022

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CI 5371 - Learning Analytics: Theory and Practice
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Learning analytics as a nascent field is broadly defined as the "measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs." This course aims to provide a general, non-technical survey of learning analytics, as well as its application in various educational contexts. In particular, we will discuss foundations of learning analytics, survey pertinent education theories, discuss new forms of assessment, explore popular data mining techniques, review learning analytical tools and case studies, and de- sign analytics for our own interested contexts. Given the breadth of this field, additional support is provided for deep dives in special interest areas. Overall, this course provides a comprehensive, theory-driven overview of learning analytics to orient students to this nascent field and prepare them for advanced research/practice in learning analytics.
CI 5331 - Introduction to Learning Technologies
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
An exciting look at the field of learning technologies (LT), examining the numerous opportunities this area of study brings to individuals who decide to pursue a LT degree. Students engage in numerous real-world projects as they come to understand both the past and future of technology in education, business, and society as a whole.
EPSY 5114 - Psychology of Student Learning
Credits: 3.0 [max 3.0]
Course Equivalencies: EPsy 3301/EPsy 5114
Grading Basis: A-F only
Typically offered: Every Fall
This course is an introduction to the theories, data, and methods of Educational Psychology most relevant to understanding student thinking and learning. The first third of the course reviews those aspects of cognitive development that are foundational for education. The second third considers how cognitive psychology informs questions of learning, memory, knowledge, and transfer. With this background in place, the final third of the course will focus on the classroom: on instruction, motivation, individual differences, and group differences. The course concludes by considering the neural correlates of classroom learning.
EPSY 8113 - The Psychology of Scientific Reasoning
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Research at intersection of cognitive science, educational psychology, science education. What psychology tells us about how people think, reason, make decisions. Read empirical research that explores psychological processes that underlie scientific reasoning. prereq: 5114 or equivalent
EPSY 8114 - Seminar: Cognition and Learning
Credits: 3.0 [max 9.0]
Typically offered: Periodic Fall & Spring
Advanced study in critical analysis and application of contemporary psychological theory and research in cognition and learning for education.
EPSY 8116 - Reading for Meaning: Cognitive Processes in the Comprehension of Texts
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Cognitive processes that take place during reading comprehension/implications of these processes for instruction/assessment.
EPSY 5261 - Introductory Statistical Methods
Credits: 3.0 [max 3.0]
Course Equivalencies: EPsy 3264/5231/5261/5263
Typically offered: Every Fall, Spring & Summer
EPSY 5261 is designed to engage students in statistics as a principled approach to data collection, prediction, and scientific inference. Students first learn about data collection (e.g., random sampling, random assignment) and examine data descriptively using graphs and numerical summaries. Students build conceptual understanding of statistical inference through the use of simulation-based methods (bootstrapping and randomization) before going on to learn parametric methods, such as t-tests (one-sample and two-sample means), z-tests (one-sample and two-sample proportions), chi-square tests, and regression. This course uses pedagogical methods grounded in research, such as small group activities and discussion. Attention undergraduates: As this is a graduate level course, it does not fulfill the Mathematical Thinking Liberal Education requirement. If you would like to take a statistics course in our department that fulfills that requirement, please consider EPSY 3264.
EPSY 8264 - Advanced Multiple Regression Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
General linear model used as context for regression. Matrix algebra, multiple regression, path analysis, polynomial regression, standardized regression, stepwise solutions, analysis of variance, weighted least squares, logistic regression. prereq: [8252 or equiv], regression/ANOVA course, familiarity with statistical analysis package
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
IE 5561 - Analytics and Data-Driven Decision Making
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language. Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
CI 8145 - Using Mixed Methods in Educational Research
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Conceptual issues surrounding design/use of mixed methods in addressing problems/research questions in education. Critique of select mixed design exemplars published in respected research publications/practical application of analyses of data using mixed inquiry methods. prereq: [8133, 8148, OLPD 8812] or equiv, [CI PhD student or instr consent], additional quantitative/qualitative methodology courses recommended
CI 8371 - Applied Social Network Analysis in Education
Credits: 3.0 [max 3.0]
Typically offered: Spring & Summer Odd Year
This course examines the application of Social Network Analysis in various educational settings. As a methodology, Social Network Analysis (SNA) is concerned with social affiliations and interactions in social structures of all kinds. SNA has garnered significant interests in educational research and has been applied to investigating a myriad of educational phenomena such as student friendship, school choice, and classroom discourse. This course is organized into four major components including: (1) foundations of social network perspectives in education; (2) techniques for collecting social network data in educational settings; (3) techniques for analyzing and visualizing social networks; and (4) practical guidelines on conducting SNA research in educational contexts, with considerations to education theories, ethics, and real-world implications.
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5609 - Visualization
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization. prereq: [1913, 4041] or equiv or instr consent
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc. prereq: Familiarity with Java, C++, or Python