UTA M.S. in Learning Analytics

Interactions in digital environments produce data. Learning analytics is the field of study that uses this data to understand what individuals have learned, how teams perform most effectively, and the networks that support social and organizational knowledge development. The University of Texas at Arlington’s Master of Science in Learning Analytics (MSLA) is the world’s first fully online program intended for individuals who want to pursue a career in fields that are impacted by the digitization of learning, sensemaking, and knowledge processes in complex information environments.

M.S. Learning Analytics Degree Plan

The 36-credit hour program offers two pathways, with one requiring a traditional research thesis and the other requiring a capstone project. Additionally, students following both paths will complete six core courses and electives that suit their needs and contexts. 

Course of Study

  • 18 hours in Learning Analytics Core Coursework
  • 12 hours of Learning Analytics Electives
  • 6 hours of Learning Analytics Capstone

Core Coursework (18 Hours):

  • LAPS 5310 Learning Analytics Fundamentals (3 hrs)
  • LAPS 5320 Experimental Design & Methodology (3 hrs)
  • LAPS 5330 Psychology of Learning & Learning Sciences (3 hrs)
  • LAPS 5340 Big Data Methods (3 hrs)
  • LAPS 5350 Privacy & Ethics in Learning Analytics (3 hrs)
  • LAPS 5360 Intro to Data Analysis and R (3 hrs)

Electives (12 Hours):
Prerequisites: Completion of LAPS 5310, LAPS 5320, LAPS 5330 and LAPS 5340 or LAPS 5360.

  • LAPS 5370 Intro to Statistical Analysis (3 hrs)
  • LAPS 5375 Probability & Statistical Inference (3 hrs)
  • LAPS 5376 Applied Regression Analysis (3 hrs)
  • LAPS 5377 Linear Models & Experimental Design (3 hrs)
  • LAPS 5378 Multidimensional Scaling & Clustering (3 hrs)
  • LAPS 5380 Causal Inference for Program Evaluation (3 hrs)
  • LAPS 5388 Advanced Methods in Educational Data Management/Learning Analytics (3 hrs)
  • LAPS 5390 Learning Design & Analytics (3 hrs)
  • LAPS 5391 Independent Study (3 hrs)
  • LAPS 5392 Cognition, Computers & Metacognition (3 hrs)
  • LAPS 5393 Natural Language Processing for Educational Research (3 hrs)
  • LAPS 5394 Social Network Analysis (3 hrs)
  • LAPS 5395 Human & Artificial Cognition (3 hrs)

Capstone:
Additionally, students in the program will complete a capstone, pending the completion of coursework and approval of the department.

  • LAPS 5610 Learning Analytics Capstone (6 hrs)

Why Learning Analytics?

Learning analytics is a rapidly growing area of research and practice that uses data science to make sense of the world and to improve teaching, learning, and knowledge processes in a variety of contexts, such as informal settings, schools, universities, corporations, and non-profit organizations. It sits at the nexus of learning science, education, computer science, and psychology and uses a range of analytics approaches.

Students will gain critical, in-demand skills to be better positioned to work in an increasingly complex global knowledge economy and to address social and knowledge challenges. Program graduates will be leaders in computational social science and will be able to prepare organizations for the future of learning, including sensemaking and artificial intelligence. Additionally, graduates will have the skills and expertise to use data generated through human interactions to create insights into social trends, knowledge networks, and organizational performance. 

 

Course Descriptions

Introduction to foundational elements in the emerging field of learning analytics, including theory, philosophy, ethics, cognitive processes, and tools, as well as its contribution to the psychology of learning research and relationship with other academic fields.
The collection, analysis, and reporting of large-scale educational and social interaction datasets, including the survey of different types of data, data infrastructure, methods for managing and interacting datasets, governing policies, and data stewardship.
Ethical considerations for the collection and use of learning data, including social and trust practices with learners, access, ownership, storage, security, privacy, policy, transparency, and algorithms.

This course will provide students who receive probationary admission due to an inadequate mathematical background with the core principles of statistical analysis necessary to be successful in the program.  

Using learning analytics to determine the impact of intervention outcomes and critically evaluate quantitative research pertaining to cause and effect in a learning context. This will include potential pitfalls and key factors, as well as application of both practitioner and research lenses.  

 

Sophisticated and emerging techniques for analyzing learning data, including advanced graphing and visualization techniques, multimodal data (such as psychophysiological data), modeling, process mining, measurement of psychological attributes involved in knowledge creation, and learner profile development. 

Survey of foundational learning design theories related to human behavior in formal and informal learning settings. Focus on models and strategies to design and evaluate technology that supports and helps improve learning. 

Introduction to social network analysis in educational settings. The course focuses on how to analyze and interpret relationships between people, artifacts, and text in digital learning settings. The students will learn to prepare data, map and analyze these relationships. Foundational graph analysis concepts and their application in learning analytics will be discussed. Students will be trained to use R programming for analysis, but the use of other software is possible. 

Artificial intelligence (AI) is exerting growing influence in all aspects of modern life. This course surveys AI trends and details prominent models for how human and machine agents intersect in knowledge work, using discrete cognitive processes as the basic unit for determining agent roles. A specific focus is on optimal relationship determination and the data types that provide indicators of cognitive states.
Application of program knowledge and skills learned in prior coursework to complete a small-scale, integrative project involving analysis of a real world, educational data set. Students will have the opportunity to apply for competitive internships that will provide small scholarships. All students will to work in diverse groups of 5 to 6 students along with a faculty mentor analyzing specific industry data to solve real-world problems. The small groups will be designed to combine students with diverse skill sets and emphasize community and collaboration. Prerequisite: Completion of coursework and approval of department.

Wide-Ranging Opportunities...

"Graduates of this program will have wide-ranging opportunities, from positions in government agencies and academic settings to large corporations. Any organization that deals with an abundance of data to try to understand social and knowledge processes needs employees with this expertise.”

- Dr. George Siemens, UTA Psychology Professor

Learning Analytics FAQs

UTA provides need-based financial aid and scholarships to qualified individuals. Incoming domestic students are encouraged to fill out a FAFSA to determine need-based aid. A listing of current UTA scholarships is available in the Mav ScholarShop.

Scholarship recipients who are nonresidents of Texas or citizens of a country other than the United States of America may be eligible to pay the in-state tuition rate if they are offered a competitive scholarship through UTA. The competitive scholarships that may be considered for an out-of-state tuition waiver must be a minimum of $1,000 for the period of time within the academic year covered by the scholarship, not to exceed 12 months. Please note that the out-of-state tuition waiver is not guaranteed, is contingent upon funding and may vary in availability.


The program is intended to be completed online, with no travel to campus required. Given the global nature of the program and challenges with time zones, in addition to considerations for working professionals, we will primarily utilize an asynchronous format, but there will be some live sessions from time-to-time.

No, at this time, the GRE is not required for admission to this program.

Faculty and staff will evaluate all applicants for admission to the program and priority will be given to applicants who meet the following criteria:

  1. Overall undergraduate GPA of 3.2 
  2. An applicant whose native language is not English must demonstrate a sufficient level of skill with the English language to assure success in graduate studies. This requirement will be waived for non-native speakers of English who possess a Bachelor’s degree from an accredited US institution. Applicants are expected to submit a score of at least 550 on the paper-based TOEFL, a score of at least 213 on the computer-based TOEFL, a minimum score of 40 on the TSE, a minimum score of 6.5 on the IELTS, or a minimum TOEFL IBT total score of 79. Further, When the TOEFL IBT is taken, sectional scores of at least 22 on the writing section, 21 on the speaking section, 20 on the reading section, and 16 on the listening section are preferred. However, admission to any graduate program is limited and competitive. Meeting the minimum admission requirements does not guarantee acceptance and programs may give preference to students with higher scores. Only scores submitted directly by ETS or IELTS to UT Arlington are acceptable.

Students who do not meet these criteria may still be considered if the meet all of the general admissions requirements of the Graduate School. Admission is competitive and meeting the admission requirements will not ensure acceptance in the program.

Yes! Prospective international students who reside outside of the U.S. and have no plans for establishing F-1 or J-1 student status are eligible for program admission. Prospective students who have:

  • F-1 or J-1 visa status and reside in the U.S. are not eligible for program admission.
  • F-2 visa status are eligible for program admission, but can take no more than three (3) credit hours per semester.
    • Given the cohort model for the program (six (6) hours per term with a specific schedule for course offerings), this means it would be difficult to progress and complete quickly.
  • J-2 visa status are eligible for program admission.
  • B-1 or B-2 visa status are not eligible for admission to this program.
Yes! UTA has a variety of established and emerging degree programs related to Data Science to fit a number of student needs and career paths. In addition to the emerging master's program in Learning Analytics the College of Science has a bachelor's degree and minor in Data Science, the UTA College of Business offers a bachelor's and master's program in Business Analytics, UTA's Math Department has a PhD in Data Science and the College of Engineering has announced plans for a master's program in Data Science. Visit UTA.edu to browse course offerings and determine which program is right for you.

Be a Maverick Scientist

Become a leader in the digitization of learning, sensemaking, and knowledge processes in complex information environments.