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.

Methodologies in learning analytics research, including the philosophy of science, measurement, and complex experimental and quasi-experimental designs.

Exploration of knowledge processes such as learning, sensemaking, decision-making, and self-regulation with focus on psychological processes and the science of learning.

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.

Fundamental elements of conducting data analysis in the R programming language, including basic operations, data structures, dataset cleaning and manipulation, and visualization.

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.  

Examination of probability, distributions, estimation, and hypothesis testing in learning contexts.  

A comprehensive review of different regression models that emphasizes modeling, inference, diagnostics, and application to educational datasets.  

In-depth exploration of univariate and multivariate linear models to derive inferential procedures depending on appropriate learning contexts. 

In-depth study of the investigation of observed similarities and dissimilarities between different objects and then grouping the objects based on those similarities. 

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. 

Student and instructor agree upon topic of study and requirements for deadlines and products. 

The role of learning, sensemaking, human development, and cognition theories in relation to the use of digital technology in knowledge processes. 

Application of methods in natural language processing (NLP) and natural language understanding (NLU) to text and language data in the educational setting.  

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.

The University of Texas at Arlington has approved a modified tuition model for the Master of Science in Learning Analytics program that provides comparable tuition and fees between prospective Texas resident and non-resident (out-of-state and international) students. Per 2021-2022 academic year rates, the program may be completed under $22,000 USD (tuition and fees are subject to change each academic year).

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.

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.