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Improve teaching, learning and knowledge processes in a variety of contexts.
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.
The program is an induction to the broader global learning science communities. Monthly webinars will address the latest research in data, cognitive and organizational sciences, providing students with world-leading current knowledge of research and practices.
This program includes online courses and cohort-based admissions to promote greater student outcomes for working professionals around the world. A diverse cohort of learners provides important learning opportunities beyond the curriculum.
In addition to developing critical conceptual frameworks and technical skills, students will work collaboratively in cohorts to solve real-word problems.
The MSLA provides ongoing technology skill development through workshops in R, Python, AI, cloud computing, and other current and emerging tools and approaches. Program graduates will be well-versed in current and innovative data science methods and toolsets.
Extensive use of learning analytics to guide your progress through the program and demonstrate the power of data to support students through development of learner profiles, adaptive pathways, and personalized learning.
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.
Core Coursework (18 Hours):
Electives (12 Hours):
Prerequisites: Completion of LAPS 5310, LAPS 5320, LAPS 5330 and LAPS 5340 or LAPS 5360.
Additionally, students in the program will complete a capstone, pending the completion of coursework and approval of the department.
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.
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.
"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
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:
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:
Become a leader in the digitization of learning, sensemaking, and knowledge processes in complex information environments.