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. With the rapid rise of digital technologies for work and learning, this program is ideal for anyone interested in learning how to use data to gain insight into how people and systems produce knowledge. The program will benefit faculty, teachers, corporate learning specialists, sociologists, or others interested in using data as a research instrument to understand human social and cognitive processes.
The MS LA has been designed as both a research program and opportunity for direct application into real-world settings. The core faculty in the program are amongst the most accomplished globally and have established and developed the field of learning analytics since its inception more than a decade ago.
As an interdisciplinary program, students will learn core concepts in Psychology, Education, Learning Sciences, and Computer Science, enabling them to solve complex problems through the use of data, complexity science methods, and research in human cognition and behavior. Students will also develop skills to evaluate how systems and organizations are impacted by digitization and datafication and prepare for ongoing prominence of artificial intelligence in all aspects of society. Students will work in teams and cohorts, providing creative solutions to real world problems. After program completion, graduates will be proficient in both the technical and social aspects of solving complex challenges and will be able to provide clear action plans and paths forward for individuals and organizations. As a result, they will be in demand to help schools, universities, corporations, non-profit organizations, government agencies, and startups to make sense of their learning and knowledge-related data.
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.
Why is this program unique?
Our goal for the program is to provide students with integrated and nuanced understanding of how to use data to improve learning, organizational performance, how teams perform, and how social systems solve problems and evolve. To meet these goals, UTA’s MS LA has several unique features over other academic programs, including:
- Community. The program of study is an induction to the broader global learning analytics and learning sciences communities. Monthly webinars will address the latest research in data science, cognitive sciences, and organizational science, providing students with world-leading current knowledge of research and practices.
- Global Network. 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.
- Current skills. 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.
- Walk the talk. 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.
- Real-world learning. In addition to developing critical conceptual frameworks and technical skills, students will work collaboratively in cohorts to solve real-word problems.