University of Texas Arlington

Master of Science in Learning Analytics (MSLA)

Overview

The Master of Science in learning analytics (MSLA) is 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 socio-technical environments. This program is ideal for anyone interested in learning how to use data to gain insight into how people and systems produce knowledge.

About The Program

The MSLA will prepare students coming from different backgrounds to meet the growing demand for learning analytics professionals across a variety of industries, including education, nonprofit, government, and corporate settings. Graduates will gain critical skills for working in an increasingly complex global knowledge economy and be well-positioned to become leaders in their organizations, preparing them for the future of learning.

The online, two-year program consists of six core courses, four electives composed of a variety of topics, and a collaborative capstone project (36 credit hours).

Degree Plan

Thirty-six (36) credit hours are required for the Master of Science in Learning Analytics. Required courses are the following:

• Core Courses (18 hours): LAPS 5310, 5320, 5330, 5340, 5350, 5360
• Four Electives (12 hours) from: LAPS 5370, 5375, 5376, 5377, 5378, 5380, 5388, 5390, 5391, 5392, 5393, 5394, 5395
• Capstone (6 hours): LAPS 5610

If a prospective student does not have sufficient statistical experience in prior coursework, they may be required to take LAPS 5370 – Introduction to Statistical Analysis as a leveling course at the end of their core coursework. This course would count as one of four required electives.

After completing 30 hours of coursework (18 hours core, 12 hours elective) and receiving approval from the program coordinator, students may enroll in the LAPS 5610 Capstone course. Students will work in diverse groups of 5 to 6 students along with a faculty mentor, and the small groups will be designed to combine students with diverse skill sets and emphasize community and collaboration. Students will apply program knowledge and skills learned in prior coursework to complete a small-scale, integrative project involving the analysis of a real-world, educational data set. Students will have the opportunity to apply for competitive internships that will provide small scholarships. Once the student enrolls in this course, they must continuously enroll in it until they successfully complete their capstone, but no more than 4 times.

**Note: At this time, students in the Learning Analytics program may not earn a Master’s in Passing in order to complete a Ph.D.

Here is a sample program of study:

Year 1 (Fall)

• LAPS 5310 Learning Analytics Fundamentals
• LAPS 5360 Introduction to Data Analysis and R

Year 1 (Spring)

• LAPS 5320 Experimental Design and Methodology
• LAPS 5330 Psychology of Learning and Learning Sciences

Year 1 (Summer)

• LAPS 5340 Big Data Methods
• LAPS 5350 Privacy & Ethics in Learning Analytics

Year 2 (Fall)

• Two (2) electives

Year 2 (Spring)

• Two (2) electives

Year 2 (Summer)

• LAPS 5610 Capstone

Current electives include:

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

Career Opportunities

• Data Scientist
• Education Administrator
• Learning Designer
• Research/Data Analyst
• Evaluation

Why Choose Us?

• Online program with cohort-based admissions to support working professionals around the world.
• Courses taught by leading learning analytics experts.
• Collaboration with faculty, students, and external partners to address real-world, complex, socio-technical challenges.
• Skill development for current and innovative methods and tools.

Admission Requirements

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.

At this time, the GRE is not required for admission to this program.

Students who do not meet these criteria may still be considered if they 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.

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 residence in the U.S. are not eligible for program admission.
• F-2 visa status is 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.
  • 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 is eligible for program admission.
• B-1 or B-2 visa status are not eligible for admission to this program.

Prospective students may apply at any time, but the deadline for the Fall 2021 semester is July 30, 2021. While it may be possible, we cannot guarantee admission after that date. Please note that this deadline is different from the general university application deadline. It is the student’s responsibility to adhere to departmental deadlines to ensure timely processing and review of their application.