Curriculum

Master of Science in Learning Engineering: Curriculum

MSLE is an interdisciplinary program and students are encouraged to take electives from various departments within the university. This freedom allows you to tailor the program to your particular area of interest.

Current Carnegie Mellon students, staff and faculty may access the syllabus repository here to learn more about specific course offerings.

Degree Requirements

Seven MSLE Core Courses

All students are required to take the following seven core courses:

  • 05-823 E-Learning Design Principles and Methods
    Good design is a continuous improvement process that combines scientific principles and data-driven methods to achieve desired outcomes. E-learning design is no exception. In this course, you will learn how to design innovative e-learning: online interactions and technologies that make learning more effective and efficient. You will practice instructional design using learning science theories and principles, and apply learning engineering via data-driven methods to discover insights about how learners think. Instructional designers explain and use principles of learning and instruction such as proven ways to support learning-by-doing, like deliberate practice and self-explanation, and proven ways to support multimedia learning from text, visuals and audio. They employ “backward design”: designing and aligning learning goals, the assessments that measure them, and the instruction that achieves them. However, today’s learning engineers do not simply design in sequence — goals, then assessments, then instruction — but are agile and iterative. For example, they collect qualitative data by having an expert “think aloud” while performing one of their assessments and use the results to add or change goals. They also leverage quantitative data, for instance, by mining learning data from online course interactions or by comparing alternative designs in an A/B experiment. By using data, learning engineers create innovative and effective designs that surpass the results of those who rely on science and intuition alone. You will do the same in an end-to-end e-learning design project, where you will develop an e-learning module of your choice, continuously improve it, and test it in an A/B experiment.
  • 05-738 Evidence-Based Educational Design
    In this course, you will explore the essential principles of educational design, focusing on creating inclusive environments for diverse learners and promoting positive behavior. You will examine effective strategies for measuring learning outcomes, enhancing student engagement and assessing educational effectiveness. The coursework includes a thorough examination of current research in the learning sciences through various papers and textbooks. To bridge theory and practice, you will complete two hands-on projects that apply these concepts to real-world use cases. Class time will be spent discussing weekly readings, highlighting relevant case studies and engaging in group activities that foster collaboration. Ultimately, this course will prepare you for a career as an instructional designer, learning engineer, educator or researcher, enabling you to create effective educational designs and strategies.
  • 05-840 Tools for Online Learning
    This course will cover a variety of learning science principles and how they apply to tools used for online learning. You will examine what it means to make a “good” tool for learning, why it is difficult and how to create and prototype them. The bulk of this class centers on three learning mechanics: feedback and active learning, collaboration between learners and data-driven improvement. Using these mechanics as specific case studies, the course will teach you how to think about, build and study tools for both formal classroom education and informal learning environments. While we cover learning science principles, the focus of this course will be on the application of those principles as they are used in a variety of learning tools. The ultimate goal of this course is to give you hands-on experience working with a variety of tools, enabling you to better design, improve and utilize them for all types of learners.
  • 05-660 Interaction Design Fundamentals
    This course introduces the human-centered design process as well as fundamental interaction design principles, methods and practices. Working individually and in small teams, you will learn interaction design concepts and apply them to real-world problems. This course teaches how to: apply appropriate interaction design methods in a human-centered design process; create persuasive interim and final design artifacts that demonstrate communication design fundamentals; facilitate productive and structured critique across the class and with instructors; explain and apply fundamental interaction design principles; create clarity and readability in artifacts, including GUIs and deliverables, through the disciplined application of visual design principles such as typography, color and composition; practice reframing a given problem in order to create opportunities that drive the generation of multiple solutions; demonstrate habits that foster the creative process, including drawing, divergent thinking and creative experimentation; and explore core concepts within interaction design.
Students receive significant guided feedback from faculty and mentors.
Students receive significant guided feedback from faculty and mentors.
05-683 MSLE Capstone Project I (24-unit spring course)
05-686 Special Research or 05-888 Practicum
05-684 MSLE Capstone Project II (24-unit fall course)

Experiential learning is a key component of the MSLE program. Through a substantial team project, you will apply classroom knowledge in analysis and evaluation, implementation and design while developing skills working in multidisciplinary teams. The project begins in the spring semester, continues through a required summer internship or special research course (05-686 or 05-888) and resumes in the final fall semester. Note: Capstone I and II must be completed consecutively in the spring and fall semesters of a single calendar year.

Five Electives

You may use the five elective courses to tailor the program to your individual interests, background and goals.
 
Distributional Requirements:
  • You must choose a minimum of three electives split across at least two of the three distributional areas (Technology, Methods & Design and Learning Sciences Theory & Instructional Design). It is your responsibility to ensure that you fulfill the distributional requirements.
  • See the table below for the approved electives in each area.
  • Cross-listed electives can only count toward one distributional area.
  • Independent studies typically do not fulfill these requirements unless pre-approved by the program director.
Course Credit & Restrictions:
  • Course Size: Each elective must be a full-semester course (9 to 12 units). Two mini (half-semester, 6-unit) courses can be combined to count as one elective.
  • No Double-Counting: Electives must be different from your MSLE core courses and cannot have been applied toward any previously awarded CMU degree.

Note: Electives not listed below must be individually approved by the program director on a case-by-case basis to ensure they align with your career and academic goals.

Technology
  • Accessibility (05-899 B S22 & S21)#
  • AI Engineering (11-695) or Machine Learning in Production (17-645)
  • Advanced Natural Language Processing (11-711)
  • Algorithm Design and Analysis (15-651)
  • Applied Data Science (16-791)
  • Applied Deep Learning (17-644)*
  • Applied Machine Learning (05-834)
  • Building Technologies for the Resistance (05-899 D F24)#
  • Celebrating Accessibility (05-899 A F24)#
  • Cloud Computing (15-619)
  • Computational Methods for Interactive Systems (05-899 B S25)#
  • Data Science for Product Managers (05-898)* #
  • Data Visualization (05-619)
  • Database Management (95-703)
  • Design Center: Design for Digital Systems (51-828)
  • Design Educational Games (05-818)
  • Designing Human Centered Software (05-891)
  • Foundations of Computational Data Science (11-637)
  • Gadgets, Sensors and Activity Recognition in HCI (05-833)
  • HCI for Product Managers (05-898)* #
  • Human AI Interaction (05-618)
  • Human Language for AI (11-624, 11-724)
  • Interaction Techniques (05-640)
  • Interactive Data Science (05-839)
  • Introduction to Deep Learning (11-685)
  • Introduction to Machine Learning (10-601, 10-701)
  • Machine Learning for Text & Graph-based Mining (11-641) or Machine Learning with Graphs (11-741)
  • Multimodal Machine Learning (11-777)
  • Natural Language Processing (11-611)
  • Personalized Online Learning (05-832)
  • Practical Data Science (15-688)
  • Principles of Software Construction (17-514)
  • Programming Usable Interfaces (PUI) (05-630)**
  • Prompt Engineering (17-630)
  • Prototyping Algorithmic Experiences (05-685)
  • Python for Data Science (11-603)
  • Role of Technology in Learning in the 21st Century (05-838)
  • Software Project Management (17-632)*
  • Software Structures for User Interfaces (05-631)
  • Web Application Development (17-637)
 
Methods & Design
  • Advanced Interaction Design (05-661)
  • Agile Methods (95-874)*
  • Applied Research Methods (05-816)
  • Computer Science Perspectives in HCI (05-773)*
  • Data Science for Psychology & Neuroscience (85-732)
  • Design of Artificial Intelligence Products (05-617)
  • Design Educational Games (05-818)
  • Designing Experiences for Learning (51-886)
  • Designing Human Centered Software (05-891)
  • Designing with CARE (51-636)
  • Digital Ethnography (49-717)*
  • Digital Service Innovation (05-670)
  • Experimental Design for Behavioral and Social Sciences (36-749)
  • History and Future of Interaction Design (51-695)
  • Human Factors (05-813)
  • IDeATe: Learning in Museums (05-602)
  • Learning Media Design (05-691)
  • Personalized Online Learning (05-832)
  • Prototyping Algorithmic Experiences (05-685)
  • Research Methods for Design (51-744)
  • Service Design (05-652)
  • Social Perspectives in HCI (05-772)* 
  • The AI Augmented Designer (05-612)
  • The AI Augmented Designer (05-899)#
  • Transformational Game Design Studio (05-899)#
  • Transformational Game Design Studio (05-662)
  • User Centered Research & Evaluation (UCRE) (05-610)**
 
Learning Sciences Theory & Instructional Design
  • Applications of Cognitive Science (85-795, 05-795)
  • Cognitive Development (85-723)
  • Learning Analytics and Educational Data Science (05-899 B F23; 05-899 A S25)#
  • Persuasive Design (05-615)
  • Role of Technology in Learning in the 21st Century (05-838)
 
General Electives
Any two additional courses listed above or no more than two of the following:
  • AI Venture Studio (11-681)
  • Augmenting Intelligence (05-899 B F24)#
  • Data Analytics with Tableau (94-819)*
  • Decision Making Under Uncertainty (95-760)*
  • Design Center: Human Experience in Design (51-673)
  • Design Center: Methodology of Visualization (51-831)
  • Designing for Service (51-785)
  • Commercialization and Innovation: Strategy (45-807)*
  • Evaluate Research: Think Aloud Protocol and Usability Testing (05-898 C4)*#
  • Evidence-Based Management (94-814)*
  • Fairness, Accountability, Transparency, and Ethics in Sociotechnical Systems (05-899 A F22 & F23)#
  • Guest Experience in Theme Park Design (53-612)
  • Human Robot Interaction (16-867)
  • Independent Study (05-680)
  • Language and Statistics (11-761)
  • Machine Learning with Graphs (11-741)
  • Negotiations for Software Leaders (17-693)*
  • Product Management Essentials I (17-619 or 17-692)*
  • Product Management Essentials II (17-629)*
  • Quality Assurance (17-623)*
  • Second Language Acquisition: Theories and Research (82-783)
  • Social Agent (05-899)#
  • Social Web (05-820)
  • Talking to Robots (11-851) is offered in 6 or 12 units
  • Technology Outreach and Engagement in the Pittsburgh Community (67-706)
  • Topics in Second Language Acquisition (82-888)
  • Topics on Ethics for AI (80-836)
  • Transition Design (51-702)
  • Tutoring, Teaching and Leading through Education (99-761)
  • UX Research with Quantitative Data Sources (05-898 D3)* #
  • Unstructured Data Analytics (95-865)*
  • Other possibilities if approved by MSLE Director. To request approval, click here.

*Mini-course: Counts as half of one elective
**Available in the Spring semester for MSLE students. In the Fall semester, PUI and UCRE are both reserved for MHCI students. Others may take them if and only if space is available and with the instructor’s permission. Non-MHCI students who register for these courses in the Fall without the instructor’s approval will be removed without warning.
#05-898 and #05-899 Special Topics in HCI offer different topics across sections and semesters. Some of these topics may only be offered once or twice.

Required Statistics Prerequisite

Carnegie Mellon’s MSLE is a rigorous interdisciplinary program, and while every student arrives with their own unique talents, one course or the equivalent, demonstrable knowledge of statistics is required for entry. This prerequisite includes basic concepts, logic and issues involved in statistical reasoning, such as probability theory, methods for statistical inference, introductory research methods, exploratory data analysis and the use of some statistical tests in regression analysis and contingency tables (equivalent CMU courses are 36-220 and 36-202). If you do not have this foundational knowledge, we offer a free online course that you can take prior to matriculating in August. However, if this course is required for you, you must successfully complete it in order to matriculate in the Fall semester.

Sample Plans of Study

Full-time Study
The MSLE degree is a four-semester graduate program. The following is a sample full-time plan of study that takes into account required course sequences. 

Fall Spring Summer
05-823 E-Learning Design Principles
05-738 Evidence-Based Educational Design
05-660 Interaction Design Fundamentals
Elective 1
Elective 2
05-683 MSLE Capstone Project I
05-840 Tools for Online Learning
Elective 3
Elective 4
05-888 Practicum
OR
05-686 Special Research
 
Second Fall    
05-684 MSLE Capstone Project II
Elective 5
Any remaining electives
   

* International students

  • are limited to taking one remote course in the first 36 units. Additional courses taken after the first 36 units may be delivered in any modality. For more information, see the OIE page on modality requirements.
  • must maintain full-time status by enrolling in a minimum of 36 units per semester. However, in your final fall semester, you may take fewer than 36 units with OIE approval. 

Part-Time Study
For U.S. citizens and permanent residents, the program offers the flexibility to enroll on a part-time basis. Because visa regulations require international students to maintain full-time status, this part-time path is not available to those on a visa. By choosing this option, you can tailor your coursework to fit your specific needs while working closely with an advisor to establish your plan of study. Ideally, you should be able to complete the degree within a period of two years by taking two courses per semester, including summers. You must also be aware that our core courses are held during the day, so it is not possible to complete the degree as a night student; additionally, we cannot guarantee that electives will be available during the summer.

The following is a sample part-time plan of study that keeps in mind required course sequences. 

First Fall First Spring First Summer
05-738 Evidence-Based Educational Design
05-823 E-Learning Design Principles
Elective 1
Elective 2
Elective 3
Elective 4
   
Second Fall Second Spring Second Summer
05-660 Interaction Design Fundamentals
Elective 5
05-840 Tools for Online Learning
05-683 MSLE Capstone Project I
05-888 Practicum
or 
05-686 Special Research
 
Third Fall    
05-684 MSLE Capstone Project II    

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