Masters of Educational Technology and Applied Learning Sciences: Curriculum

METALS 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

Six METALS Core Courses

All students are required to take the following six core courses (05-823, 05-73805-840, 05-660, 05-681 and 05-682):

  • Core Courses 

05-823 E-Learning Design Principles and Methods

 “e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning’ by Clark & Mayer
E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning by Clark & Mayer

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, that is, online interactions and technology that make learning more effective and efficient. You will practice instructional design using learning science theories and principles and learning engineering using 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. But today’s learning engineers do not simply design in sequence — goals then assessments then instruction — but are agile and iterative. They collect qualitative data, for example, by having an expert “think aloud” while performing one of their assessments and use the results to add or change goals. They collect and use quantitative data, for example, 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 unlike the results of others who rely on science and intuition alone. You will do so too in an end-to-end e-learning design project, where you 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, we will explore the essential principles of educational design, focusing on creating inclusive environments for diverse learners and promoting positive behavior. We will explore effective strategies for measuring learning outcomes, enhancing student engagement, and assessing educational effectiveness. Students will prepare for careers as instructional designers, learning engineers, educators, and researchers as we cover the range of topics in this class. The coursework includes a thorough examination of current research in learning sciences through various papers and textbooks. Additionally, students will apply these theoretical principles practically by completing two hands-on projects, seeing the direct application of the concepts to real-world use cases. Class time will be spent discussing the weekly readings, highlighting relevant case studies, and engaging in group activities that foster collaboration and practical application of the material covered. This course will prepare students for real-world challenges in educational design and help integrate learning science knowledge through practical experience, enabling them 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, hence the name! We will examine what it means to make a “good” tool for learning, why it is hard, and how you can create and prototype them. The bulk of this class centers around three learning mechanics: feedback and active learning, collaboration between learners, and data-driven improvement. Using these mechanics as specific case studies, the class will teach students how to think about, build, and study tools for online learning both for formal, classroom education, and informal learning. 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, and through doing so, learning how 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. As individuals and in small teams students 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 generating multiple solutions.; demonstrate habits that foster the creative process, including drawing, divergent thinking, and creative experimentation; and identify and explore with interaction design materials.

Students receive significant guided feedback from faculty and mentors.
Students receive significant guided feedback from faculty and mentors.

05-681 METALS Project I (12-unit spring course)
05-682 METALS Project II (48-unit summer course)

Experiential learning is key component of the METALS program. Through a substantial team project, students apply classroom knowledge in analysis and evaluation, implementation and design, and develop skills working in multidisciplinary teams. The project begins in the spring semester before graduation and continues full-time through the final summer semester; it must be taken in consecutive spring and summer semesters. The course number for spring is 05-681 and for summer 05-682.

Five Electives

You may use the five elective courses to design the program to your individual interests, background and goals. You must choose a minimum of three electives from at least two of the three distributional areas (Technology, Learning Sciences Theory & Instructional Design, Methods & Design).  It is your responsibility to ensure that you fulfill the distributional requirements. See the table below for the approved electives for each distributional area. Cross-listed electives count only in one distributional area. Independent studies typically do not fulfill distributional requirements unless pre-approved by the program director.

Each elective course must be the equivalent of a full-semester (9 or 12 units) course; two mini (half-semester) courses (6 units each) count as one elective. Elective courses must be different from any that you may have taken as part of the METALS core, and they cannot have counted toward a degree previously awarded by CMU.

Electives other than those listed below must be individually approved by the program director on a case-by-case basis for each student to realize their program goals and future endeavors.


  • Accessibility (05-899 B S22 & S21)
  • AI Engineering (11-695) or Machine Learning in Production (17-645)
  • Advanced Natural Language Processing (11-711)
  • Applied Data Science (16-791)
  • Applied Machine Learning (05-834)
  • Cloud Computing (15-619)
  • Data Science for Product Managers (05-898) *
  • Data Visualization (05-619)
  • Design Center: Design for Digital Systems (51-828)
  • Design Educational Games (05-818)
  • 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)
  • 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)**
  • Prototyping Algorithmic Experiences (05-685)
  • Python for Data Science (11-603)
  • Role of Technology in Learning in the 21st Century (05-838)
  • Software Structures for User Interfaces (05-631)
  • Web Application Development (17-637)

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)
  • Persuasive Design (05-615)
  • Role of Technology in Learning in the 21st Century (05-838)

Methods & Design

  • Advanced Interaction Design (05-661)
  • Agile Methods (95-874)*
  • Applied Research Methods (05-816)
  • 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)
  • Digital Ethnography (49-717)*
  • Experimental Design for Behavioral and Social Sciences (36-749)
  • 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) / Computer Science Perspectives In HCI (05-773)
  • Transformational Game Design Studio (05-899)
  • User Centered Research & Evaluation (UCRE) (05-610)**

General Electives

Any two additional courses listed above or choose no more than two of:

  • 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)
  • Digital Service Innovation (05-670)
  • Evidence-Based Management (94-814)*
  • Fairness, Accountability, Transparency, and Ethics in Sociotechnical Systems (05899 A F22)
  • Independent Study (05-680)
  • Language and Statistics (11-761)
  • Machine Learning with Graphs (11-741)
  • Product Management Essentials (17-692)*
  • Quality Assurance (17-623)*
  • Social Web (05-820)
  • Topics in Second Language Acquisition (82-888)
  • Topics on Ethics for AI (80-836)
  • Other possibilities if approved by METALS Director. To request approval, click here.

*mini course – counts as half of one elective

**Available in the Spring semester for METALS students. In the Fall semester PUI and UCRE are both reserved for MHCI students. Others may take it if and only if space is available and only with permission of the instructor. Non-MHCI students who register for this course in the Fall without the instructor’s approval will be removed without warning.

Two Required Prerequisite Courses

Carnegie Mellon’s METALS is a rigorous interdisciplinary program. Every student arrives here with his or her own set of talents and skills. However, two courses or the equivalent, demonstrable knowledge are required for entry into the program. If you do not have this knowledge, we offer free courses that you can take prior to matriculating in August. However, if required for you, you must successfully complete these courses in order to matriculate in the Fall semester.

  • Knowledge of Programming
    Proficiency in a programming language such as Python, JavaScript, C, programming methodology and style, problem analysis, program structure, algorithm analysis, data abstraction, and dynamic data. Normally met through an introductory course in programming in C, C++, Pascal or JAVA, that requires the student to write programs of about 300-lines of code from scratch. Equivalent course at CMU is 15-100 Introductory/Intermediate Programming.
  • Knowledge of Statistics
    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 the regression analysis and the contingency table families. Equivalent courses at CMU are 36-220 Engineering Statistics and Quality Control and 36-202 Statistical Methods & Data Science.

Sample Plans of Study

Full-Time Study

3 Semesters: The METALS degree is designed to be earned in three semesters over the course of one year from August to August by those students who have significant previous employment experience. A sample full-time schedule is below:

Fall Spring Summer
05-823 E-Learning Design Principles
05-738 Evidence-Based Educational Design
05-660 Interaction Design Fundamentals
Elective 1
Elective 2
05-681 METALS Project I
05-840 Tools for Online Learning
Elective 3
Elective 4
Elective 5
05-682 METALS Project II

Full-time Study

4 Semesters: The METALS degree may also be earned in four semesters by those seeking a less intense program experience or who have significant previous employment experience. The following is a sample full-time plan of study that keeps in mind required course sequences.

Fall Spring Summer
05-823 E-Learning Design Principles
05-738 Evidence-Based Educational Design
05-660 Interaction Design Fundamentals
05-681 METALS Project I
05-840 Tools for Online Learning
Elective 1
Elective 2
05-682 METALS Project II
Second Fall* Second Spring Second Summer
Elective 3
Elective 4
Elective 5

*International students must complete a minimum of 36 units per semester and 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.

Part-Time Study

Students have the option to complete the program on a part-time basis. Due to the F-1 visa requirement that students be enrolled full time, this option is only open to U.S. citizens and permanent residents. By exercising this option, you will be able to tailor completion of the coursework to suit your needs. You will work with an advisor to set up an appropriate plan of study. Ideally students should be able to complete the degree within a period of two years by taking two courses per semester, including summers. During the summer METALS Project II course, students are expected to be enrolled as full-time students and should make the appropriate arrangements with their employers for leave. Part-time students must also be aware that all HCI core courses are held during the day, so it is not possible to complete the degree as a night student. Also 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
05-681 METALS Project I
05-682 METALS Project II

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