Frequently Asked Questions

Learning Engineering is the systematic application of evidence-based principles and methods from educational technology and the learning sciences to create engaging and effective learning experiences, support the difficulties and challenges of learners as they learn, and come to better understand learners and learning. It emphasizes the use of a human-centered design approach in conjunction with analyses of rich data sets to iteratively develop and improve those designs to address specific learning needs, opportunities, and problems, often with the help of technology. Working with subject-matter and other experts, the Learning Engineer deftly combines knowledge, tools, and techniques from a variety of technical, pedagogical, empirical, and design-based disciplines to create effective and engaging learning experiences and environments and to evaluate the resulting outcomes. While doing so, the Learning Engineer strives to generate processes and theories that afford generalization of best practices, along with new tools and infrastructures that empower others to create their own learning designs based on those best practices.

Our courses are designed for people who create, study, or improve learning experiences. That includes instructional designers, learning engineers, learning scientists, educational researchers, edtech product teams, faculty, higher education staff, corporate L&D professionals, and students who want to build practical expertise in evidence-based learning design.

We recommend starting with Introduction to Learning Engineering. It gives you the clearest entry point into the field and introduces the KLI framework, which helps you match instructional principles to the kind of learning you want to support.

A strong next step is Evidence-Based Backward Design for Online Learning, where you deepen your understanding of KLI and learn how to align goals, assessments, and instruction in a more agile and iterative way.

To round out your foundation, take Active Learning: Practice and Feedback. This course helps you design effective instructional activities and understand when and why different forms of active learning work.

Yes. Most courses can be taken on their own, especially if you already know the area you want to strengthen.

That said, if you are new to the field, we recommend starting with Introduction to Learning Engineering before branching into more specialized topics. Some advanced courses are easier to benefit from once you already have a foundation in learning design or learning analytics.

Core Courses build strong foundations across the essential concepts, methods, and applications of learning engineering.

Short Courses focus on one specific skill or design challenge, so you can upskill quickly in a targeted area.

Deep Dive Courses go further into specialized topics, methods, or tools for learners who want more depth in a focused area.

Start with Uncovering Implicit Knowledge with Cognitive Task Analysis.

One of the biggest challenges in designing effective educational experiences is the expert blind spot. Experts often leave out the intermediate thinking and decision steps that novices need. In this course, you will learn cognitive task analysis methods such as structured interview, contextual inquiry, think aloud, and difficulty factors assessment to uncover that implicit knowledge and use it to design better learning experiences.

A strong companion course is Evidence-Based Backward Design for Online Learning, which helps you turn that insight into aligned goals, assessments, and instruction.

We offer several courses for learners who want to use data to improve educational experiences.

A good starting point is Learning Analytics Foundations: Predicting Student Success, where you will learn how to analyze educational data and use core modeling approaches to understand learner outcomes.

From there, you can go deeper depending on your goal:

• Predictive Modeling and Knowledge Tracing for Adaptive Learning
• Learning Curves and Data Mining for Course Improvement
• A/B Testing for Learning Design
• Motivation, Metacognition, and Discourse Analytics
• From Signals to Support: Detecting and Responding to Motivation

Together, these courses help you move from descriptive analysis to prediction, experimentation, redesign, and learner support.

Take Adaptive Learning and Intelligent Tutoring Systems.

In this course, you will learn how advanced learning technologies adapt to learner differences across knowledge, problem-solving path, and affect. You will be introduced to proven personalization methods used in intelligent tutoring systems and other adaptive technologies, along with newer approaches based on metacognition, motivation, and affect.

If you want to go deeper into tutoring systems after that, consider:

• Build Intelligent Tutors with CTAT, No Programming Required
• Rule-Based Cognitive Modeling for Intelligent Tutors
• Predictive Modeling and Knowledge Tracing for Adaptive Learning

Take UX Design for Better Learning Experiences.

This course teaches key multimedia principles and helps you apply them to digital learning experiences such as videos, presentations, and fully online courses. It is especially useful if you want to design learning experiences that support learning, not just usability.

You may also want to pair it with:

• Universal Design for Inclusive Course Design
• Segmenting and Pretraining for Online Learning
• Active Learning: Practice and Feedback

A strong entry point is Using Generative AI to Develop Active Learning Experiences. This course focuses on practical ways to use generative AI to build more effective learning experiences.

If you are interested in adaptive systems and tutors, you can continue with:

• Adaptive Learning and Intelligent Tutoring Systems
• Build Intelligent Tutors with CTAT, No Programming Required
• Rule-Based Cognitive Modeling for Intelligent Tutors
• Predictive Modeling and Knowledge Tracing for Adaptive Learning

This path is especially useful for people building educational products, adaptive experiences, or AI-supported instruction.

That depends on what kind of motivation work you want to do.

Choose From Signals to Support: Detecting and Responding to Motivation if you want to identify motivational patterns and respond with timely, ethical support.

Choose Sustaining Motivation Through Identity, Reflection, and Resilience if you want to design for long-term engagement, belonging, resilience, and learner ownership.

Choose Motivation, Metacognition, and Discourse Analytics if you want a more analytics-focused and research-oriented approach to understanding how motivation and metacognition show up in learner data.

A simple way to choose is to start with the type of question you want to answer.

Choose Learning Analytics Foundations: Predicting Student Success if you want a broad introduction to using data to understand and predict learner outcomes.

Choose Predictive Modeling and Knowledge Tracing for Adaptive Learning if you want to model learner knowledge and support adaptive systems.

Choose Learning Curves and Data Mining for Course Improvement if your goal is redesign and improvement based on learner interaction patterns.

Choose A/B Testing for Learning Design if you want to compare design alternatives experimentally.

Choose Motivation, Metacognition, and Discourse Analytics if you want to analyze more complex learner processes using richer data.

If you are looking for a more intensive learning experience with sustained support, deeper practice, and a more comprehensive path into the field, our Master of Science in Learning Engineering program may be a better fit.

The MSLE program is a stronger option for learners who want more than individual certificate courses and are looking for a fuller graduate-level experience.

Learn more about the program here: https://msle.hcii.cmu.edu/

Not necessarily. Many learners begin with no formal background in learning engineering.

If you are new, start with Introduction to Learning Engineering and then choose follow-on courses based on your goals. Some analytics and intelligent tutoring courses are more advanced, but the catalog is designed so you can begin with foundations and move into deeper topics over time.

Our courses are online and designed for flexible professional learning. Depending on the course, you may encounter video instruction, applied exercises, practical design tasks, mini-projects, and other hands-on activities.

Each course page explains the course format, pacing, and what to expect so you can choose the option that best fits your goals and schedule.

Yes. Our certificate courses are designed to help you build practical expertise and earn a shareable certificate upon successful completion.

If you are comparing options, check each course page for details on what is included and what completion looks like.

No. While many examples come from education and edtech, the methods apply broadly anywhere people design, study, or improve learning experiences, including higher education, K-12, workplace learning, training, and nonprofit contexts.

Visit the course page for the course you want, review the details, and click the enrollment button. Pricing and enrollment information are provided on each course page, including any student pricing or special options when available.

If you want help choosing a course before you enroll, contact us and we can point you in the right direction.

Yes. We offer discounted pricing for eligible students and nonprofit learners.

Please email learnlab-help@lists.andrew.cmu.edu before enrolling if you would like to confirm eligibility or learn more about the reduced-price option for a specific course.

Yes. We offer discounts for bulk or group purchases.

If you are interested in enrolling a team, department, or organization, please email learnlab-help@lists.andrew.cmu.edu and we can help you identify the right courses and provide group pricing options.