Designing Product Experiences that Motivate Learners
Start Any Time
Work on your pace and you will have instructors available to help you answer any questions.
Duration
Approximately 6 weeks, 3-4 hours/week
Fee
$750 Professional Rate
$300 Full-time Student Rate
*Proof of full time student enrollment required. Acceptable forms of id include a letter from your university’s registrar office or an unofficial transcript. Email your documents to learnlab-help@lists.andrew.cmu.edu
Certificate Course Description:
This course builds on core motivation theories and product design principles to help you support learners through every stage of their journey – from their first login to moments when motivation starts to dip. You’ll explore how to recognize when users are losing interest, how to re-engage them with thoughtful design choices, and how to sustain motivation through personalized support, well-calibrated challenges, and meaningful rewards.
Through research-backed frameworks, design examples, and practical activities, you’ll learn to craft onboarding experiences that spark early interest, implement rewards that motivate without backfiring, and personalize the learning path to help users stay committed. Whether you’re designing something new or refining an existing product, this course will help you make intentional, learner-centered choices that keep people coming back, because they’re motivated to grow, not just to finish.
Module 1: Start Strong – Motivating learners
- Analyze product features that help first-time learners recognize personal value in the learning experience
- Evaluate examples of early product experiences that support competence and build user confidence
- Recommend appropriate responses (e.g., offering choices, simplifying tasks, adding a human touch) to re-engage users who show signs of losing interest or confidence
Module 2: Using Rewards Thoughtfully
- Describe how different types of rewards influence intrinsic and extrinsic motivation in product design
- Identify examples of short-term reward strategies that encourage early participation and build behavioral momentum
- Recognize reward patterns that may reduce learner motivation or autonomy over time
- Explain how reward strategies can be adjusted to support persistence, enjoyment, and growth even without external perks
Module 3: Designing for Motivational Momentum – Balancing Challenge, Support, and Personalization
- Assess how adaptive feedback and difficulty adjustments can keep learners in the challenge zone
- Examine the role of personalized prompts in supporting persistence without disrupting focus
- Critique over-personalization approaches that reduce meaningful challenge or learner engagement
Module 4: Course Project or Final Exam
At the end of the course, you’ll have an opportunity to do a little project where you can choose to work on topic of your choice. That will provide you with a nice experience to apply the fundamentals you will learn in the modules to a larger, more authentic, context getting feedback from experts.
You will have an alternative option to take a final exam where you will answer 10 questions. The exam can be taken multiple times and each time new questions are randomly selected from a pool of questions.
You are also free to do both the course project and the final exam, we will consider the one in which you score more for counting towards the certificate.
No prerequisites required, finishing the prior course in series Designing for Motivation in EdTech will be helpful and is recommended.
Researchers, learning designers, UI/UX designers, instructional designers, and students who want to learn about how to motivate their learners. Anyone interested in edtech.
What you'll learn
This course will help you:
- Acquire hands-on skills in exploratory data analysis tailored to educational datasets
- Analyze specific predictive classifiers such as decision trees, random forests, Bayesian models, and logistic regression, evaluating their suitability, strengths, and limitations in educational contexts
- Apply knowledge gained throughout the course to real-world datasets
- Evaluate the performance of predictive models, considering ethical dimensions and accuracy
Course Instructors
Dr. Amy Ogan
is an Associate Professor of Learning Sciences at Carnegie Mellon University. Dr. Ogan has a PhD in Human-Computer Interaction supported by a fellowship from the Institute of Education Sciences. Her main area of research is focused on ways to make learning experiences more engaging, effective, and enjoyable. She is also the Director of Learning Sciences for Innovators, which helps companies in Africa refine and scale edtech products using evidence-based methods that support engaging and effective learning experiences…
Certificate
Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University’s Open Learning Initiative.
In addition to the knowledge and immediately applicable frameworks you will gain by attending your selected courses, you will benefit from:
- A digital, verified version of your Executive Certificate (Smart Certificate) you can add to your resume and LinkedIn
- Networking with a global group of your peers and instructors for advancing your career
Register Now
Register and start taking the course in three steps:
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Have questions? Our learning engineers are here to answer them at our monthly live AMA events! Join us at 4 PM EST on First Fridays, or 10 AM EST on Third Mondays. Registration required.