Designing for Motivation in EdTech
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 introduces you to the foundational principles of designing EdTech products that foster not just engagement but meaningful, sustained learner motivation. You will explore how motivation drives learning outcomes, how to distinguish surface-level engagement from deeper investment, and how product design choices can either support or undermine motivation.
Through real-world examples, research-backed frameworks, and applied activities, you’ll learn to design with motivation in mind. Using tools like Self-Determination Theory, Expectancy-Value Theory, and Bandura’s four sources of self-efficacy, you’ll practice evaluating and improving your product to foster learner curiosity, confidence, and persistence. By the end, you’ll be able to recognize motivational challenges in your product, apply practical design strategies to address them, and create learning experiences that help learners not just start but keep going.
Module 1: Beyond Clicks: Why Motivation Matters in EdTech
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Explain why motivation is essential for learner success and product outcomes in EdTech—and distinguish it from surface-level engagement
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Explain why motivation matters in educational product design
- Distinguish between motivation and engagement
- Begin evaluating learner behavior with a motivational lens
- Identify motivation-focused features in products
- Reflect on one’s product or context using a motivational lens
Module 2: Rewards, Purpose, and What Really Drives Learning
- Identify types of motivation
- Explain Self-Determination Theory as a framework
- Evaluate motivational design choices
- Explain the phases of interest development and how design can support them
- Reflect on the risk of over-reliance on rewards
Module 3: The “Why Bother?” Factor – Expectancy-Value Theory
- Diagnose how product features affect learner expectancy
- Identify design decisions that influence perceived value and cost
- Distinguish between low expectancy and low value in learner behavior
Module 4: The “Why Bother?” Factor – Expectancy-Value Theory
- Identify how skill level and confidence interact in shaping learner behavior
- Map product design to Bandura’s four sources of self-efficacy
- Recognize confidence-building design features
- Reflect on equity and access in learner experience
Module 5: 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.
Researchers, learning designers, instructional designers, students, and any stakeholders in education system who want to learn about how to motivate their learners.
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:
1. Enter your name and email address.
2. Create your account here to access our learning platform.
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.