Deep Dive Course

From Signals to Support: Detecting and Responding to Motivation

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Intermediate level

No prior experience required

Flexible schedule

2 weeks, 6 to 8 hours per week

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*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.

What you will learn

Identify motivational signals that can be observed in learning environments.
Interpret engagement and persistence patterns without overgeneralizing from limited data.
Design responses and supports that address motivational challenges constructively.
Evaluate the ethical and practical tradeoffs involved in motivation-sensitive interventions.

Course description

Learner motivation changes over time, and many online systems fail because they notice disengagement too late or respond in ways that are too generic. Detecting meaningful motivational signals and acting on them well is essential for sustaining learner progress.

In this course, you will learn how to identify, interpret, and respond to motivational signals in learning environments. You will examine what data can reveal about engagement, persistence, and struggle, and how to design supports that are timely, ethical, and useful for learners.

Syllabus

Module 1: Measuring and Interpreting Motivation

Analyze behavioral engagement data to identify meaningful motivation signals and distinguish them from surface-level behaviors.
Analyze cognitive engagement patterns to identify meaningful indicators of learner thinking and distinguish them from passive or superficial activity.
Interpret affective signals in learner behavior to assess emotional investment and distinguish them from neutral or ambiguous participation patterns.

Module 2: Responding to Motivation Dips with Empathy and Ethics

Identify and verify early warning signs of disengagement across behavioral, cognitive, and affective dimensions in user behavior data to confirm motivational risk.
Visualize and represent motivational dip patterns across behavioral, cognitive, and affective data to understand scope, recurrence, and group-level trends that inform decision-making.
Identify and differentiate common motivational trajectories using time-series and learner feedback data, verify whether dips are systemic or isolated, and anticipate when and where intervention is most effective.
Design and implement timely, culturally aware, and empathy-driven re-engagement strategies that restore learner autonomy, purpose, and motivation.
Apply ethical design principles to the collection, analysis, and intervention of motivational data, ensuring learner privacy, autonomy, and equity are protected.

Module 3: 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 a 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 while getting feedback from experts.

You will have an alternative option to take a final exam where you will answer 30 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 count the one in which you score higher toward the certificate.

Meet the instructor

Dr. Amy Ogan

Associate Professor of Learning Sciences
Carnegie Mellon University

Amy Ogan is the Director of the Learning Science for Innovators program and a Professor in Carnegie Mellon University’s Human-Computer Interaction Institute, with a courtesy appointment at CMU-Africa. Her research sits at the intersection of human-computer interaction, learning science, and educational technology, with a focus on designing learning experiences that are more engaging and effective. She has conducted field research on the deployment of educational technology across five continents. She has been named a Jacobs Foundation CRISP Fellow, World Economic Forum Young Scientist, and Rising Star in EECS by MIT. She has received the McCandless Chair, the Moran Professorship in Learning Science, the 2024 SIGCHI Societal Impact Award, and numerous best paper awards. Before Carnegie Mellon, she was a visiting researcher at USC’s Institute for Creative Technologies and the Pontificia Universidad Católica de Chile. Her research is supported by the Mastercard Foundation, National Science Foundation, Google, McDonnell Foundation, and Jacobs Foundation.

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