Intermediate level
No prior experience required
Motivation, Metacognition, and Discourse Analytics
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 indicators of motivation, metacognition, and discourse in learner data.
- Engineer and interpret features that capture these constructs in educational settings.
- Apply discourse analysis and analytics methods to richer forms of learner interaction data.
- Use analytic findings to inform support strategies, product decisions, and research questions.
Course description
Important learning signals are often embedded in how learners reflect, regulate, and communicate, not just in whether they answer correctly. Analytics for motivation, metacognition, and discourse can help teams understand these richer processes and design better support around them.
In this course, you will learn methods for analyzing learner motivation, metacognition, and discourse using learning analytics, feature engineering, and discourse analysis tools. The course is designed to help you identify meaningful signals, interpret them responsibly, and connect them to instructional or product decisions.
Syllabus
Module 1: Metacognition and Motivation
- Apply the four learning analytics techniques for researching learner metacognition and motivation.
- Explain how feature engineering can be used to aid in metacognition and motivation research.
Module 2: Discourse Analysis
- Explain the function of discourse analysis.
- Apply tools and methods of discourse analysis.
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 will have a choice to analyze discourse data. That will provide you with a nice experience to apply the fundamentals you will learn in the modules to a larger, more authentic context. It will be self-graded and you will receive a sample solution.
You will also have the option to take a final exam with 20 questions. The exam can be taken multiple times, and each attempt draws new questions randomly from a pool of questions.
You may also complete both the course project and the final exam. The higher of the two scores will count toward the certificate.
Meet the instructor
Dr. John Stamper
John Stamper is a faculty member in the Human-Computer Interaction Institute at Carnegie Mellon University in Pittsburgh, Pennsylvania. He is the Director of the Master of Science in Learning Engineering (MSLE) degree program and the Technical Director of the DataShop.
Dr. Paulo Carvalho
Paulo Carvalho is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. His research explores how AI can revolutionize learning by creating engaging, practice-first environments. He uses data analytics and computational modeling to understand student learning, motivation, and interest and to develop precise models that inform better learning experiences. He is currently investigating how generative AI can empower these practice-focused approaches, boost engagement, and free teachers to provide personalized support. His research is funded by the National Science Foundation, IES, Schmidt Futures, the Walton Foundation, and Google.
Where to go next
From Signals to Support: Detecting and Responding to Motivation
Track, interpret, and respond to motivational signals so learners stay engaged, supported, and on track.
Sustaining Motivation Through Identity, Reflection, and Resilience
Design for lasting motivation by supporting identity, reflection, belonging, ownership, and resilience over time.
Learning Analytics Foundations: Predicting Student Success
Analyze educational data, build regression and classification models, and predict student outcomes to support better learning decisions.