We are the CLOUDS Lab (Computational Learning Organization Using Digital Software) ☁️

Learning is not a single point of data. It is an organized, interconnected, and dynamic system made up of cognitive, metacognitive, affective, and motivational components, all working together. Much like the atmosphere, learning emerges through the constant interaction between the external environment and internal processes.

Clouds form the same way. Water vapor cools and gathers around tiny particles in the air, taking visible shape only when temperature, humidity, and air movement line up. Learning forms when thinking (cognition) takes shape around moments of self-monitoring and reflection (metacognition), shaped by emotion (affect) and fueled by motivation. Change one part, and the whole system shifts — which is why we study learning as a whole, not in isolated pieces.

At the CLOUDS Lab, we aim to model this complex architecture. We are an interdisciplinary group dedicated to advancing the science of learning by leveraging educational technology, advanced computational methods, and mixed multimodal data.

Our mission is to comprehensively explore the extent to which educational technologies can effectively support the complex organization of teaching and learning. We build and test adaptive, personalized, and context-aware environments designed to support the entire human, including their learning needs, well-being, and potential as independent, lifelong learners.

Our Research

Our work investigates the fundamental question: How can we build learning ecosystems that empower human development and support data-informed educators?

Pillar I: Modeling the (Whole) Learner

We develop computational models that move beyond simple “knowledge-tacking.” Our models integrate the cognitive (what a learner knows), the metacognitive (how they plan and reflect), the affective (how they feel), and the motivational (why they persist) in relation to contextual demands.

Pillar II: Adaptive & Context-Aware Environments

Learning does not happen in a vacuum. We design and study environments that perceive and adapt to “contextual demands”—from the cognitive load of a task to the socio-emotional climate of a classroom. Our goal is to support learners’ self-regulation in real-time.

Pillar III: Data-Informed Pedagogy & Well-Being

We are committed to empowering educators as data-informed professionals. We build and evaluate tools that make learning data transparent, actionable, and ethical, with a core focus on the well-being and agency of both learners and educators.

Our Lab Ethos

We believe that a “Team First” mentality and a supportive, rigorous environment are non-negotiable prerequisites for high-impact science. Our culture is our most important system, and we co-revise it as a team.

1. We Practice Civility: We are respectful of each other’s time, working styles (remote/in-person), and perspectives. We are mindful and present in our interactions.
2. We Celebrate Intellectual Honesty: Mistakes are a natural and celebrated part of the scientific and human experience. Hiding mistakes compromises data, damages collaboration, and is intellectually dishonest. We own our mistakes so we can learn from them.
3. We Believe Success is Collective: The most successful labs are those where members are genuinely happy for each other’s success. We actively share knowledge, help with tasks, and celebrate all accomplishments, large and small.

Join the CLOUDS

We are actively seeking doctoral students and collaborators who are passionate about advancing the science of learning through computational methods. If our mission and ethos resonate with you, please email Dr. Cloude.

To ensure your email is read, please:

• Include your CV.
• Write a brief (1-2 paragraph) statement connecting your research interests to one of our lab’s three pillars.
• Briefly describe one of our lab’s recent publications and a novel question it sparked for you.

Bulletin Board 📌

• Recruiting undergraduate research assistants for Fall 2026 and Spring 2027! Apply Here
• Upcoming Paper Presentation: Automatically detecting Emotions during Game-based Learning using Large Language Models and Concurrent Verbal Protocols – Annual Meeting for the American Educational Research Association, April 2026
• Upcoming Publication: A Balance between Stability and Flexibility: Adaptive Self-regulated Learning Processes during Game-based Learning – Published in the British Journal of Educational Technology, In Press

Contact Us

Interested in collaborating?
📫 Email: cloudeel@msu.edu

This page is currently evolving — just like our research.