In the Change Lab, we study learning in context. Our work aims to identify mechanisms of human learning, the conceptual structure of human knowledge, and effective strategies when trying to build new knowledge. Our work is contextual, which means that we’re particularly interested in real-world learning materials, learning goals, learning situations, and learning influences, and we explore new ways to bring rigorous experimental research methods and analyses to natural learning environments. We think context matters, but importantly, we don’t reject basic cognitive principles by saying “it all depends” — instead we seek to identify generalizable mechanisms of human learning and behavior that have real-world utility. Below are some examples of our eclectic research interests.
How People Learn and Think about Abstract Concepts
Many learning goals in education involve abstract concepts and processes — things that are difficult to directly perceive, or that are expected to transfer across a wide range of scenarios. For example, statistical concepts are important to learn in school, but have no single direct representation in the physical world. The abstract nature of this knowledge presents challenges for teaching and learning in practice and raises theoretical questions about how we think about abstract concepts more generally.
- Motz, B., Fyfe, E., & Guba, T. (2022). Learning to call bullsh*t via induction: Categorization training improves critical thinking performance. Journal of Applied Research in Memory and Cognition. https://doi.org/10.1037/mac0000053
- Day, S., Motz, B., Goldstone, R. (2015). The cognitive costs of context: The effects of concreteness and immersiveness in instructional examples. Frontiers in Psychology, 6(1876). https://doi.org/10.3389/fpsyg.2015.01876
Large-Scale Tests of Theory in Education
Imagine that you run an experiment in a single class: Some students get one treatment, others get another, and you measure outcomes on the midterm exam. In this situation, no matter the findings, it can be unclear how to interpret the results: either the findings generalize broadly, or they only apply to the unique idiosyncrasies of this one class. For this reason, the Change Lab invests hard work in conducting theoretically motivated experiments and analyses that are distributed across many class implementations, so that our research is robust to variations, our findings generalize, and so that we can build a science of the context-dependencies in natural learning settings.
- Motz, B., Canning, E., Green, D., Mallon, M., & Quick, J. (2021). The influence of automated praise on behavior and performance. Technology, Mind, and Behavior, 2(3). https://doi.org/10.1037/tmb0000042
- Fyfe, E., de Leeuw, J. R., Carvalho, P. F., Goldstone, R., Sherman, J., [42 others] & Motz, B. (2021). ManyClasses 1: Assessing the generalizable effect of immediate versus delayed feedback across many college classes. Advances in Methods and Practices in Psychological Science, 4(3), 1-24. https://doi.org/10.1177/25152459211027575
What behaviors result in improved learning? — and why? And moreover, can we leverage these insights, scaffolding the learning process to improve outcomes or efficiency? In the Change Lab we examine and manipulate the cognitive strategies that people engage in when learning, both in real-world learning environments and using realistic learning materials in laboratory environments. In many cases, our work aims to bridge cognitive theory with the social context of learning.
- Eyink, J., Motz, B., Heltzel, G., & Liddell, T. (2020). Self-regulated studying behavior, and the social norms that influence it. Journal of Applied Social Psychology, 50(1), 10-21. https://doi.org/10.1111/jasp.12637
- Carvalho, P., Braithwaite, D., de Leeuw, J., Motz, B., & Goldstone, R. (2016). An in-vivo study of self-regulated study sequencing in Introductory Psychology courses. PLoS ONE 11(3): e0152115. https://doi.org/10.1371/journal.pone.0152115
Research Infrastructure and Data Analysis
An important propellant for progress is new research tools and methods, and we believe that the study of human learning is ripe for new ways of doing things. In the Change Lab we innovate these new methods, enabling new discoveries and lowering the barriers to rigor in the science of learning. In particular, Change Lab ❤️ Terracotta, a platform for enabling experimental research in learning management system course sites.
- Motz, B., Carvalho, P., de Leeuw, J., & Goldstone, R. (2018). Embedding experiments: Staking causal inference in authentic educational contexts. Journal of Learning Analytics, 5(2), 47-59. https://doi.org/10.18608/jla.2018.52.4
- Motz, B., Quick, J., Schroeder, N., Zook, J., & Gunkel, M. (2019). The validity and utility of activity logs as a measure of student engagement. In Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19). https://doi.org/10.1145/3303772.3303789
But That’s Not All
The categories above should be viewed as examples of our research interests, not as their limits. Our work is eclectic, and we opportunistically pursue research questions that are interesting and intriguing to us. To quote Linda Smith, we relish in becoming “fascinated by what we don’t know.”