Welcome to TALL!
Teaching the hard stuff
Learn more about our lab


Meet the people in our lab.

Picture of Jim Stigler

Jim Stigler

Jim Stigler is professor of psychology at UCLA. His research interests include teaching and learning in complex domains, the impacts of culture and technology on learning, and applications of improvement science to education.

Picture of Karen Givvin

Karen Givvin

Karen Givvin is a researcher and adjunct professor of psychology at UCLA. Her research interests center on teaching, learning, and motivation in achievement domains.

Picture of Ji Son

Ji Son

Ji Son is professor of psychology at Cal State LA. She is interested in how basic cognitive and perceptual processes foster rich and transferable learning.

Picture of Adam Blake

Adam Blake

Adam Blake is a Research Scientist at UCLA. He is interested in the factors that influence students' judgments, decisions, and strategies related to learning, and how instruction and learning materials can improve these behaviors.

Picture of Laura Fries

Laura Fries

Laura Fries is a graduate student in Developmental Psychology at UCLA. She is interested in the cognitive and affective factors that influence learning, and instructional strategies to promote transferable understanding and retention of complex domains.

Picture of Mary Tucker

Mary Tucker

Mary Tucker is a graduate student in Developmental Psychology at UCLA. Her research interests include the psychological and contextual factors that influence teaching and learning, the design of learning environments in STEM domains, and the application of technology and data analytics to understand and improve teaching and learning.

Picture of Caylor Davis

Caylor Davis

Caylor Davis is a researcher and curriculum developer for high school and college statistics courses. She is interested in research and course design that makes math more accessible for non-STEM students.

Picture of Icy (Yunyi) Zhang

Icy (Yunyi) Zhang

Icy Zhang is a graduate student in Developmental Psychology at UCLA. She is interested in the role of gesture in supporting students’ understanding of difficult content such as statistics, the application of technology in education, and collaborative learning.

Picture of Claudia Sutter

Claudia Sutter

Claudia C. Sutter is a postdoctoral scholar at UCLA and visiting scholar at UVA. She is interested in understanding (a) what drives students to learn and engage within the educational setting, (b) how students’ motivation (e.g., their beliefs and values) affects their learning behaviors and outcomes, and (c) how educational environments (instruction, learning materials) shape students’ opportunities to reach their full potential.

Picture of Ben Winjum

Ben Winjum

Ben Winjum is a staff member in the High Performance Computing group of UCLA’s Institute for Digital Research and Education, where he works on advanced computing and data analytics projects that support both education and research.


Picture of Stacy Shaw

Stacy Shaw

Stacy Shaw graduated from UCLA in 2020 and is now an Assistant Professor at Worcester Polytechnic Institute in the areas of Learning Science and Technology and Psychological and Cognitive Science. Her research focuses on learning in mathematics and statistics with a focus on flexible thinking and creativity.

Picture of Emma Geller

Emma Geller

Emma Geller is an assistant teaching professor at UC San Diego. She is interested in applications of cognitive psychology to support deep learning and understanding in multimedia environments, especially regarding the design of online video learning.


Dig into what we've been working on.

The ​Better Book​ Approach for Education Research and Development

James W. Stigler, Ji Y. Son, Karen B. Givvin, Adam Blake, Laura Fries, Stacy T. Shaw, & Mary C. Tucker (2019).

This paper describes a new approach for education research and development - the better book approach - and reports on our initial development and application of the approach in the context of introductory college-level statistics. Download a copy.

Modeling First: Applying Learning Science to the Teaching of Introductory Statistics

Ji Y. Son, Adam Blake, Laura Fries, James W. Stigler (2021).

In this article we describe our attempt to apply theories and findings from learning science to the design of a statistics course that aims to help students build a coherent and interconnected representation of the domain. The resulting practicing connections approach provides students with repeated opportunities to practice connections between core concepts (especially the concepts of statistical model, distribution, and randomness), key representations (R programming language and computational techniques such as simulation and bootstrapping), and real-world situations statisticians face as they explore variation, model variation, and evaluate and compare statistical models. We provide a guided tour through our curriculum implemented in an interactive online textbook (CourseKata.org) and then provide some evidence that students who complete the course are able to transfer what they have learned to the learning of new statistical techniques. Download a copy.

Instructed Hand Movements Affect Students’ Learning of an Abstract Concept from Video

Icy (Yunyi) Zhang, Karen B. Givvin, Jeffrey M. Sipple, Ji Y. Son, James W. Stigler (2021).

The two studies reported here investigate the impact of instructed hand movements on students’ subsequent understanding of a concept. Students were asked to watch an instructional video—focused on the concept of statistical model—three times. These two studies found that instructed hand movement—even when presented as an unrelated, secondary task—can affect students’ learning of a complex concept. This article was featured in Scientific American. Download a copy.

Practicing Connections: A Framework to Guide Instructional Design for Developing Understanding in Complex Domains

Laura Fries, Ji Y. Son, Karen B. Givvin, James W. Stigler (2020).

Research suggests that expert understanding is characterized by coherent mental representations featuring a high level of connectedness. This paper advances the idea that educators can facilitate this level of understanding in students through the practicing connections framework: a practical framework to guide instructional design for developing deep understanding and transferable knowledge in complex academic domains. Download a copy.

Removing Opportunities to Calculate Improves Students’ Performance on Subsequent Word Problems

Karen B. Givvin, Veronika Moroz, William Loftus, & James W. Stigler (2019).

This paper reports our investigation on whether removing opportunities to calculate could improve students’ subsequent ability to solve similar word problems Download a copy.

Exploring the Practicing-connections Hypothesis: Using Gesture to Support Coordination of Ideas in Understanding a Complex Statistical Concept

Ji Y. Son , Priscilla Ramos , Melissa DeWolf , William Loftus and James W. Stigler (2018).

This paper presented a framework and approach for studying how students come to understand complex concepts in rich domains. Specifically, it explores the role that a teacher’s gesture might play in supporting students’ coordination of two concepts central to understanding in the domain of statistics: mean and standard deviation. Download a copy.

Expertise and Expert Performance in Teaching

James W. Stigler and Kevin F. Miller (2018).

This chapter tries to take a broader approach to understanding the nature and development of expertise and expert performance in teaching. The paper also tries to integrate a number of ideas and findings from literatures as diverse as cross-cultural compar- isons of teaching, cognitive psychology, and systems improvement, among others. Download a copy.


James W. Stigler, James Hiebert, and Karen B. Givvin (2018).

The paper discusses the logic of the more traditional approach on which many current policies for improving teaching in the United States are based and then presents an alternative research approach, in which a different theory of improvement is assumed. Download a copy.

Online Learning as a Wind Tunnel for Improving Teaching

James W. Stigler, Karen B. Givvin (2017).

The chapter proposes an approach that combines the affordances of online learning with the methodologies of systems improvement. It discusses how online learning might be a wind tunnel for the study and improvement of teaching. Download a copy.

What Community College Developmental Mathematics Students Understand about Mathematics, Part2: The Interviews

Karen B. Givvin, James W. Stigler, and Belinda J. Thompson (2011).

Following the prior paper, this article presents findings from one-on-one interviews with a sample of community college developmental math students. These interviews were designed to further probe students’ mathematical thinking, both correct and incorrect. Download a copy.

What Community College Developmental Mathematics Students Understand about Mathematics

James W. Stigler, Karen B. Givvin, and Belinda J. Thompson (2010).

This paper investigates what community college students actually understand about the mathematics that underlie the topics they’ve been taught and seeks evidence that students used reasoning in answering mathematical questions. Download a copy.


Learn about our active projects.

The Better Book Project:

We are developing a new approach to education R&D in which researchers, designers / developers, and practitioners work together to build and continuously improve online instructional materials. We leverage theories and findings from the learning sciences, translational research, affordances of online learning, and the methodologies of improvement science. (Funded by Chan Zuckerberg Initiative DAF of Silicon Valley Foundation.)

- Some early thoughts on the Better Book approach are found in Stigler & Givvin, Online learning as a wind tunnel for improving teaching.

- An overview of the approach and our CourseKata web R&D platform: Stigler et al., The Better Book approach to education research and development.

We are building a web platform to support our Better Book R&D approach (CourseKata.org). The CourseKata platform takes incremental improvements in content (stored as markdown files); transcodes them into HTML pages; and renders the content via LTI in multiple learning management systems (starting with Canvas). It also supports the R&D process, including randomized experiments at the individual student level within classes. You can also know more about the project by listening to the podcast, reading this article or watching our interview video about our new approach of teaching statistics.

CourseKata Statistics for College:

Following a “learn by doing” strategy to developing the Better Book approach, our initial focus is on introductory college-level statistics. Our innovative statistics and data science textbook includes more than 1200 formative assessments and R coding exercises. The book and resulting data are designed to implement, test, and improve our practicing connections theory of transferable learning. (Funded by Chan Zuckerberg Initiative DAF of Silicon Valley Foundation.)

We are scaling CourseKata Statistics into all levels of the California higher education system: community college (starting with Pierce College); California State University (starting with the Los Angeles campus); and the University of California (starting with UCLA). (Funded by California Learning Lab.)

- Check out the current version of our book, Introduction to Statistics: A Modeling Approach, by Ji Son and Jim Stigler. (Note: Choose I am a new user when login.)

- Read about our practicing connections hypothesis. Fries, et al...

CourseKata Statistics and Data Science for High School:

In our newest project we are adapting our innovative statistics program for high schools. Our target audience, initially, are the “Students Previously Known as Non-STEM”: students currently taking no mathematics at all in the twelfth grade. We are aiming to build skills and understanding, while at the same time changing their self-perceptions of their own capacity for STEM-related majors and careers.( Funded by Chan Zuckerberg Initiative, and by the Schusterman Family Foundation.)

Our Amazing Funders

Who make what we do possible.

Chan Zuckerberg Initiative DAF

California Governor’s Office of Planning and Research

Schusterman Foundation

We’re deeply grateful for the support of the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (DRL-1229004), the California Governor’s Office of Planning and Research (contract OPR18115), and the Schusterman Foundation.

Contact Us

Please let us know if you have any questions by emailing us at: ucla.tall@gmail.com