Research lab: Keisha Varma
Focus
Understanding the cognitive, developmental and neuroscience underpinnings of STEM thinking and reasoning, particularly science
Projects
Scientific Reasoning and Science Learning
My work on scientific reasoning and science learning is classroom-based research that explores ways to enhance middle school students' science learning by supporting their scientific reasoning skills.
Games and Learning Sciences
My current work in this area explores how to use digital and board games to support science learning by providing opportunities for middle school students to engage in scientific practices and practice critical thinking skills related to science learning. Current studies are exploring the relationship between executive function, hypothetico-deductive reasoning exhibited in board games and scientific reasoning.
Read the work being done by one of my students, Nicolaas VanMeerten.
ESPRIT- Fostering Equitable Science through PaRent Involvement and Technology
This NSF-funded project looks at ways to use a social media learning environment to help mediate the achievement gap by increasing minority and immigrant parent involvement in middle school science education.
GopherMath Parent Engagement Program
GopherMath is a collaboration between Generation Next, Minneapolis Public Schools, and faculty from the Department of Educational Psychology, the Institute for Child Development, and the Department of Curriculum and Instruction, and is led by Kathleen Kramer. For GopherMath, I lead a team designing a new program to help parents from the Somali community better understand how to encourage productive mindsets in learning mathematics. The program helps parents understand why fraction instruction is taught using concrete models and other representations and their role in supporting their children’s education in general.
Keisha Varma
- Lab director
- Associate professor of educational psychology in the psychological foundations program
- Emphasis: Learning and cognition/educational technologies
Research group
Jeremy Wang PhD graduate student, psychological foundations: learning and cognition
Jeremy's research interests surround application of cognitive science to science education, with an emphasis in scientific reasoning and learning of complex science concepts and skills. His dissertation focuses on how implicit learning approaches can be applied to understand cognitive processes in conceptual change.
Jean Baptiste PhD graduate student, psychological foundations: learning and cognition
Jean Baptiste is interested in the different roles of cognitive modes (convergent and divergent thinking) and executive functions during scientific reasoning. What role does the construct of cognitive flexibility, and more particularly contextual focus, play during scientific reasoning? How does it manifest during each phase of the scientific reasoning process (i.e. problem space search, hypothesis space search, experiment space search, final solution)?
Nicolaas VanMeerten PhD graduate student, psychological foundations: learning and cognition/educational technologies
Nicolaas' research is primarily to study learning in digital game environments. Specifically, he is interested in how people learn in complex multiplayer environments, where learning predominantly occurs as a result of social interactions. To perform this research, Nicolaas uses logged player behavior that is automatically collected while people are playing digital games to track changes in behavior over time.
Tayler Loiselle PhD graduate student, psychological foundations: learning and cognition/educational technologies
Tayler’s research looks at how middle school students cognitively engage within an online social learning environment. More specifically, what kinds of science-related teacher prompts elicit higher modes of student cognitive engagement? She is using a modified coding scheme that helps identify interactive, constructive, active, and passive engagement behaviors within technology-enhanced environments. Additionally, Tayler is a graduate assistant on the ESPRIT project.
Alyssa Worley Undergraduate student
Drake Bauer Undergraduate student, Life Sciences and Psychology
Drake is interested in how psychology and neuroscience affect educational achievement. By understanding these relationships, he hopes to improve science education in order teach all academic capabilities effectively. He is currently working in Dr. Varma's lab, narrowing down pivotal executive functions for scientific thinking as well as understanding how game-playing can improve these executive functions.
Jonathan Shoberg Undergraduate student, College of Science and Engineering
Jonathan is focused on video games and is interested to see how they can be used as a tool of research, as they provide complex scenarios that nonetheless can be fully understood by researchers. Currently, he works with graduate student, Nicolaas VanMeerten, on the Battle School Study research project regarding use of video games. Jonathan and Nic utilize the game, League of Legends, to see what effects different forms of communication have on the performance of players of the game.
Anna RaffertyVisiting scholar
Anna Rafferty is an Assistant Professor of Computer Science at Carleton College, and her work combines ideas from computer science, education, and cognitive science. Her research focuses on applying and developing machine learning and artificial intelligence techniques to improve educational technologies and better understand human learning. One current project focuses on developing algorithms to automatically assess learners’ misunderstandings from their actions and using these assessments to provide personalized feedback. She has applied the core technologies in this project to several domains, including game-based assessments for experiments about concept learning and interpreting learners’ algebra solving strategies. Other recent projects include exploring how reinforcement learning algorithms can be used for experimentation within online courses and materials in a way that meets the goals of both teachers and researchers, and examining how middle school students use and interpret interactive models about science content. Other general areas of interest include automated scoring and feedback for students, especially about strategies and non-written work; individualizing instruction in educational technologies; and how to draw on the strengths of both human teachers and machine learning to most effectively help students learn.