My interest in education research started when I was an undergrad at UCLA. During my final year there, I was a research assistant in two labs: the Human Perception Lab and the Language and Cognitive Development Lab. At the Human Perception Lab, I focused on computer-assisted mathematics learning in middle school children with the use of perceptual learning. This perceptual learning module was developed as an alternative to traditional fraction learning and involved the use of repeated trials of multiple representations in mathematics. At the Language and Cognitive Development Lab I assisted a graduate student on her dissertation work on the use of spatial language and its affects in search patterns of children ages 2 to 4.
Upon entering UCI, I worked with Lindsey Richland and Michael Martinez in the area of mathematics and science cognition in students and how teachers can use principles from cognitive psychology as effective tools for instruction. Although I started my graduate career focused purely on cognitive processes through which students learn math and science, I’ve widened my views on what it means for students to be successful by also focusing on areas related to student motivation related to STEM (science, technology, engineering, and mathematics) domains. I worked with AnneMarie Conley and Jacquelynne Eccles on understanding the effect contexts have on student motivation.
Outside of UCI, I work with Stuart Karabenick at the University of Michigan and Erik Ruzek at the University of Virginia on issues related to student perceptions of the classroom. Part of this work involves understanding how students perceive the classroom climate and how to appropriately model student perceptions of the classroom. Much heterogeneity exists in students’ perceptions within the same classroom, and we are trying to understand whether heterogeneity is a meaningful aspect of the classroom. Together, we work with Arena Lam and use pattern-centered approaches and multi-level analysis to look at classrooms.
I also work with Daniel Hickey at the University of Indiana on a project looking at the principles for designing effective badge ecosystems for recognizing, assessing, motivating and studying learning. With Cathy Tran, we look at how digital badges can be used to adaptively increase learner motivation through understanding the context in which these badge systems have been designed. We use data from written artifacts and interviews to code the practices of projects as they design badge ecosystems.
Currently, I’m a postdoc at the School of Education and Information Studies at the University of California, Los Angeles. I work with Li Cai and others in evaluating the delivery of computer-adaptive tests as well as apply advanced quantitative methods to developmental questions.