Tianying Feng

/by
I am Tianying (Teanna) Feng, a fourth-year cognitive science major and statistics minor at UCLA. My general interest is in learning how we may theorize about the mind quantitatively and computationally by synthesizing research and perspectives from multiple disciplines. I have been working as an undergraduate researcher at CRESST (the National Center for Research on Evaluation, Standards, and Student Testing) since fall 2017. CRESST is a research center on campus affiliated with the Graduate School of Education and Information Studies. I have contributed to two CRESST projects. One is with Public Broadcasting Service (PBS) KIDS, where CRESST is evaluating their instructional games that teach and assess how young children learn math and science concepts. My experiences with data collection and analyses of the data sparked my interest in developing useful measures of children’s knowledge using simulations because some children and their responses may be restrained by the linguistic demands of the assessment items, whereas their actions could shed light on their thinking. I also helped develop algorithms that aligned with past literature on children’s intuitive knowledge to model such actions, and results fell in line with the statistical analysis of children’s test scores. Furthermore, I have contributed to deliverables and conference proposals. I am a co-author on two technical reports to PBS KIDS and one to the CA state department of education.

I am Tianying (Teanna) Feng, a fourth-year cognitive science major and statistics minor at UCLA. My general interest is in learning how we may theorize about the mind quantitatively and computationally by synthesizing research and perspectives from multiple disciplines. I have been working as an undergraduate researcher at CRESST (the National Center for Research on Evaluation, Standards, and Student Testing) since fall 2017. CRESST is a research center on campus affiliated with the Graduate School of Education and Information Studies. I have contributed to two CRESST projects. One is with Public Broadcasting Service (PBS) KIDS, where CRESST is evaluating their instructional games that teach and assess how young children learn math and science concepts. My experiences with data collection and analyses of the data sparked my interest in developing useful measures of children’s knowledge using simulations because some children and their responses may be restrained by the linguistic demands of the assessment items, whereas their actions could shed light on their thinking. I also helped develop algorithms that aligned with past literature on children’s intuitive knowledge to model such actions, and results fell in line with the statistical analysis of children’s test scores. Furthermore, I have contributed to deliverables and conference proposals. I am a co-author on two technical reports to PBS KIDS and one to the CA state department of education.