Cynthia D’Angelo, Ph.D, is a researcher specializing in science education, technology-enhanced learning environments (including simulations and games) and collaborative learning. She focuses her work on leveraging data gathered through online technologies to better understand student learning of STEM concepts and practices and help teachers improve their instruction. She was the PI of a $1.5 million NSF-funded project that worked on developing learning analytics for speech data of face-to-face collaboration of middle school students. She has also worked extensively with NGSS, both in implementation of curricular initiatives related to NGSS and in developing and studying NGSS-aligned formative assessments. In her free time, Cynthia enjoys photography, playing soccer, and hanging out with her dog. 

Rodney Hopson is excited to be joining the department and the QUERIES unit in the College of Education. He received his Ph.D. from the Curry School of Education, University of Virginia with major concentrations in educational evaluation, anthropology, and policy, and sociolinguistics.  Most recently, he served as professor of education policy and evaluation and associate dean for research in the College of Education and Human Development, George Mason University.  He is an avid futbol/soccer fan and proudly boasts jerseys from countries and teams from around the world.  His wife, Deborah is a cooking aficionado and entrepreneur, and they have a blended family of five young adult children:  Ayana, Hannibal, Aliyah, Habiba, and Ayia.

The broad objective of her research agenda is to develop quantitative methods for analyzing complex data in educational and psychological research, including non-normally distributed data, small samples, high-dimensional data, and categorical data. To advance this objective, her research centers around two areas: (1) structural equation modeling and (2) data mining. In the field of structural equation modeling, she studies test statistics, fit indices, and robust estimation methods with the aim to provide more valid inference for messy data in social and behavioral sciences. In data mining, she studies regularization methods (lasso, ridge, elastic net) in the contexts of clustering and classification, with application on genomic datasets for cancer diagnosis. Substantively, she is interested in developing software packages to facilitate the use of quantitative methods and applying them to developmental and health research.



Justin Kern is an alumnus of the University of Illinois, having finished his PhD in quantitative psychology in 2017. He is very excited to be returning to Illinois for this Fall semester after having spent a year at the University of California, Merced as a visiting assistant professor. His work can broadly be described as focusing on creating, improving, and expanding methods for the collection and analysis of social science data. In particular, he is interested in response time modeling, computer adaptive testing, measurement bias, and extensions of modern measurement models. Outside of his academic work, he maintains an interest in music, and can often be found dabbling with his instrument collection, which includes (among others) four guitars and five saxophones. Other non-academic interests include baseball, college football, craft beer, video games, random facts, and his cat, Koko.

Mike's research, which focuses on collaborative learning and knowledge communities, aims to understand how children develop STEM and computational literacies when engaged with technology-enhanced learning. More broadly, Mike's work focuses on how to design transformational learning environments that combine interactive physical spaces, digital information, and collaboration between learners to envision the future of learning both in and out of schools. Mike has developed several theories on how students collaborate and learn in open-ended and exploratory learning environments. During his time at MIT, Mike advocated for a new approach to computing education, computational action - which is founded on the idea that while learning about computing, young people should also have opportunities to create with computing which have direct impact on their lives and their communities. During Mike’s free time, he enjoys making the occasional piece of furniture (often poorly), playing pinball, and rock climbing.

Yan Xia earned his B.S. degree in Psychology from Peking University, China. He earned both his M.S. in Statistics and Ph.D. in Measurement and Statistics from Florida State University. His areas of specialization lie in advanced statistical analyses, with a specific focus on structural equation models. With research experience in the field of education, psychology, and family relationship, where data are commonly messy, he has developed his current research interests in the analytical methods that are designed for difficult real-world data, such as missing, nonnormal, categorical, and contaminated data. He is also devoted to applying quantitative methods to substantive studies and helping practitioners gain a better understanding of the inferences drawn from real-world data.