Panel on non-academic research careers
Visitors from Pearson Research and Innovation Network will be talking about the kind of work they do and, more generally, about non-academic research career paths for PhDs.
Kathy McKnight, Ph.D.
Director, Center for Educator Learning & Effectiveness
Pearson Research and Innovation Network
Katherine McKnight leads the Center for Educator Learning & Effectiveness. She oversees the research agenda for the Center for Educator Learning & Effectiveness, designs and implements research studies, collaborates with a wide range of education organizations, and shares research results via publications, presentations and social media. With Pearson since 2006, she has directed research and evaluations, focusing on whole school reform and educator effectiveness. Dr. McKnight’s current projects focus on educator evaluation systems and career pathways; integration of technology for learning; and collaborative processes for enhancing instruction and learning opportunities for students. She also teaches statistics as an Adjunct Assistant Professor at George Mason University and has developed online statistics courses to train program evaluators. She holds a doctorate degree in Clinical Psychology with an emphasis on Quantitative Methods from the University of Arizona.
Jinnie Choi, Ph.D.
Associate Research Scientist, Center for Learning Science & Technology
Pearson Research and Innovation Network
Jinnie Choi’s work expands across learning science, data science, and computational/ quantitative social science research. Her doctoral training at University of California, Berkeley was focused on quantitative methods and evaluation. Her past and continuing research topics cover empirical validation of science learning progression, evaluation of public education policy, and statistical modeling of complex measurement situations (such as rater bundles, multidimensional item response modeling and multivariate generalizability). Her recent works include psychometric theories, methodologies, and algorithms for solving examinee classification problem and semi-automated item selection problems within a learning progression framework.
