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With broad training in both quantitative psychology and statistics and years of experience in educational measurement, my research interests span multiple disciplines, crossing theoretical advances with practical applications.I am primarily interested in methodological issues in psychometrics, with latent variable modeling, latent class analysis, and mixed-effects models being the main focus of my current quantitative research program.

Empirically, I am interested in the assessment, treatment, and etiology of pathological gambling and psychological disorders in general. I also have empirical interests large scale and formative educational testing. My recent work has been to integrate methodological research into avenues for harnessing the information in big data.

Currently, I have many projects ongoing in a broad set of research areas including:

  • Educational Measurement
  • Diagnostic Assessment/Cognitive Diagnosis
  • Estimation and Optimization
  • Sports Forecasting
  • Latent Variable Models
  • Bayesian Statistics

The pages in this portion of my website are meant to help better disseminate the work I have done.

Awards

  • 2017
    Robert L. Linn Memorial Lecture Award
    University of Colorado-Boulder and University of California-Los Angeles
  • 2015
    Outstanding Contribution to Research in Cognition and Assessment
    American Educational Research Association, Cognition and Assessment SIG
  • 2012
    Significant Contribution to Educational Measurement and Research Methodology
    American Educational Research Association, Division D

Current Research Funding

  • Usable Measures of Teacher Understanding

    National Science Foundation (DRL-1813760; Co-PI)

    https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813760&HistoricalAwards=false

    One of the great challenges related to teachers and their knowledge is measuring their learning in ways that are both formative and meaningful in relation to their likely impact on students. This challenge persists despite efforts to define the knowledge teachers should have and despite previous innovative efforts to create good measures. This project tackles the challenge by specifically aiming to measure the kinds of knowledge developed in professional development (PD) programs that has been shown to matter for teachers’ classroom practices and their students’ learning. The project aims to develop an assessment that identifies patterns in the teachers’ learning in a way that helps drive subsequent professional development. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.

    The overall goal of this project is to pursue a potentially transformative approach to the assessment of teacher proportional knowledge by developing a measure that is well aligned with the content and skills taught in various PD programs. This instrument will be based on a new approach that builds on emerging psychometric models. Specifically, diagnostic classification models (DCMs) will be utilized to diagnose teachers’ learning during a PD program as well as employed to identify the progression in teachers’ learning. Statistical topic models (STMs) will be used to look for patterns of understanding that emerge from open-ended responses and provide natural-language insight into teachers’ reasoning. A final version of the assessment will be constructed for a national sample based on the results from the predictive validity stage, and this version will be tested with teachers who participate in various types of PD programs targeting proportional reasoning. This project has broad implications for the creation of assessments and for teacher education. It will provide insights about whether there is a clear learning progression for teachers. While much work has been done with students’ learning progression, much less is known about how teachers learn. Another implication is that the STM approach allows machine scoring of natural language in a way that highlights strengths and weaknesses in reasoning rather than simply returning a score. For formative use, this is information that is more helpful as it highlights areas for further instruction. A third implication is that DCMs will allow to assess teacher knowledge at a finer-grained understanding than is typically available, thus allowing for careful refinement of PD as well as a tool for showing overall growth in PD. A fourth implication is that a more systematic approach will be followed to capture the kinds of knowledge teachers need. Assessments developed using DCMs and STMs have the potential to serve as models for developing further instruments in other STEM content areas. Such assessments have the potential to not only help identify successful PD programs, but also to provide PD providers with rich data from which they can make instructional decisions.

    This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

  • Developing a Learning Map for Introductory Statistics

    National Science Foundation (DUE-1544481; Co-PI)

    https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544481&HistoricalAwards=false

    The overall goal of this project will be to create and validate a “learning map” (Stat-LM) for the content of undergraduate introductory statistics. This learning map will be a graphical representation of statistics concepts with connections among the concepts suggesting effective learning sequences. Use of the learning map will improve undergraduate learning by providing diagnostic information to instructors about students in their statistics courses, informing professional development for undergraduate statistics instructors, and modeling how critical prerequisites taught in high school connect to postsecondary learning expectations. In the first phase of the project, researchers will collaborate with statistics instructors from five institutions to create a learning map representing the broadest possible content set for introductory statistics material and the typical learning patterns of students in statistics courses taught in high school, community college, and university settings. In the second phase of the project, the research team will study the accuracy of the learning map using data collected from the Comprehensive Assessment of Outcomes in a First Statistics Course (CAOS), a confirmed valid and reliable assessment of student learning in statistics. This analysis will provide evidence of the learning map”s accuracy as a model of how students develop knowledge of introductory statistics.

    The project will build on research conducted at the University of Kansas in the development and application of learning maps as tools to support instruction and assessment by addressing two specific aims. First, the Stat-LM will be developed by expert statistics educators, educational psychologists, and statistics instructors from five varied types of institutions to model the broadest possible set of topics taught at the university level, thereby assuring its relevance for high school and undergraduate courses. Expert reviewers will provide feedback on the nodes and connections in the map in order to refine the content validity of the Stat-LM as a model of student learning. Second, the analysis using quantitative data collected from the CAOS will consist of an alignment study of the test items on the CAOS to the Stat-LM, identifying the nodes in the Stat-LM that students must have mastered to answer each item correctly. Along with data from the CAOS found to be aligned to the map, the map specification will be analyzed with diagnostic classification (also called Bayesian network methods; e.g., Rupp, Templin, & Henson, 2010). Based on results from these analyses, the researchers will evaluate the Stat-LM with respect to the ordered connections between nodes and the validity of nodes themselves as latent constructs. The Stat-LM will become a “living document”, subject to refinements throughout the proposed project and thereafter. As the goal of this project is to produce a tool for aiding statistics instruction, versions of the Stat-LM will be shared broadly for use within varied instructional settings.

See a list of my written work on Google Scholar

See my OrcID ID

See my latest talk slides and other software on GitHub