Dr. Lee has been at NC State since 2000. Her research interests include teaching and learning of probability, statistics, and data science, especially incorporating technology use and designing technology environments that facilitate students’ learning. She situates her work in educational design in order to provide the best learning opportunities for students in K-12, her university students, and educators around the world that engage with her in online professional development.
Dr. Lee is a Distinguished Professor of Mathematics and Statistics Education in the STEM Education department at NC State University, and a Senior Faculty Fellow at the Friday Institute for Educational Innovation. Her research expertise focues on teaching and learning of probability, statistics, and data science in grades 4-14. She is an expert on the design and use of technology tools to facilitate students’ learning of mathematics and statistics, as well as preparing preservice and inservice teachers to use technology in mathematics (e.g., https://www.fi.ncsu.edu/projects/ptmt/). She currently has over 5 million in funding from IES and NSF to support her research, and is the director of the Hub for Innovation and Research in Statistics Education at the Friday Institute. She collaborates with a strong team of researchers and graduate students at the Friday Institute, as well as faculty, researchers, and technological designers at organizations such as RTI International, Research Matters Inc, University of Georgia, Eastern Michigan University, University of Southern Indiana, Middle Tennessee State University, UNC Charlotte, and Concord Consortium. Hollylynne Lee served as an RTI University Scholar in 2018-2019. Her current work includes developing and implementing online professional development and microcredentials to assist teachers in preparing to teach statistics and data science. See Amplifying Statistics and Data Science in Classrooms and Instepwithdata.org.
Dr. Lee was appointed to the planning committee for the National Academies convening on Foundations of Data Science for Students in K-12, held in Washington, DC September 13-14, 2022. At the convening she led a panel on issues in teacher education for K-12 data science.
In episode 2203 in the MathEd podcast series, Dr. Lee discusses her team’s research related to trends in classroom teaching in the context of AP Statistics, March 2022
Listen to a recent podcast on Teaching Math Teaching where Dr. Lee discusses her designs in teacher education and the importance of data science education, December 2021.
Watch a brief video as part of the Ask an Expert series in the College of Education where Hollylynne discusses how important it is for K-12 students to develop data literacy, September 2021.
- Recent Courses Taught Include:
- EMS 573 Design of Tools and Learning Environments in STEM Education
- EMS 480/580 Teaching and Learning Mathematics with Technology
- EMS/ST 519 Teaching and Learning Statistical Thinking
- EMS 770 Foundations in Mathematics Education Research
Recent Honors and Awards
- 2023 awarded the William D Warde Statistics Education award, from the Mu Sigma Rho statistics honorary society
- 2022 nominated and served on planning committee for the National Academies of Sciences workshop on Foundations of Data Science for K-12 Students.
- 2022 awarded the Robert Foster Cherry Award for Great Teaching from Baylor University
- 2021 named a finalist for the national-level Robert Foster Cherry Award for Great Teaching from Baylor University
- 2020 awarded UNC Board of Governor’s Award for Excellence in Teaching
- 2020 named a Fellow of the American Statistical Association
- 2018 named an RTI University Scholar
- 2014 NC State Alumnae Association Outstanding Teacher Award
- 2013 named an NC State University Scholar
- 2012 received National Technology Leadership Initiative Fellowship for Mathematics Education for best research paper at Association of Mathematics Teacher Education conference
Doctor of Philosophy Mathematics Education University of Virginia 2000
- Preparing secondary prospective mathematics teachers to teach with technology , Reflection on Past, Present and Future: Paving the Way for the Future of Mathematics Teacher Education (AMTE Professional Series) (2023)
- Validating a concept inventory for measuring students' probabilistic reasoning: The case of reasoning within the context of a raffle , JOURNAL OF MATHEMATICAL BEHAVIOR (2023)
- Visualizing qualitative data: unpacking the complexities and nuances of technology-supported learning processes , ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT (2023)
- A Look into the AP Statistics Classroom: Who Teaches It and What Aspects of Statistics Do They Emphasize? , CHANCE (2022)
- Attending to Students’ Reasoning About Probability Concepts for Building Statistical Literacy , Proceedings of the IASE 2021 Satellite Conference (2022)
- Digging into data: Illustrating a data investigation process , Statistics Teacher (2022)
- From public health to personal finance, statistical literacy is essential for careers and everyday life (Opinion) , K-12 Dive (2022)
- INVESTIGATING DATA LIKE A DATA SCIENTIST: KEY PRACTICES AND PROCESSES , STATISTICS EDUCATION RESEARCH JOURNAL (2022)
- Making data science practices explicit in data investigation process: a framework to guide reasoning about data , Proceedings of the IASE 2021 Satellite Conference (2022)
- Online tools to support mathematical modeling and community building , The Centroid (2022)
ESTEEM II is a collaborative research proposal for a 5-year Level 2 project in the Institutional and Community Transformation track. We aim to build a community that will transform undergraduate teacher preparation so that future K-12 mathematics teachers are prepared to effectively teach modern data science and statistics (DS&S). Modern society demands that citizens be statistically and data literate, resulting in growing efforts at the K-12 level to include more DS&S in the curriculum (e.g., Boaler & Levitt, 2019; Gould, 2017). Now is the time to transform teacher education accordingly so that new teachers are prepared to develop studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ statistical and data literacy. Currently, teacher education programs do little to ensure that K-12 prospective mathematics teachers are ready to teach students DS&S concepts and skills, reflected in their lack of content knowledge and confidence to teach statistics topics (Lovett & Lee, 2017; 2018). To prepare data literate students ready to pursue data-intensive STEM careers, undergraduate mathematics teacher education needs to transform so that it prepares teachers who can effectively teach DS&S in K-12 settings. The transformative efforts will focus on faculty, courses, and programs, specifically in secondary mathematics and elementary education programs in a broad range of institutions. We need a community of faculty, organizations, initiatives, and projects focused on transforming undergraduate teacher preparation in DS&S education.
PTMT-ESP is a proposed 5 year project that brings a cross-institutional team together to build from the successes of previous PTMT projects (DUE 0442319, 0817253, 1123001) to meet a critical need in mathematics teacher education. Specifically, the PTMT-ESP project proposes to utilize design based research to create, refine and study materials for prospective mathematics teachers (PSMTs) to examine secondary students authentic mathematical practices on technology-based algebraic tasks. We will study the implementation of these materials through examination of the understandings that prospective secondary mathematics teachers develop related to both the algebraic concepts and the ways that students engage with and make sense of these algebraic concepts in technological contexts. These freely available materials will build on the previous PTMT work that is available in a free online portal. PTMT-ESP will also facilitate continued growth and support of the large community of technology using mathematics teacher educators (TUMTEs) that began with that prior work and now exceeds 250 users. Subgoals and Outcomes Design and refine seven curriculum modules for undergraduate mathematics education students to examine secondary studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ mathematical practices when engaging with technology-based algebra tasks. Materials will be designed to align with the existing PTMT Algebra materials and will include authentic, and carefully selected, video cases of secondary studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ work on technology-based algebraic tasks. Conduct a research study to examine PTMTs development related to understandings of studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ technology-based algebraic practices and their own algebraic knowledge. We will use both qualitative and quantitative measures to examine the ways that PSMTs make sense of studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ mathematical practices, make pedagogical decisions based on their sensemaking, and deepen their own mathematical understandings in the process. Expand and support the TUMTE community through professional learning opportunities such as pre-conference workshops, newsletters, and the PTMT webportal. Through these outlets we plan to continue to support the existing PTMT TUMTE community while also expanding it to include at least 10 new (early career) faculty members in the workshops each year (20 total participating in workshops).
The InSTEP project is a late Design and Development level II project focused on Teaching statistics as essential aspect of developing STEM learning. To prepare K-16 students who are data literate and prepared to pursue careers that require data science and statistics skills, teachers need to effectively integrate meaningful data experiences into instruction. Even though all STEM disciplines are awash in data, todayÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s high school graduates are generally underprepared in statistics and data skills (Finzer, 2013), making a career pathway in a STEM discipline out of reach for many (Kwasny, 2015). This under preparedness exists in spite of efforts by organizations such as NCTM and ASA and adoption of the Common Core State Standards. Most statistical concepts in secondary education are taught within an already packed mathematics curriculum by teachers who are typically underprepared to teach statistics. The preparation of teachers to develop understanding of statistics has not met the demands needed for teachersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ development. Approaches such as in-person teacher workshops or traditional university coursework have been used to address teacher education needs in statistics. InSTEP, however, will harness the research-base supporting online professional learning to invigorate teachers in improving their practices in teaching statistics in grades 6-12. In particular we are focused on two questions in the DRK-12 program: 1) providing a solution for in-service teachers to develop STEM content knowledge (statistics) and pedagogical content knowledge in ways that improve their instructional practice, and 2) develop, apply, and test a model of professional development through personalized online learning and micro-credentials and examine if it is effective. The proposed project strategically combines deliverables and results from three previous efforts (NSF and non-NSF funded) to solve the aforementioned problem with over a decade of development in resources for teacher education that are particularly suited for online delivery. Resource include videos of expert discussions in statistics education, videos of studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ work in classrooms, a framework for understanding how to support studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ statistical investigations, guides for developing meaningful statistics tasks, and 6 microcredentials where teachers can illustrate their understandings and ability to implement new strategies in their practice. These resources, combined with the professional learning platform RTI AMAZE (developed by RTI International), and the collective expertise of our team, situates us to effectively: 1) curate and create online resources and tools for professional development for teaching statistics in grades 6-12, 2) design models for using these resources as personalized learning in a professional online community, and 3) examine effectiveness of teachersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ engagement with models and resources in their personalized professional education pathways.
The proposed STEM+C project brings together educators in science, statistics, computing, and literacy to address a critical needÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Âand important opportunityÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Âin science education. In line with NSFÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s calls to ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œharness the data revolutionÃƒÂ¢Ã¢â€šÂ¬Ã‚Â (NSF, 2018), we see great potential for complex, public datasets to provide students new access and insights to some of the most critical socio-scientific issues of our time. But still little is known about how students come to see public datasets as resources to investigate, argue about, and explain our world; or, how to design tools that actually enable students to manage and manipulate such data. Using complex datasets and visualizations can also introduce new challenges and entry points for students who are English learners (EL) or otherwise struggle with academic literacy. This project will contribute research, development, and professional learning efforts to integrate computational data analysis in middle grades science through science data stories activities. In such activities, students investigate datasets and interactive visualizations with a computational data analysis platform that encourages reflective data manipulation, as part of community-of-learners based argumentation and explanation activities, with a special focus on academic literacy support.
The Diagnostic Inventories of Cognition in Education (DICE) project aims to address develop a freely-available, web-based assessment system that efficiently provides teachers with timely, accurate, and actionable feedback about student cognition in probabilistic reasoning. This collaborative research project brings together an interdisciplinary team of researchers from the University of Georgia, Research Matters, and North Carolina State University. With our interdisciplinary expertise, we seek to develop a formative assessment system to support a more global shift in assessment practice where cognition and assessment are better aligned ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Âa shift away from ubiquitous general ability tests and towards assessment systems that profile multifaceted reasoning with the ultimate goal of aiding teachers in implementing an effective formative assessment process. Our goal is to develop a comprehensive formative assessment system that focuses on probabilistic reasoning, a critical middle gradesÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ mathematics concept foundational for developing descriptive and inferential statistical reasoning. At the core of the DICE system, we will design a diagnostic concept inventory to specifically identify examines who exhibit robust misconceptions when reasoning. Key to the psychometric underpinnings of a diagnostic concept inventory, each misconception will be operationalized as a binary latent construct, a conception that a student does or does not consistently exhibit on the inventory items for a single testing occasion. To reliably estimate whether the misconception is exhibited for each examinee, we will leverage a new class of multidimensional psychometric models, diagnostic classification models, to capitalize on information that is typically disregarded in assessments: incorrect answers. The focus of this project is to develop the concept inventory and to provide validity evidence to support the primary claim of the inventory, namely, that the inventory can accurately identify which misconception(s) a student has. To support the interpretation and appropriate use of the inventory diagnoses, we will design feedback reports accompanied by interpretive guides as integral components for the intended formative use of the DICE system.
There currently exists a multifaceted problem in STEM learning that the ESTEEM project aims to solve: a) preservice mathematics teachers (undergraduates) are underprepared to teach statistical content demands in middle and high school, thus impacting the quality of statistics education in grades 6Ãƒâ€šÃ‚Â12; b) technologies to engage in rich data analysis and statistics investigations is difficult to use and not freely available; and c) teacher preparation programs, and often faculty, do not place enough emphasis on teaching statistics. Many institutions have program constraints on where and how to address the issue of preparing preservice secondary mathematics teachers (PSMTs) to teach statistics. Secondary PSMTs need to be prepared to teach many advanced content areas, and faculty may not have access to high quality materials for preparing to future teachers to understand deeply the content and pedagogy they need to teach statistics in their future classrooms. Three efforts have made strides in developing and making high quality resources available. Our intent is to combine these efforts in a strategic way to provide access and opportunities on a larger scale. As part of the PTMT project (0442319, 0817253, 1123001), a module was developed that included about 6 weeks of materials focusing on key content that secondary mathematics teachers need to know how to teach and that technology is particularly wellÃƒâ€šÃ‚Âsuited to support. A new online portal, released in December 2015 (with a supplement #) gives free access to these materials (PDFs, videos, and technology files). The CODAP project has been funded through efforts on several projects (1316728, 1435470, 153057) and has a rich online open education tool that can be used and expanded on by others. This tool builds on the past work of Fathom and TinkerPlots to bring an open and dynamic platform to many more users on the web. In the past 18 months, with funding from the William and Flora Hewlett Foundation, a Massive Open Online Course for Educators (MOOCÃƒâ€šÃ‚ÂEd) focused on teaching Statistics Through Data Investigations was developed and offered to over 1600 professional educators from around the world, including about 40 preservice teachers. The resources in this MOOCÃƒâ€šÃ‚ÂEd are all open educational resources and can be reÃƒâ€šÃ‚Âused by others in other contexts. Many have already begun using these resources, and indeed the entire course, embedded in local professional development, as well as courses for preservice teachers at universities. For this project, we aim to push the discipline of mathematics teacher education, and indeed the field of statistics education, forward into utilizing free online resources (and/or entire modules) within traditional settings in university teacher preparation programs. Thus, our goals and corresponding actions are: 1. Create online resources for infusing secondary mathematics teacher preparation programs with content focused on teaching statistics a. expand CODAP (Common online data analysis platform) to be able to functionally support statistical content in current PTMT DAP materials, and enhance materials in the existing PTMT portal to have these interactive technology experiences embedded (rather than relying on downloading files and using desktop software). b. create additional materials on statistical content we know is difficult to teach and that PSMTs donÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢t know a lot about (e.g, simulations approach to inference and twoÃƒâ€šÃ‚Âway tables for categorical analysis), c. create videos of classrooms in which teachers and students are engaged with tasks used in these materials, d. create materials, including expert videos with statistics education faculty and teachers, about critical issues in teaching statistics (e.g., statistics task development guides). This is building off the success of the use of expert panels in the MOOCÃƒâ€šÃ‚ÂEd. 2. Design modules and approaches to be used within teacher preparation programs a. package online resources into different modules and an open resource repository (PTMT portal) b. disseminate materials to institutions with secondary mathematics teacher preparation programs c. host professional learning seminars to assist faculty in becoming familiar with resources and making strategic plans for how to integrate these in their undergraduate curriculum 3. Implement resources and modules in undergraduate courses and investigate studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ learning and faculty implementation a. examine different ways resources and modules are integrated into undergraduate curriculum for middle and secondary programs in mathematics education. b. examine how well prepared PSMTs are to teach statistics content for secondary students (perhaps use adapted SETS self efficacy to teach stats instrument and the LOCUS instruments as well other survey measures and embedded investigation in online resources) c. explore how to develop sustainable models for institutions using online resources and modules Intellectual merit:ESTEEM will develop open education resources that advance teachersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ understanding of statistical content and ability to engage in statistical investigations using free dynamic online tools, and facilitate wideÃƒâ€šÃ‚Âscale access and use of these materials. The project will also answer important research questions about a) how the design of resources and eÃƒâ€šÃ‚Âmodules and different approaches to infusing these in undergraduate courses impacts studentsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ learning in statistics education and b) what barriers and supports exist for faculty and institutions to approach course and curriculum issues related to using open education resources and modules. Broader Impacts: We will prepare teachers in a high demand area of secondary mathematics curriculum that can assist them in better preparing their future secondary students. This can all lead to a stronger preparation of students in statistics and data literacy and feed the pipeline into dataÃƒâ€šÃ‚Âcentric STEM disciplines.
- Hollylynne Lee and Gemma Mojica Aim to Transform Teacher Preparation for Data Science and Statistics Education through $2.5 Million Grant
- NC State College of Education Distinguished Professor Hollylynne S. Lee Receives Cherry Award for Great Teaching
- Lance Fusarelli and Hollylynne Lee Named Professors of Distinction
- Why is it Important for K-12 Students to Understand Data and Statistics?