About

Hollylynne S. Lee is a Distinguished University Professor of Mathematics and Statistics Education at NC State University, appointed on January 1, 2024. She is also a Senior Faculty Fellow at the Friday Institute for Educational Innovation and serves as Co-Director of the Hub for Innovation and Research in Statistics Education. Her research interests include the teaching and learning of probability, statistics, and data science, with a focus on incorporating technology and designing technology environments that facilitate student learning. She situates her work within educational design to provide optimal learning opportunities for K-12 students, university students, and educators worldwide through online professional development. Dr. Lee has managed over 7 million dollars in funding from IES and NSF to support her research, collaborating with a team of researchers and graduate students at the Friday Institute, as well as with faculty and organizations such as RTI International, Research Matters Inc, and several universities. Her expertise includes designing and implementing technology tools to enhance mathematics and statistics education, preparing preservice and inservice teachers to teach these subjects effectively, and developing online professional development programs. She has contributed to national and international efforts in mathematics and data science education, serving on committees for the National Academies and participating in global conferences. Her recent honors include the Judith E. Jacobs Lecture Award by the Association of Mathematics Teacher Educators and the William D Warde Statistics Education Award from Mu Sigma Rho.

Research topics

• Computer Science
• Mathematics education
• Psychology
• Artificial Intelligence
• Machine Learning
• Data science
• Pedagogy
• Sociology
• Mathematics
• Engineering
• Statistics
• Engineering ethics
• World Wide Web

Selected publications

• How to be “Choosy”: Wrangling big datasets for the classroom

  Teaching Statistics · 2025-11-17

  articleOpen access

  Abstract Educators are being encouraged to teach with “big” datasets that have more cases and attributes than are typically used in the classroom. When introduced carefully, these types of datasets can allow students to engage in complex and self‐directed reasoning, develop data management and inquiry skills, and experience data analysis in a way that is more authentic to professional practice. However, “big data” is also often unwieldy. It can overwhelm students, overload their software tools, and interfere with planful analysis. How can we make such datasets manageable? This paper presents pedagogical strategies and technical methods for educators, educational designers, and young investigators to reduce the number of cases or attributes in a large dataset in ways that do not unduly compromise their analyses. We illustrate these strategies with interactive demonstrations in two freely‐available open source tools: the Choosy plugin in CODAP, and a Python Jupyter notebook.

  PublisherOA PDFDOI

• How Teachers Envision Using Data Visualization Discussion Tasks in Classroom Instruction

  International Journal of Science and Mathematics Education · 2025-01-10 · 8 citations

  articleOpen access

  Abstract A growing number of teaching materials invite students to discuss the complex mathematical, contextual and social aspects of data visualizations. Orchestrating such discussions can be difficult, as this requires teachers to balance a variety of learning goals and student perspectives. This paper examines how teachers interact with data visualization discussion tasks—specifically, those that engage visualizations’ social complexities—as they consider using them in their own classrooms. Drawing from semi-structured clinical interviews with six U.S.-based teachers as they reviewed discussion tasks called Data Story Bytes, we explore: How did these teachers envision using these data visualization discussions in their classrooms? And, What mathematical, contextual, and/or social aspects of visualizations did teachers emphasize when engaging with the discussion task materials? We found that all teachers envisioned using data visualization discussions as lesson openers or routine activities, but they differed in their overall emphasis on the visualizations’ mathematical, contextual, or social aspects. Despite these differences, certain types of discussion prompts were associated with particular response patterns across all teachers, suggesting these task structures can help guide teachers to address a shared set of intended baseline goals for all three of these dimensions. Our findings represent a first step in understanding whether and how socially-oriented data discussion materials may be enacted in classrooms, and what additional design features and supports may be needed to help teachers do so productively.

  PublisherOA PDFDOI

• Designing online professional learning to support advances in teaching strategies in statistics and data science

  2025-01-01

  articleOpen access1st authorCorresponding

  This paper describes the design of an innovative online platform that has over 50 hours of learning experiences to support educators in further advancing their understandings and pedagogical skills in teaching statistics and data science to learners age 11-18+. Two frameworks are described that support effective classroom practices: a Data Investigation Process and Seven Dimensions of Teaching Statistics and Data Science. Examples of features and interface designs are included to illustrate how the frameworks work together to support teachers' learning in a cohesive way, even as they personalize their experience and engage in different learning pathways.

  PublisherOA PDFDOI

• Examining Teachers’ Professional Learning in an Online Asynchronous System: Personalized Supports for Growth and Engagement in Learning to Teach Statistics and Data Science

  Education Sciences · 2024-11-11 · 2 citations

  articleOpen access1st authorCorresponding

  Teachers’ professional learning often includes online components. This study examined how a case of 37 teachers utilized a specific online asynchronous professional learning platform designed to support teachers’ growth in learning to teach statistics and data science in secondary schools in the United States. The platform’s features and learning materials were designed based on effective online learning designs, supports for self-guided learning, and research on the teaching and learning of statistics and data science. We paid particular attention to the features we designed into the platform to support self-regulation and personalizing the experiences to meet their preferred learning goals such as allowing for free choice of learning materials, flexibility of when and how long to engage, providing personal recommendations based on user input, internal systems to track progress, and generating certificates of completion. In this study, we used a case study with both quantitative and qualitative data to examine whether teachers had gains in meeting learning goals related to their development in teaching statistics and data science, had sustained engagement, and found the features for personalization supportive for their learning. Results showed, overall, positive growth towards meeting learning goals and making small changes towards improved classroom practice. Most teachers were generally engaged in sustained ways across the study period, though we found six different patterns of completion that highlight ways in which teachers’ goal-directed and self-regulated learning occurred within the busy schedules of educators. Several personalized features, especially the recommendations and tracking system, were highly utilized and perceived as supportive of teachers’ learning.

  PublisherOA PDFDOI

• The Viability of Topic Modeling to Identify Participant Motivations for Enrolling in Online Professional Development

  Online Learning · 2024-03-01 · 2 citations

  articleOpen access

  Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify participants’ motivation for enrolling in two successive statistics education professional development online courses. Computational text mining, such as topic modeling, is a learning analytics field that has proven effective in analyzing large volumes of text to automatically identify topics or themes. This contrasts with traditional qualitative approaches, in which researchers manually apply labels (or codes) to parts of text to identify common themes. Combining topic modeling and qualitative research may prove useful to education researchers and practitioners in better understanding and improving online learning contexts that feature asynchronous discussion. Three topic modeling approaches were used in this study, including both unsupervised and semi-supervised modeling techniques. The three topic modeling approaches were validated and compared to determine which participants were assigned motivation themes that most closely aligned to their posts made in an introductory discussion forum. A discussion of how each technique can be useful for identifying topical themes within discussion forum data is included. Though the three techniques have varying success rates in identifying motivation for enrolling in the MOOCs, they do all identify similar themes for motivation that are specific to statistics education.

  PublisherOA PDFDOI

• Data Moves as a Focusing Lens for Learning to Teach with CODAP

  Computers in the Schools · 2024-10-17 · 4 citations

  article
  PublisherDOI

• Preparing Secondary Prospective Teachers to Teach Mathematics with Technology

  2024-01-30

  book-chapter

  The Association of Mathematics Teacher Educators’ (2017)Standards for Preparing Teachers of Mathematics (SPTM) has set aspirational goals for the preparation of well-prepared beginning teachers of mathematics that includes teaching with mathematics action technologies. These standards require that beginning teachers of mathematics not only be proficient users of technologies, but also understand how to use technology in meaningful ways to support students’ thinking. In this chapter we describe where we are as a field with respect to meeting the goals of the SPTM related to teaching with technology. With problems of practice and theory related to the work of MTEs in preparing secondary mathematics teachers to teach mathematics with technology as a guidepost, we lay out foundational work in this area (the past), followed by the current efforts by three projects to address the practical and theoretical challenges MTEs are facing. Finally, we describe our vision for the work that is needed down the road for us to make headway towards meeting the aspirational goals laid out in the AMTE SPTM.

  PublisherDOI

• Visualizing qualitative data: unpacking the complexities and nuances of technology-supported learning processes

  Educational Technology Research and Development · 2023-07-28 · 3 citations

  articleOpen accessSenior author

  Analyzing qualitative data from learning processes is considered “messy” and time consuming (Chi in J Learn Sci 6(3):271–315, 1997). It is often challenging to summarize and synthesize such data in a manner that conveys the richness and complexity of learning processes in a clear and concise manner. Moreover, qualitative data often contains patterns that are not immediately apparent. Consequently, visualization can be an effective tool for representing and unpacking the complexities and multidimensions of learning processes. Additionally, visualizations provide a time-efficient approach to analyzing data and a high-level view of the learning process over time for researchers to zoom in on intriguing moments and patterns (Huang et al. in Comput Human Behav 87:480–492, 2018). In this conceptual paper, we provide a broad overview of research in the field of visualizing qualitative data and discuss two studies (1) visualizing role-changing patterns in an interdisciplinary learning environment and (2) operationalizing collaborative computational thinking practices via visualization. By leveraging these studies, we aim to demonstrate a visualization processing flow along with qualitative research and methods. Particularly, the processing flow includes three critical elements: research subjectivity, complexity of visual encoding, and purpose of visual encoding. The discussion highlights the iterative and creative nature of the visualization technique. Furthermore, we discuss the benefits, challenges, and limitations of using visualization in the context of qualitative studies.

  PublisherDOI

• Validating a concept inventory for measuring students’ probabilistic reasoning: The case of reasoning within the context of a raffle

  The Journal of Mathematical Behavior · 2023-06-30 · 3 citations

  articleOpen access1st authorCorresponding

  Assessing students’ conceptions related to independence of events and determining probabilities from a sample space has been the focus of research in probability education for over 40 years. While we know a lot from past studies about predictable ways students may reason with well-known tasks, developing a diagnostic assessment that can be used by teachers to inform instruction demands the use of familiar and unfamiliar contexts. This paper presents the current work of a research team whose aim is to create a formative concept inventory with strong evidence of validity that uses a psychometric model to confidently predict whether a student exhibits one or more misconception across many items. We illustrate this process in this paper using a particular item with a context of a raffle aimed to measure whether a student reasons with misconceptions related to independence or equiprobability. The results of two aspects of the validity process: cognitive interviews to assess response processes on individual items, and a large-scale administration to examine internal structure of the concept inventory revealed difficulties in assessing students’ reasoning about these key probability concepts and trends in the prevalence of misconceptions across grades. Results can provide guidance for others aiming to develop assessments in mathematics education and also support further possibilities for research into understanding students’ reasoning about independence and sample space.

  PublisherDOI

• Supporting teachers' confidence to teach statistics through a blended professional development approach

  HAL (Le Centre pour la Communication Scientifique Directe) · 2022-02-02

  preprintOpen accessSenior author

  International audience

  Publisher

Recent grants

• Noyce Mathematics Education Teaching Scholars at NC State University

  NSF · $994k · 2007–2015

• Invigorating Statistics Teacher Education Through Professional Online Learning (InSTEP)

  NSF · $3.1M · 2019–2026

• Collaborative Research: Preparing to Teach Mathematics with Technology - Examining Student Practice [PTMT-ESP]

  NSF · $438k · 2018–2024

• Preparing Prospective Mathematics Teachers to Teach with Technology: An Integrated Approach

  NSF · $75k · 2005–2007

• Collaborative Research: Enhancing Data Science and Statistics Teacher Education--Transforming and Building Community

  NSF · $2.1M · 2022–2028

Frequent coauthors

• Jennifer N. Lovett

  18 shared

• Karen Hollebrands

  North Carolina State University

  16 shared

• Gemma Mojica

  North Carolina State University

  16 shared

• Taylor Harrison

  University of North Florida

  9 shared

• Dũng Trần

  Centre National de la Recherche Scientifique

  8 shared

• Tina Starling

  North Central State College

  7 shared

• Helen M. Doerr

  Syracuse University

  7 shared

• Hamid Sanei

  North Carolina State University

  5 shared

Education

• Ph.D., Education

  University of North Carolina at Chapel Hill

  2004

• M.S., Education

  University of North Carolina at Chapel Hill

  2000

• B.A., English

  University of North Carolina at Charlotte

  1997

Awards & honors

• Judith E. Jacobs Lecture Award by the Association of Mathema…
• Innovators Award by the NC Council of Teachers of Mathematic…
• William D Warde Statistics Education award from the Mu Sigma…
• Robert Foster Cherry Award for Great Teaching from Baylor Un…
• Finalist for the Robert Foster Cherry Award for Great Teachi…