As the demands of the 21st century workforce increasingly focus on data use and interpretation, teacher-educators will play a critical role in preparing early childhood teachers to lay the building blocks for data science and data literacy. Teacher-educators serve as the bridge between research and classroom practice, shaping pre-service coursework, modeling instructional strategies, and mentoring teachers as they develop their pedagogical approaches (Bjorklund, van den Heuvel-Panhuizen, & Kullberg, 2020; Utts, 2016). Without explicit attention to data science in teacher preparation programs, early childhood teachers may enter the field without the knowledge or confidence to guide children’s data-rich explorations (Lewis Presser, Young et al., 2023; Lewis Presser, Young et al., 2025). This is particularly pressing given the American Statistical Association’s call for data literacy to begin in the earliest years of schooling (Bargagliotti et al., 2020) and the National Academies’ recent emphasis on data science as foundational across K–12 education (NASEM, 2023). Unfortunately, data literacy rates are falling, increasing the need to create a data-literate society by providing equitable, early access to data science education and tools (Aponte et al., 2025).
Research demonstrates that teacher-educators have a lasting influence on the instructional beliefs and practices of teachers, especially in mathematics and science, which are often areas of discomfort for early childhood educators (DePiper et al., 2021; Duncan et al., 2007). Therefore, equipping teacher-educators with both content knowledge and instructional resources in data science will be a critical step forward—ensuring young children’s early experiences with data are developmentally appropriate, engaging, and meaningful.
Importance of Mathematics and Data Science in Preschool
In early childhood, we view data science as an authentic and meaningful context for engaging early learners in mathematics as they solve interesting problems by collecting, sorting, organizing and interpreting data. The recent national calls to strengthen data science education underscore our central argument: that data science is not only appropriate but essential for preschool education, where early experiences in counting, sorting, and classifying actively engage students in real-world mathematics. Indeed, a key goal of early childhood education is to enhance students’ understanding and development of mathematical concepts and skills (Bjorklund et al., 2020) as mathematics is considered a foundational component of cognitive development (Clements & Sarama, 2016). Studies show that acquiring early mathematics knowledge before kindergarten predicts subsequent academic success across content areas (Duncan et al., 2007; Preskitt et al., 2020) and cultivates interest in STEM (Alexander et al., 2012; Fisher et al., 2012). Indeed, early data science concepts like sorting, comparing, categorizing, and interpreting data are well-aligned with early learning goals. Data science is a comprehensive and interdisciplinary field focused on extracting meaning from data, which may include the generation, collection, processing, analysis, visualization, and interpretation of that data. The goal is to leverage data to gain insights about the world. It is essential to develop data science skills, or “data acumen,” in early education (Boaler, 2021; Lee, 2022; Martinez & LaLonde, 2020) because young children are inherently data scientists, continuously absorbing information and organizing it to understand their surroundings.
Furthermore, data science can be a developmentally appropriate context for young children to practice their foundational mathematics skills, such as counting, sorting, and classifying. For example, as children ask questions and explore answering them by collecting and analyzing data, they can sort and group objects by one or more attributes, count how many in each category, and compare the categories to identify which has the less and which has more.
A key aspect of data science learning for young children includes creating and interpreting data visualizations and displays (e.g. graphs, tally charts). Children can “read” the displays to help answer their questions. For example, if the class wants to know, “What snack do the most children enjoy?”, the teacher can help children identify categories and then vote on their favorite snacks. By creating a tally chart of the votes, children can view this visualization and identify which category has the most votes (e.g. pretzels have greatest number of votes) and based on this, interpret that data to mean that most children would prefer pretzels for snacktime.
Data science transcends mathematics and science domains and serves to connect them. Children trying to answer a research question by collecting, organizing and representing data can participate in significant STEM inquiry (Lewis Presser et al., 2023) and build strong academic skills. Indeed, engaging in data science produces visual representations of mathematics that support students to better understand relationships between quantities (Stylianou & Silver, 2004), facilitate communication of thought processes and support problem solving (DePiper et al., 2021), and boost overall mathematics achievement (de Araujo et al., 2018). However, early childhood teachers tend to be generalists, without specific training in data science and limited pre-service training or professional development in mathematics. As a result, they report uncertainty about how to support early learning goals related to data and measurement (Lewis Presser et al., 2023). Thus, preparing teacher- educators to guide early childhood teachers in teaching data science is essential.
Developmentally Appropriate Data Science for Preschoolers
Data science engages preschoolers in problem-solving with data, engaging their developing mathematical skills in counting, sorting, classifying, comparing, and contrasting to address significant research questions. Consequently, data science is a natural part of early childhood as children are constantly organizing small toys, objects, or cards, physically arranging themselves into categories, and shows interest in visually representing those categories through displays (Platas, 2018). Although this process may be instinctive for children, it requires teachers to skillfully guide and scaffold young children’s learning to make their thinking visible.
For early data science to be developmentally appropriate it needs to be play-based and utilize hands-on manipulatives, pictures, and physical movement in the collection, organization, and visualization of data. In this way, children can apply their growing mathematics skills to explore and solve real-world questions.
Resources for Early Childhood Math Teacher- Educators
Given that most early educators and early grades teachers have not been trained in data science or statistics, they need professional development and curricular supports not only to understand what to teach in data science, but also how to teach it through engaging experiences. To effectively address this problem and include data science in preschool, teachers need curricula, professional supports, and tools to help implement lessons (Aponte et al., 2025). Thus, we developed an open source, supplemental curricula and a free digital teachers’ guide that includes lesson plans and resources for teachers, plus a video library to support teachers’ instruction. Also available, but not required to implement, is a free teacher-facing digital app, Preschool Data Toolbox, available on iPad and Android tablets that guide teachers and students through simple, age-appropriate investigations with data and the easy creation of data displays.
The goal is to provide teachers with the instructional resources they need to engage young children—from preschool through the early elementary grades—in data investigations that meaningfully build on and strengthen their growing mathematics skills in counting, sorting, and classifying. Along with a versatile, practical data visualization tool, the curriculum includes six comprehensive investigations, and a “create your own” option to encourage children to formulate and explore ways to purposely use data.
Importantly, these resources are not only intended for classroom teachers but can also serve as a professional learning tool for teacher-educators, with separate “About” data science and “Preparation” sections. By using the Preschool Data Toolbox, video library, and lesson plans in coursework or workshops, teacher-educators can model effective practice and demonstrate how to scaffold children’s data-rich investigations. Embedding these resources in pre-service preparation ensures that future teachers enter the classroom with greater confidence in guiding playful, data-focused learning (DePiper et al., 2021).
Using the Preschool Data Toolbox to Engage with Data Investigations
The Preschool Data Toolbox can support teacher -educators to demystify data concepts and support teachers to lay the building blocks of data literacy through a set of carefully sequenced investigations. These investigations begin with dichotomous (yes/no) categories and gradually build from there, introducing additional categories and data visualizations that build young children’s data literacy step by step. For example, in the What do we wear? Investigation children begin by sorting themselves and their clothing one attribute at a time (do my clothes have zippers, yes or no?). In each investigation, children are encouraged to engage with data through three separate activities that build on one another. These can be done with or without the app. However, the app was specifically designed with early learning teachers to facilitate data collection, visualization, and analysis with young children, while keeping their need for movement and hands-on exploration in mind. This allows teachers and students more time to focus on discussing data—a step in the process typically left out in early learning classrooms.
In the first activity of What do we wear?, teachers and children select from a series of research questions—such as “Do you have a zipper?”, “Do you have a button?”, or “Are you wearing socks?”. Once a yes/no research question is selected (Figure 1), they can add in the relevant category, such as “zipper” and “no zipper” (with corresponding images for pre-literate children), and then choose the range of expected values (Figure 2). Next, data can be entered with a simple interface of “plus” and “minus” images that teachers or children can tap. The resulting data can be viewed as a pictograph, a graph with individual blocks, or a bar graph (Figure 3). For teacher-educators, there are videos around lesson implementation, building sorting skills, and making a useful graph.
Figure 1. Selecting a Research Question
Figure 2. Selecting Category Labels and Range
Figure 3. Transforming Data (Pictograph, Block Graph, or Bar Graph)
To foster discussions about data, the graph includes question prompts, the ability to sort categories (ascending, descending, drag and drop), and transform that data (per Figure 3). Most importantly, the app allows teachers and children to digitally draw or write on the screen and annotate the graph to aid in comprehension and analysis. These scaffolded investigations also provide a model for teacher- educators to introduce data science concepts to teachers in training. By experiencing the investigations as learners themselves, teachers can better anticipate children’s needs and see how developmentally appropriate activities can progress in complexity and build children’s understanding. This dual-purpose design—supporting both teacher educators and early childhood teachers—creates a pathway for sustainable integration of data science in preschool classrooms (Bargagliotti et al., 2020).
Promoting Teachers’ Implementation
To foster teachers' implementation of data-focused investigations and to supplement the lesson plans in the teachers’ guide, there is also a video library that teacher- educators’ can use to help guide teachers through each investigation and learn how to incorporate data into the classroom. The preparation section of the teacher's’ guide also provides learning goals and related vocabulary, describes the materials needed, and provides background information that can help teacher- educators to equip teachers to work with data and engage young children in investigations.
Next Steps
To ensure preschool children have access to data-rich learning experiences, it is important to integrate data science into both early childhood classrooms and teacher preparation programs. Building from current recommendations (Bargagliotti et al., 2020; NASEM, 2023), we highlight three priorities:
- Enhance Teacher Education and Foster Continued Professional Development. Teacher-
- Provide Accessible, Play-Based Resources. To make data science developmentally appropriate, teachers need concrete supports such as lesson plans, manipulatives, and digital tools that scaffold children’s investigations. Expanding access to the free Preschool Data Toolbox app, teacher
’s’ guide, and video library provides educators with resources that align with how children naturally learn through play, sorting, and categorizing (Platas, 2018; Boaler et al., 2021). These tools help bridge the gap between abstract concepts and children’s lived experiences, enabling meaningful engagement with data from the earliest years. - Support Additional Research and Policy to Foster Data Science. Future research should examine the long-term impact of early data science learning on later mathematics and STEM achievement (Duncan et al., 2007; Alexander, Johnson, & Kelley, 2012). At the policy level, embedding data literacy expectations into early childhood standards and teacher preparation requirements would create coherence across the education pipeline. As the American Statistical Association has emphasized, developing data literacy should be viewed as a core competency, beginning in preschool (Bargagliotti et al., 2020 & Aponte et al., 2025).
By emphasizing teacher- educators’ preparation, providing developmentally appropriate curricular supports, and advocating for research and policy shifts, we can create a system where data-rich experiences are a routine part of preschool classrooms. This effort not only strengthens early mathematics learning but also lays the foundation for a generation of students who are confident, critical, and capable data thinkers (Martinez & LaLonde, 2020).
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