







































The Potential of Data Collection and Analysis Activities for Preschoolers: A Formative Study with Teachers


 Global Journal of Education and Allied  

Research (GJEAR) 
Volume.13, Number 7; July-2022; 

ISSN: 2837-3707 | Impact Factor: 6.79 

https://zapjournals.com/Journals/index.php/gjear  

Published By: Zendo Academic Publishing 

 

 

pg. 22 

SHAPING EARLY MATHEMATICAL MINDS: A STUDY OF DATA 

COLLECTION AND ANALYSIS IN PRESCHOOL 

 
1Dr. Emily Grace Johnson and 2Dr. Benjamin Michael Thompson 

 

Article Info  Abstract 

Keywords: Preschool, 

mathematics, computational 

thinking, data collection and 

analysis, intervention, hands-on 

activities. 

 This formative study initiates a series aimed at informing the 

design and evaluation of an innovative intervention fostering 

mathematics and computational thinking (CT) engagement among 

preschoolers. A design-based research approach was adopted to 

develop and assess hands-on curricular investigations along with a 

teacher-centric digital app, emphasizing collaborative control 

rather than autonomous play. The project seeks to determine the 

developmental appropriateness of data collection and analysis 

(DCA) aligned with preschool curricula, while also investigating 

the potential of hands-on activities and the accompanying app to 

stimulate data-driven exploration for enhanced mathematics and 

CT learning. The intervention's curriculum involves preschoolers 

in DCA processes through interactive investigations, facilitated by 

a teacher-oriented digital app supporting data collection, graph 

creation, and comparison. These activities leverage mathematical 

concepts such as counting, sorting, and classifying, grounding 

them in real-world problem-solving contexts to lay the 

groundwork for mathematical and computational thinking skills 

development. By employing the app, teachers assist students in 

inputting and interpreting data, while diverse representations like 

pictographs, object graphs, and body graphs enrich the learning 

experience. The study encompasses three iterative cycles of 

intervention development and testing. This paper details the first 

cycle, focusing on a formative interview and survey study 

conducted with preschool teachers. The study elicited feedback on 

the developmental appropriateness and alignment of DCA 

investigations and the teacher-oriented digital app with preschool 

curricula and routines. Moreover, the study explored design 

elements promoting usability and learning, alongside the potential 

benefits and challenges associated with employing DCA 

investigations and the app for educational support. 
 

                                                      
1Education Development Center, 96 Morton Street, 7th Floor, New York NY 10014, USA 
2Education Development Center, 96 Morton Street, 7th Floor, New York NY 10014, USA 

https://zapjournals.com/Journals/index.php/gjear


Global Research Journal of Management and Social Sciences (GRJMSS) Vol. 13 (7) 
 

pg. 23 

1. INTRODUCTION  

This formative study is the first in a series intended to inform the development and testing of an intervention 

to promote preschoolers’ engagement with and learning of mathematics and computational thinking (CT). 

This was the first of several iterative cycles within a design-based research [1] approach focused on developing 

and testing the hands-on and play-based curricular investigations and accompanying teacher-facing digital 

app. By teacher-facing, we emphasize that this is a tool intended for teachers to primarily control, as opposed 

to a game or simulation that children play independently. This innovative project aims to build critically 

needed knowledge about the extent to which preschool teachers think that data collection and analysis is  

DOI: 10.5121/ijite.2022.11101 

Developmentally appropriate and aligns with preschool curricula and routines. Also, the elements of hands-

on, play-based activities and accompanying app will likely engage preschoolers in collecting and analyzing 

data, and ultimately supporting the learning of mathematics and computational thinking. 

The intervention’s curricular investigations are designed to engage preschoolers in the data collection and 

analysis (DCA) process to answer questions of interest. A key component of the intervention is a teacher-

facing digital app to support the collaboration of preschool teachers and children in collecting data, creating 

simple graphs, and using the graphs to compare data, make pre-dictions, and answer questions.  The curricular 

investigations offer an applied context for using mathematical knowledge (i.e., counting, sorting, classifying, 

comparing, contrasting) to engage with real-world problems and lay the foundation for developing math and 

computational thinking skills. 

The intervention includes a series of nine hands-on and play-based problem-solving investigations. The use 

of a digital app by teachers with their students scaffolds the investigation process and sup-ports specific data 

collection and organization steps (i.e., collecting, recording, and visualizing data). To be clear, the use of the 

technology here is to make the process easier, not to in any way to automate the teaching process that should 

remain focused on developing children’s conceptual understanding [3, 4]. As part of each investigation, 

teachers work with children to input their data into the app and use the resulting digital graphs to help children 

interpret and discuss the results and answer authentic research questions. The activities also include 

opportunities to represent data with pictures (pictograph), hands-on manipulatives (object graphs), and with 

children’s bodies (body graphs).  

This project includes three iterative cycles of development and testing of the intervention. This paper reports 

on the first cycle of development and testing in which researchers conducted a formative interview and survey 

study with preschool teachers to elicit feedback on: (1) the extent to which the data collection and analysis 

investigations and a teacher-facing digital app are developmentally appropriate and aligned with existing 

preschool curricula and routines; (2) the design elements of the investigations and teacher-facing digital app 

that teachers think will foster usability and learning; and (3) the affordances and challenges of using DCA 

investigations and a teacher-facing digital app to support learning.  

2. LITERATURE REVIEW 

There is a pressing need to invest in preschool STEM (science, technology, engineering, and mathematics) 

education, as research shows early childhood is a time when learners begin to develop positive attitudes toward 

science and mathematics [5] and form their STEM identity and professional interests [6]. Early exposure to 

high-quality inquiry-based STEM experiences also boosts children’s motivation, persistence, and self-

efficacy, which can spark and sustain children’s interest in pursuing STEM opportunities later in life [7, 8, 9]. 

This is critical, as there is an urgent need for highly skilled workers in STEM fields that include a 21st century 

data-capable workforce [10]. This has led to a growing interest in computational thinking (CT) [11, 12] and 

spurred recent efforts to prepare students from the early grades through high school by developing curricula 

to foster CT in prekindergarten through grade 12 education. Although definitions for CT vary, it is generally 

https://doi.org/10.5121/ijite.2022.11101


Global Research Journal of Management and Social Sciences (GRJMSS) Vol. 13 (7) 
 

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understood to be a problem-solving process that requires the collection and use of data to answer authentic 

questions. A literature review of apps to support coding for pre-schoolers found that apps do positively support 

learning in this domain [13]. The common elements of CT for preschoolers are inquiry-based and include 

asking questions and then collecting, organizing, representing, and analyzing data with the goal of efficiently 

addressing authentic, real-world problems [14, 15].   

2.1. Data Collection and Analysis (DCA) For Preschoolers 

It is fair to ask: is data collection and analysis (DCA) a developmentally appropriate content area for 

preschool-aged children? The results of this study help to answer this question, though prior research also 

supports the use of DCA content with preschool children [11 Clements 2008]. In fact, recent research suggests 

that the development of mathematical knowledge and early statistical reasoning begins at a younger age than 

is typically acknowledged [12 English 2013]. Educational interventions that are designed based on an 

understanding of young children’s cognitive development can support children’s engagement with complex 

mathematical concepts while they are still quite young [16, 17, 18, 19, 20, 21]. 

Very little research exists on how to teach data collection and analysis (DCA) during the preschool years, 

despite the fact that the mathematics education literature indicates that preschoolers can engage in simple, 

teacher-led investigations. In fact, DCA activities are not new to early childhood classrooms, as instances can 

be found of teachers and children engaging in DCA activities, such as measuring each child’s height and then 

displaying the varying heights on a chart. Children are also often found sorting and counting objects and then 

displaying them so that they can easily see and “analyze” which set has more. But to do this well, teachers 

need to set up the activity and scaffold which variables to consider, which data to collect, and how to represent 

the data in such a way that children can use it to effectively answer questions and draw conclusions [19,22]. 

To guide the process, each investigation in the intervention follows a simple set of steps that include 

formulating the question, collecting data, analysing data, and interpreting results [23]. 

This study puts a renewed emphasis on DCA activities in preschool and investigates the promise of DCA as 

developmentally appropriate activities that can support preschoolers’ mathematics and CT learning. Future 

studies will investigate how implementation aides in the learning process [2]. Table 1 shows descriptions of 

the proposed classroom investigations, which were shared with teachers during this study. 

Table 1. Description of Proposed Investigations 

Investigation  Description of Investigation  

Data Shuffle  The goal of the Data Shuffle investigation is for children to use their 

bodies to make two object graphs. The first graph is an animal graph 

and the second is an animal movement graph.   

Hungry   

Caterpillar  

Using the Very Hungry Caterpillar story, children make object graphs 

using their bodies and pictographs to represent the fruit and food eaten 

by the caterpillar each day. Children then use the app to make additional 

graphical representations of the data.  

Clothing   

Investigation  

The goal of the Clothing Investigation is to use attributes (qualities) of 

clothing to sort children into groups that can then be represented 

graphically and discussed.  

Children then use the app to make additional visual representations of 

the data.  

Centers   

Investigation  

The goal of the Centers investigation is for children to recognize, count, 

and compare the colors, shapes, and materials at three center time 

stations. Children then use the app to make additional visual 

representations of the data.  



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Feelings  

Freeze  

The goal of the Feelings Freeze investigation is for children to collect 

data to answer the question, “How are the members of our class 

feeling?” Children graph the data and compare the graphs over time to 

notice changes.  

Measurement 

Investigation  

The goal of the Measurement investigation is for children to 

collectively use their bodies – full bodies arms outstretched, full bodies 

narrow, and their shoes – to measure distance. Their measurements are 

entered into the app to create a pictograph or bar graph for analysis and 

discussion.  

2.2. The Use of Technology with Preschool Children 

Using data collection and analysis to solve problems involves a multi-step process that places a heavy 

cognitive load on young children. To effectively engage young children in the steps of data collection, data 

organization, and interpretation of data can be challenging, and requires thoughtful scaffolding. To be 

successful, children must collect and organize their data and then represent that data in a way that allows them 

to directly compare quantities across categories [24]. Prior research indicates that preschool teachers regularly 

use tablets for multiple purposes [25], but that teacher’s attitude toward and confidence with using technology 

affects its implementation and use [26]. Digital tools can support teachers to effectively scaffold this process 

by breaking it down into discrete steps and in recent years, preschool teachers have reported more positive 

attitudes toward using technology with young children [27]. Having the ability to quickly and easily create 

graphs also enables teachers to focus on viewing and discussing the data with children, rather than spending 

time drawing and representing the data by hand. Yet, it is critical to ensure that the developed technology 

meets the needs of teachers, a goal for this particular study. 

Yet, research about how the features of digital representations help or hinder preschoolers’ engagement in 

DCA activities is lacking. While there is evidence that preschoolers can understand graphs as representations 

of quantity, Clements and Sarama [28] note a lack of evidence as to what supports young children’s data 

analysis competencies; though importantly, subsequent work has suggested that computers hold promise for 

helping young children learn DCA. Likewise, as [29] suggests, we focus on how to design learning 

experiences with technology. With this focus in mind, this study seeks to leverage teacher experience to design 

this intervention help teacher implement and engage pre-schoolers in DCA, ultimately, helping to inform the 

ongoing intervention development and inform the literature base about how to foster DCA skills with 

technology. 

3. METHODS 

This study was the first in this project’s overall design-based research approach [1]. Building off the design-

based research approach [1], our work used an evidence-based curricular design framework [30] to guide the 

process of intervention development. By including multiple perspectives in the iterative design process, we 

hope to increase the usefulness and quality of the resulting intervention. Prior to this study, the research team 

created a “learning blueprint” that articulated the targeted learning goals based on prior descriptions of the 

learning trajectory for young children [28]. The learning blueprint serves as a “anchor” that is tied directly to 

each learning activity and assessment task children experience. 

The primary goals of the study were to (1) investigate teachers’ perception of data collection analysis as 

developmentally appropriate and aligned with preschool curricula and routines; (2) identify the design 

elements of learning activities and teacher-facing digital app that foster usability and support the teaching and 

learning of DCA; and (3) elicit teacher perceptions of the affordances and challenges to using the 

investigations and teacher-facing digital app to support children’s learning, including the educators’ 

perception of the comprehensibility of the DCA activities and their willingness to implement the DCA 



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activities. This survey and interview study followed an initial phase of development that produced the 

prototype versions of both the curricular investigation activities and the digital app. Our team then leveraged 

this information to inform curricular revisions prior to two classroom-based implementation studies [e.g. 2]. 

3.1. Research Questions  

This study focused on the following research questions:    

1. Developmental Appropriateness and Curricular Alignment: To what extent do preschool teachers 

perceive the use of data collection and analysis as developmentally appropriate and aligned to preschool 

curricula and routines?  

2. Design Elements to Foster Use and Learning: What design elements of the learning activities and 

digital tool (such as collaborative data collection and visual data display) do teachers think will increase (a) 

usability and (b) preschoolers’ ability to understand how to use data to answer questions?  

3. Affordances and Challenges: What do teachers view as the potential affordances and challenges to 

successful engagement in data collection and analysis?  

3.2. Participants  

Teachers were recruited from Head Start and state-funded prekindergarten programs in Massachusetts, Rhode 

Island, and New York.  Participants included 29 teachers who work with children ages 4 and 5 (n=10 

participated in an interview with a researcher; n=19 completed an online survey). Participating teachers 

completed either the interview or the survey, but not both, to provide feedback on the hands-on curricular 

investigations and digital app. Teachers were recruited in the spring of 2020 at the beginning of the global 

pandemic so recruitment, survey administration, and interviews were conducted digitally or virtually. 

Demographic data about participating teachers was not collected. 

3.3. Instruments 

Teacher Interview (n=10). During a 45-60-minute interview conducted via remote meeting software (e.g. 

Zoom, Ring Central), researchers showed participants a series of PowerPoint slides and short videos that 

demonstrated the hands-on, play-based lessons and teacher-facing digital app prototype for each curricular 

investigation. Participants then responded to questions about the appeal of using the investigations, anticipated 

challenges, ease of implementation, developmental appropriateness, and curricular alignment.   

  

Teacher Survey (n=19). Teachers completed the online survey independently. Teachers viewed the same 

videos that were included in the teacher interview, as well as written summaries focused on the preparation 

and materials needed for each investigation. For each investigation, teachers then answered a combination of 

multiple choice and open-ended questions about the appeal of the investigation, anticipated challenges, ease 

of implementation, developmental appropriateness, and the extent to which they perceived the investigation 

as meetings its learning goals. Because of time limitations, each teacher viewed four of the six investigations 

during their interview, with the investigations counter-balanced across participants.  

3.4. Analysis 

This mixed methods analysis included qualitative summarization of interview responses nad open-ended 

survey responses, in addition to descriptive quantitative findings from survey ratings. Audio recordings of 

teachers’ interview responses were transcribed and then analyzed such that teachers’ closed-ended responses 

were descriptively summarized and their open-ended (transcribed) responses were qualitatively analyzed. 

Researchers qualitatively coded and examined the responses to each open-ended item, looking for patterns 

that emerged, and summarized participants’ responses. To examine the intersection of qualitative and 

quantitative data, we used a mixed method analysis involving the creation of a “joint matrix” [31] to highlight 

patterns of implementation and suggest modifications [30]. These findings provided feedback about the 

investigations’ usability and comprehensibility, and informed program revisions in preparation for a 

classroom-based study. 



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4. RESULTS  

4.1. Teachers’ Overall Feedback  

Results of the interview and survey indicated that data collection and analysis activities are an authentic part 

of early childhood classrooms that provide an effective way to introduce computational thinking in preschool. 

Specifically, a majority of teachers indicated that they view data collection and analysis as being engaging for 

children, relevant to their preschool curricula and learning standards, and feasible to implement in early 

childhood settings. A large majority of teachers also viewed the prototyped curricular investigations as fun, 

related to their curriculum, and feasible to do in their classrooms. Teachers noted that DCA content builds on 

and extends the mathematics that children are already learning, and they felt the play-based and real-world 

elements of the investigations would be engaging for children. Importantly, some teachers mentioned that they 

currently have learning standards related to DCA that their school requires them to meet, but they were often 

unsure of how to meet those requirements in practice. These teachers noted that the investigations provided 

them with a better understanding of what developmentally appropriate DCA activities can look like. In 

addition, teachers noted that they felt the investigations could support formative assessment (e.g., gauging 

where individual children are in developing their counting or sorting skills) while simultaneously engaging 

learners across developmental levels. This is particularly important in Head Start classrooms where students 

can range in age from 3-years-old to 5-years-old, making activities that effectively engage all learners 

particularly valuable. For example, one teacher stated “These all seem like really great ideas and I think it 

would be so helpful for teachers to have this supplemental app to help with assessment purposes and just in 

general to make this area of learning more fun to learn for younger children!”  

As part of the survey, teachers also rated how well each investigation meets its focal learning goals. Learning 

goals varied across the investigations and included mathematics goals such as classifying, sorting, and 

counting data to answer questions; and computational thinking goals such as describing what different parts 

of data visualizations represent, making comparisons between individual parts of data visualizations, and using 

data visualizations to answer questions. Teacher feedback strongly indicated that the DCA investigations 

successfully met the targeted learning goals. Across all investigations, the majority of teachers responded that 

each learning goal had been met by the proposed activity. After reviewing four investigations, one teacher 

wrote in their survey: “I feel this activity and all the ones viewed so far are giving me new and creative ways 

to introduce graphing and the app would be very helpful.”  

  

4.2. Developmental Appropriateness and Curricular Alignment (Research Question 1) 

Developmental Appropriateness  

Results suggest that—with the right scaffolding—teachers felt that introducing computational thinking (CT) 

through data collection and analysis (DCA) is developmentally appropriate for preschool children and is well 

aligned to preschool curricula and routines. Teachers noted that the content of the investigations aligned with 

their mathematics curricula, specifically in the areas of number sense, operations, and measurement.  With 

regard to how well this would fit into their curriculum, teachers said: 

They have to use their words to describe the graphs, and as far as science, they’re learning about animal 

characteristics, different body parts, different movements of animals. And then math, they’re comparing, 

they’re sorting. You can take away from a number on the graph, you can add a number. It fits perfectly within 

all realms of what we need in our curriculum.    

Something I always look for in activities is to see if they are adaptable to our curriculum…I think that’s a 

strong suit of this activity, that it is very adaptable. 

Anticipated Difficulty of DCA Content 

In order to better understand how developmentally appropriate DCA investigations are for preschool learners, 

we asked teachers to evaluate how easy or difficult it would be for children to complete particular activities 



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(or aspects of activities) within each investigation. Our analysis focused on a set of common themes related 

to sorting, representing, and interpreting data, as well as making predictions.  

For each investigation, teachers were asked to rate how difficult they anticipated the mathematics demands 

within the investigations’ activities would be for preschoolers (see Table 2 with anticipated difficulty levels 

for selected activities). These ratings varied, with some rated as being very easy (ranging from 6-50% of 

activities) and others rated as being difficult (ranging from 1122%). The teachers’ ratings matched the 

expected levels of difficulty for each investigation based on early mathematics learning progressions [32]. 

However, no activities were rated as being “very difficult,” suggesting that the mathematics demands included 

in each investigation are developmentally appropriate for this age group. Further, the perceived difficulty of 

the activities rated as being “difficult” could be intertwined with the perceived challenges related to behavior 

management during the activities, which is not a trivial concern for teachers working with young children.  

Table 2. Levels of Anticipated Activity Difficulty by Investigation 

Mathematical Skill  Investigation  Very 

Easy  

Easy  Difficult  Very   

Difficult  

Identify colors and 

shapes of items in a box 

frame (n=18)  

Centers  22%  78%  --  --  

Count objects in box 

frame (n=18)  

Centers  17%  83%  --  --  

Sort their own clothing 

(n=18)  

Clothing   50%  50%  --  --  

Sorting themselves 

(n=19)  

Data Shuffle  21%  68%  11%  --  

Sort dress up clothing 

(n=18)  

Clothing   11%  78%  11%  --  

Body Graph (n=19)   Hungry  

Caterpillar  

10%  74%  16%  --  

Collect data in small 

groups (n=18)  

Centers  11%  72%  17%  --  

Analyze data in small 

groups (n=18)  

Centers  11%  67%  22%  --  

Represent data by 

arranging data points on 

the graph (n=18)  

Centers  6%  71%  23%  --  

Comparing Graphs 

(n=18)  

Clothing   5%  67%  28%  --  

Make predictions (n=18)  Centers  --  61%  39%  --  

Sorting Data. Although most teachers reported that the number of sorting categories within a particular 

investigation or activity (ranging from 2-7 categories across investigations) was appropriate, responses varied 

and teacher comments offered additional insight into determining the “just right” number. For example, 95% 

of teachers rated the number of categories in Hungry  

Caterpillar (5-7 categories) as being “just right” and 79% of teachers said the same about Data Shuffle (3-5 

categories). In their responses, teachers indicated three key considerations for sorting: children’s previous 

experience with sorting and where they are developmentally, teacher scaffolding and support, and the steps 

required to complete the sorting process. For example, teachers said: 



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Depending on what level they’re at, they can just do it [sorting] by color and then we can go through the 

different shapes, or do it by shapes and then advanced ones would start doing it by size, so that would fit all 

levels throughout the year. It’s a very easy thing to tie into anything that we are teaching or playing.  

Sorting the dramatic play costumes can be done in few or many groups depending on how advanced the 

students are who are doing it. You can keep it simple or get as challenging as you feel necessary.  

I really love how this ties right into their play. I think children learn best through play and sorting clothes in 

dramatic play is just that! 

Representing Data. Results suggest that the DCA investigations provide engaging and developmentally 

appropriate opportunities for preschool children to graphically represent data. Teachers liked the use of body 

graphs (with four teachers reporting that they already use them) and felt that – with help from their teacher – 

children would be able to use the app to graph the data they have collected. As with sorting data, the process 

of representing data can be messy, especially if multiple children are doing it at the same time. But teachers 

also expressed confidence that children would be able to create data representations with scaffolding and 

support. Moreover, multiple teachers felt children would eventually be able to complete most of the graphing 

steps independently. One way to support this process, while minimizing the potential for chaos and confusion, 

is to arrange children in smaller groups at the start of an investigation, allowing the teacher to more easily 

individualize the supports that their students may benefit from. 

Interpreting Data. Results suggest that interpreting data may pose more of a challenge to children than 

sorting and representing data; however, teachers generally agreed that the DCA investigations provide 

important opportunities for children to develop this skill in age-appropriate ways. Six teachers indicated that 

children may initially struggle with comparing different graphs that represent the same data; 10 teachers 

reported that children would struggle with identifying changes in data over time; and 7 said it would be 

difficult for children to use graphed data to answer questions. Teacher comments also suggest that interpreting 

data could be particularly challenging for English Language Learners (ELLs). However, for each of these 

areas, teachers expressed confidence that most children could experience success with scaffolding and targeted 

teacher supports.  Finally, as noted earlier, teachers reacted positively to the idea of creating body graphs and 

felt that having children use their bodies to represent data is a great way to engage young and tactile learners. 

One teacher remarked that when using body graphs, it is important to give children opportunities to see the 

full picture of the graphed data. One way to support this is by employing a “fishbowl” strategy, where one 

group of students stands outside the body graph and observes another group of students inside the body graph. 

The students would then switch roles, giving each child opportunities to both participate in creating the body 

graph and seeing the graph overall. Another way to allow children to observe a body graph that they personally 

participated in is to take a photo of the graph and allow children to examine the photo after the activity is 

complete.  

Making Predictions. As part of interpreting data, children are asked to make predictions based on the data 

patterns that they see. Teachers’ responses were mixed as to how difficult it might be for children to make 

predictions. Six teachers noted that they ask children to make predictions during other learning experiences; 

nine reported that many preschoolers are not yet ready to do so. As with the number of sorting categories, 

predictions may be another example of a skill to target later in the school year or with higher levels of teacher 

scaffolding and modeling. For example, one teacher noted, “This may be a tough concept for the children to 

grasp because the children would have to comprehend what a prediction is. However, with modeling and 

examples and repetition they would grasp it in time.” 

Adaptability of Content  

Teacher comments also highlighted the natural variation in developmental trajectories that exist in early 

childhood mathematics. Yet overall, the results indicated that the teachers viewed the investigations, and the 

topic of data collection and analysis more generally, as being flexible enough to be adapted for learners at 



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different stages of mathematical development. For example, teachers mentioned liking that they could begin 

an investigation with only a few sorting categories, and incorporate more categories in later activities to 

increase the level of challenge. Designing the investigations with such flexibility is critical for allowing 

teachers to assess and adapt the lessons to their students’ developmental readiness to engage in DCA. For 

example, each investigation can be implemented at various times of the school year, in different group 

arrangements, and with different available materials. 

Importantly, teachers also felt that the levels of difficulty could be modulated with teacher support and 

flexibility. For example, teachers said:  

With teacher support such as encouraging the children to persist with examination of the clothing, this will 

be an easy task.  

With teacher help and the right questions I feel that the children would be able to look at the graphs and notice 

things that might be the same and notice if there are things that are different, especially with the picture one.  

I think if the teacher is helping and the graph is very clear in what it is looking for, then children will not have 

a problem.  

Curricular Alignment 

Teachers noted that the investigations build on what they already do in their classrooms and are well-aligned 

to their curricula, both in mathematics and in other content strands. One teacher interviewee remarked: “We’re 

always looking to do things that will fit into the standards we’re teaching. Fitting this into number sense, 

operations, and measurement fits well with what we’re doing already.”  

4.3. Design Features of the Intervention (Research Question 2)  

4.3.1. Key Features of Activities 

Investigations are hands-on. Teachers appreciated that the investigations were hands-on and provided 

opportunities for children to not only engage with visual representations of data, but also to participate in 

creating these representations. 

Children are very hands-on; they like to do things that are interactive. Having them sort themselves into 

groups, they’re taking the lead. We’re always trying to have them lead an activity.  

I really enjoy that it includes physical movement to involve the children and give them a sense that they are 

all collecting data that is meaningful rather than coming up one at a time to put a mark on a chart paper. I 

think it has great involvement, there isn't any long periods of wait time, and it gives each child a sense of 

independence. I like the fact that it involves technology which is an important piece in collecting data as well. 

It shows children that you have to physically collect evidence first before you can create a graph. I also enjoy 

how the technology piece is so simple that children can help in creating the graphs. It provides enough 

learning for all levels of students who are just learning, to collect information, who are able to create graphs, 

and then showing those who are ready, how graphs can look different.  

  

Teachers also liked that narrative stories and picture books were used to anchor the investigations (e.g., Five 

Creatures by Emily Jenkins undergirds the Data Shuffle investigation) and that the activities were both hands-

on and digital, including both movement and student autonomy.   

Materials are easily accessible. With a few exceptions, teachers appreciated that the investigations do not 

require special materials, allowing them to use the classroom materials they already have or can easily and 

inexpensively find (e.g., chart paper and name tags; printed cards with emoji faces). In a few cases, teachers 

noted that they would need to purchase materials (e.g., felt) or would need to modify an activity due to having 

a limited number of the required materials (e.g., enough fruit manipulatives for each student to participate).  

Activities are multidisciplinary. Teachers also appreciated that the investigations promoted child 

engagement with content and skills outside of mathematics. For example, several investigations begin with 



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the teacher reading a story that relates to the problem or question being investigated, which teachers viewed 

as being both engaging for children and supporting their emergent literacy skills. 

It’s seamless, it could fit into many different units. Later in the year we talk about transformation and there’s 

a whole unit about how caterpillars transform into butterflies…it’s also applicable in the beginning when we 

talk about taste and the five senses. 

4.3.2. Key Features of Teacher-Facing Digital App  

Teachers felt that the unique affordances of the digital app offered meaningful opportunities for children at 

different skill levels to easily engage in data collection, visualization, and analysis. A child who needs more 

support, for example, could use the app with their teacher to enter data they have collected, create a simple 

graph, and discuss any data patterns they see. Children who are further along in their developmental trajectory 

could create multiple representations of the same (or different) data, sort their graphed data, make comparisons 

between different graphs, and make predictions based on the data patterns they see. A few digital affordances 

were particularly appealing to teachers. For example, the ability to use the camera feature of the tablet to create 

personalized category labels on graphs, and the addition of an annotation feature that allows the user to draw 

on graphs, were notably appealing.  Another theme that teachers focused on throughout their feedback related 

to the ways in which the digital app could allow them to individualize their use of technology to children’s 

ages, which range in preschool settings from three to five years old, and readiness for the mathematics content.  

Camera Feature. The majority of teachers stated that they would use the app’s camera feature, as it allows 

for customization and helps students to recognize an item by seeing a real photo versus a drawing or cartoon 

image.  

Drawing Feature. Some teachers noted that they liked the app’s drawing feature, which allows users to hand 

draw their graph’s category icons or to annotate graphs (e.g., circling columns, writing numerals on top of 

columns to indicate the total number of units in those categories). These teachers found this feature relevant 

not only for their use, but also for allowing older or more advanced children draw their own icons.  

Sorting Categories by Ascending/Descending Order. Teachers stated that they would use the sorting feature 

to arrange their graphed data in ascending or descending order; however, three teachers noted that this type of 

arranging would be relatively advanced and would require additional scaffolding from the teacher in order for 

children to understand the ordering. Teachers suggested using the sorting feature with older children instead 

of younger, or during activities that take place later in the school year.   

4.4. Challenges and Key Considerations for Implementation (Research Question 3)  

Space Challenges. Space constraints emerged as being the biggest perceived barrier to curricular 

implementation. Specifically, two investigations (Data Shuffle and Hungry Caterpillar) require space for life-

sized body or object graphs that teachers need to demarcate on the floor using masking tape prior to the lesson. 

Although the majority of teachers said that the physical size of their classrooms would allow for this, or that 

they would be able to find an alternative accommodating space, a few noted that they would need to modify 

the activity in order to make it work in their classrooms. In addition, teachers reported the need to consider 

spacing more carefully as the Covid-19 pandemic requires physical distancing of students within classrooms. 

This indicates that creating body graphs in the classroom might be more difficult currently than in typical 

times. 

Ease of Preparation. Teachers viewed ease of preparation as an important factor in deciding whether to 

implement an investigation or which investigation to implement (see Table 3). Across all investigations, the 

majority of teachers reported that their perceived level of preparation would be very easy or easy, although 

their ratings varied by the complexity of the investigation. For example, all teachers rated the Feelings Freeze 

investigation as being very easy or easy because it required very few materials. On the other hand, 

investigations that required such steps as setting up a child-sized grid on the floor with masking tape (e.g., 



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Data Shuffle) or collecting different types of sorting materials (i.e., Centers Investigation) received a wider 

range of difficulty ratings.  

Table 3. Ease of Preparation  

Investigation*  Very Easy  Easy  Difficult  Very   

Difficult  

Data Shuffle (n=19)  21%  42%  37%  --  

Hungry Caterpillar 

(n=19)   

21%  68%  11%  --  

Clothing Investigation 

(n=19)  

53%  42%  5%  --  

Centers Investigation 

(n=18)  

6%  77%  11%  6%  

Feelings Freeze (n=18)  50%  50%  --  --  

  

* Measurement Investigation was omitted because it required no preparation. 

Our analysis of teacher responses surfaced three key factors driving perceptions of the ease of preparation: 

space (see previous section), time, and pacing.   

Time. The amount of time that teachers anticipated investing into their preparation emerged as an important 

consideration, though teachers expressed a willingness to invest the time needed to prepare for the activities 

that they believe will be fun and valuable for their students. For example, one teacher noted that it would her 

take some time to prepare the investigation materials, but she would be willing to do so because the activity 

seemed engaging for her children. Importantly, for planning purposes, teachers recommended providing an 

estimate of how long it will take to read the book paired with each investigation.   

Pacing. Across all investigations, most teachers indicated that the suggested pacing (i.e., whether an 

investigation is spread out over 1-2 days or 2-3 days) was appropriate; however, there was notable variation 

(see Table 4). It is likely that the best fit of pacing will vary based on individual classroom routines and length 

of the school day.  Just as the teachers felt that the developmental appropriateness of certain investigations 

may vary based on when in the school year teachers implement them, it is also possible that the appropriateness 

of the suggested pacing will vary based on this timing. 

Table 4. Pacing (N=19)  

  

Suggested  

pacing  

Just 

right  

Too 

short  Too long  Unsure  Missing  

Data Shuffle   1 day  11  5  --  --  3  

Hungry  

Caterpillar   
2-3 days  15  1  1  --  2  

Clothing  3 days  7  1  2  2  7  

Centers  2-3 days  7  3  1  --  8  

Feelings 

Freeze  

1-2 days  7  4  --  1  7  

Measurement  1-2 days  10  1  --  --  8  

5. DISCUSSION   

Overall, findings suggest that teachers see value in engaging in DCA with preschoolers, view the DCA 

investigations as developmentally appropriate, and think the integrated teacher-facing technology offers 



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meaningful affordances. These findings suggest that this technology-infused approach to CT and DCA is 

developmentally appropriate, builds on and extends what children are already learning in mathematics, has 

cross-curricular applications, and can be adapted to meet the needs of preschool learners along the full 

developmental trajectory. Furthermore, the teachers viewed the digital app being developed as part of this 

work as affording students an easy way to visualize and interpret data they collect. Teachers support the idea 

that young children can and should engage in meaningful data collection and analysis and computational 

thinking lessons.  

We also learned that designing flexibility into each investigation is critical to allow teachers to assess and 

adapt the learning to their students’ developmental readiness to engage in DCA.  For example, each 

investigation can be implemented at various times of the year, in different group arrangements, and with 

different available materials. Teachers also valued the opportunity to make use of the variety of graphical 

representations built into the curriculum, using both handson materials and the app (e.g., creating a bar graph 

and arranging the bars on the graph from most to least). 

Limitations. However, as the first in a series of studies in this body of work, the findings are limited. This 

study included interviews and surveys of a small group of preschool teachers in the United States, thus findings 

may be influenced by the current preschool education system and cultural context. Due to the Covid-19 

pandemic, all data collection was done remotely, which may have affected the extent of participants’ 

responses. As an early step in the development process, the activities were not tested by teachers with their 

students; however, subsequent work testing the intervention in classrooms found positive outcomes [2]. Yet, 

this first step is critical in the design-based research approach and improved the second version of the 

intervention activities and digital app. Future work is intended to further test and understand the impacts on 

classroom teaching and student learning.  

6. CONCLUSIONS 

Findings from the teacher interview and survey suggest that this technology-infused approach to addressing 

DCA in preschool is developmentally-appropriate, builds on and extends what preschool children are already 

learning in mathematics, has cross-curricular applications, and can be adapted to meet the needs of young 

learners along the full developmental trajectory. These findings were subsequently used to inform revisions 

of the intervention and app, with the overarching goal of improving preschoolers’ learning about data.  

Contributions to the field. This study is novel in two regards. First, it documents how a designbased research 

approach uses smaller, early studies to determine end-users, in this case teachers, needs and further designs to 

address those needs. It is critical to include teacher feedback in the design of research-based curricula, as 

development based entirely on theory is limited [33]. Second, the study findings in and of themselves affirm 

that preschool teachers agree that (1) DCA fits into the preschool classroom, (2) activities can be designed to 

be developmentally appropriate, and (3) that technology can help assist the process and allow teachers to foster 

the development of DCA related skills and conceptual understanding.  

Researchers can use these findings to better understand the scaffolds and elements of hands-on and play-based 

DCA activities and design features of a teacher-facing digital app that support preschool teachers in teaching 

data collection and analysis to their students. Long term, the hypothesis is that familiarizing children with data 

and learning how to talk about data at an early age may also positively influence children’s later math and 

data literacy skills—a question for future research.   

Future Studies. However, questions remain about effective practices and tools to facilitate learning these 

DCA skills. Subsequent stages of this research project involve testing the investigations in preschool 

classrooms. This will allow us to (1) further specify and refine our understanding of the practices and tools 

that support preschool children’s engagement in computational thinking through DCA, and (2) refine the 

curricular investigations – including the digital app – to better meet the needs of preschool children and 



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teachers. Findings suggest that this approach to teaching preschoolers about DCA merits further study to 

ensure productive implementation that positively influences preschoolers’ learning. 

ACKNOWLEDGEMENTS  

This research was funded by the National Science Foundation grant number 1933698. Any opinions, findings, 

conclusions, or recommendations expressed in this material are those of the authors and do not necessarily 

reflect the views of the National Science Foundation.   

We thank our participating teachers and students, as well as project advisors who have provided valuable 

feedback.  

We also wish to acknowledge and thank our collaborators at WGBH Education, who developed the app and 

teacher’s guide, which can be accessed by visiting https://first8studios.org/ [Accessed on 21 February, 2022].  

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