Graph as You Go: Practitioner-Friendly Data Collection and Analysis

Abstract

Ms. Parker is in her first year of teaching. She is excited to set up her K–2 learning, interpersonal, functional, and emotional—or LIFE—skills classroom but finds the data collection she knows she will need to maintain a little daunting. How will she collect and use data on all of her students’ goals and keep it up throughout the year? She remembers her mentor teacher referencing data collection as one of the most challenging parts of her job. Ms. Parker knows ongoing assessment of her students is important but is unsure of how to get started.

Data collection and analysis is a critical skill for all teachers, especially those who provide special education services. In fact, collecting and reporting data on individualized education program (IEP) goals is cited in federal law (Individuals with Disabilities Education Act, 2004), and using data to inform instruction is one of the Council for Exceptional Children’s high-leverage practices, or HLPs (McLeskey et al., 2017). The collection and use of data allows teachers to create individualized goals for students, monitor progress of learning, and adjust instruction as needed to support student needs (McLeskey et al., 2017). However, many teachers cite additional paperwork and nonteaching responsibilities, such as data collection, as a major barrier to special education teacher retention and work satisfaction (Billingsley & Bettini, 2019). Furthermore, the demands of the job may limit time and training for data collection, graphing, and decision-making.

Vignette 1: Formative Assessment and Graph as You Go

Formative assessment encompasses regular monitoring of student performance and adapting instructional practices based on assessment outcomes, serving as an inductive approach to student instruction (Fuchs & Fuchs, 1985). Graph as You Go is a simple, teacher-created formative assessment that allows teachers to collect data and analyze it regularly. Through the nature of the data sheet, the data is graphed automatically in each observation to visually represent the change in count of correct responses per session. Decades of research cite the benefits of systematic formative assessment on academic achievement and student impact. A meta-analysis on the effects of systematic formative assessment on academic achievement found that such formative evaluation procedures positively impacted students’ academic achievement (Fuchs & Fuchs, 1985). More recent research has confirmed and extended these findings, indicating that formative assessment supports student achievement across content areas such as literacy, mathematics, and science; is effective when implemented by the teacher or as self-monitoring; and is found to be effective across technology- and paper-based formats and standardized and teacher-created assessments (Lee et al., 2020; Yao et al., 2024).

“Although data collection has been noted as a challenge for teachers (Billingsley & Bettini, 2019), the simplicity of the data sheet allows teachers to easily contribute and analyze performance data as a source of information for this task.”

“Perhaps most importantly, the Graph As You Go data sheet creates a clear visual pattern to determine which targets or steps are most often missed by the student.”

Given the impact of systematic formative assessment on student achievement, teachers should strive to incorporate the assessment-focused HLPs in daily practice. Using a Graph as You Go data sheet can help achieve this. For example, HLP 4 calls for various sources of data when evaluating a student’s strengths and needs (McLeskey et al., 2017). Although data collection has been noted as a challenge for teachers (Billingsley & Bettini, 2019), the simplicity of the data sheet allows teachers to easily contribute and analyze performance data as a source of information for this task. Furthermore, the ease of this assessment tool and its visual production make it accessible to others, and it may serve as a helpful tool in communicating progress with other stakeholders (HLP 5; McLeskey et al., 2017). Perhaps most importantly, the Graph as You Go data sheet creates a clear visual pattern to determine which targets or steps are most often missed by the student. This feature allows for immediate visual analysis of the data, which can be used by both the instructor and the students themselves, practices that have been shown to improve performance (Popham et al., 2018). Reviewing the data for patterns allows the teacher to make data-driven instruction decisions and adjust their approach in a way that addresses the commonly missed targets, skills, or steps. HLP 6 dictates that teachers should use student assessment data to analyze and adjust instructional practices to improve student outcomes, a charge that is made simpler with the Graph as You Go data sheet (McLeskey et al., 2017). The benefits of Graph as You Go are outlined further in the following scenarios.

Graph as You Go is an appropriate data collection method for goals with discrete responses, such as identifying specific targets, and more complex skills that can be broken down into a task analysis. Furthermore, Graph as You Go is helpful in collecting data on preacademic, academic, and functional skills. This data collection sheet is well suited for most goals for which teachers report “percent correct,” either of a number of targets taught (e.g., identifying sight words) or of the steps of a skill (e.g., solving a complex math problem). See Table 1 for examples of goals for which Graph as You Go is appropriate. Practitioners can follow the following seven steps to prepare, implement, and analyze Graph as You Go data sheets in their classroom.

Table 1

Examples of skills for which Graph as You Go is appropriate

Content area	Discrete skills	Complex (task-analyzed) skillsSteps to/of . . .
Math	Match/identify numbers Match/identify math vocabulary Identify shapes	Calculate degrees of angles Solve algebraic equations Solve word problems
English, language arts, and reading	Match/identify letters Match/identify letter sounds Identify sight words Identify parts of a sentence	Write in complete sentences Identify purpose of text Outline a paper
Social studies	Identify states Identify countries Identify social studies vocabulary Order events in a timeline	Using a map Analyze founding documents/treaties
Science	Identify parts of a cell Sort materials by category Match/identify science vocabulary Identify elements	Implement scientific investigations Using a microscope Conducting observations of environments
Social skills	Identify vocations/workers Identify emotions/facial expressions	Engage in conversation Collaborating with peers
Daily living skills	Match/identify colors Identify community signs Identify community services Identify methods of transportation Identify common household objects	Food preparation Washing laundry Washing dishes Hygiene skills Taking the bus
Vocational skills	Greeting customers Identify or count money Formatting professional emails Identify job-specific items or words	Welding Video editing Food preparation (commercial) Cardiopulmonary resuscitation Taking or stocking inventory

Step 1: Determine the Skill Targeted for Instruction

The first step of using the Graph as You Go data collection method is to determine what skill to monitor. The Graph as You Go data sheet is best suited for data that are reported as “correct or incorrect” or those whose mastery criterion is “X% correct.” This lends itself to two particular types of skills: discrete skills in which the goal is to master several targets of that skill (e.g., letter sound identification, sight word identification, or color identification) and complex skills in which a student works to master individual steps of the skill (e.g., brushing teeth, a job task in a vocational setting, or a complex algebraic problem). Teachers may reference mastery criteria of IEP goals or consider the nature of the target skills, to determine those which may be appropriately assessed through Graph as You Go.

As she goes through her students’ IEPs, Ms. Parker notices several have goals of identifying colors and independently washing their hands. Both goals have mastery criteria of “percent correct,” and she remembers learning about the Graph as You Go data sheet and decides to give it a shot.

Step 2: Prepare the Data Sheet

Once a target skill has been identified, prepare the Graph as You Go data sheet. At the top of the data sheet, include the direction to the student. This could be the vocal direction (e.g., “What letter?”) or the natural change in the environment (e.g., flushing the toilet) that should signal the student to initiate the target response (e.g., stating the letter or washing their hands, respectively). The “Directions” section is intended for the practitioner who will be teaching the skill and collecting data with the student. Indicate under “Directions” what steps instructors should take as they work with the student, what it looks like for the student to perform the skill correctly, what the long-term goal is for the student, and how they record student responses in the data. See Figure 1 for a description of each of these components.

Figure 1

Description of the components of a blank Graph as You Go data collection sheet

First, Ms. Parker sets up the Graph as You Go data sheet for the colors goal. A goal for many of her students is to expressively identify colors (e.g., say it out loud when presented with the color). For these students, she completes the top of the data sheet as seen in Figure 3, with the student direction of “What color” and directions for the instructor that the students should say the color that corresponds with the item (see Figure 3, Step 1). Next, Ms. Parker sets up the rest of the data sheet. Because this is a brand new skill for many of the students, she decides to start with just eight colors and focuses on those the student comes into contact with most frequently (see Figure 3, Step 2).

Step 3: Incorporate the Data Sheet Into Assessment or Instruction

The Graph as You Go data sheet can be used during an assessment session, when a teacher presents targets and evaluates performance specifically for the purpose of recording data. When used in this way, present the materials, directions, or task to the student and record each response or step as noted in Step 4. Assessment sessions like these are often conducted in a 1:1 or small-group setting, where the students have an opportunity to perform each target or step once. Data are then collected on those responses. However, the Graph as You Go data sheet may also be used when data collection is embedded into a lesson or naturalistic teaching procedures are used throughout the day. In this way, the teacher would mark a student’s first response or attempt at a task step that day or session as it occurs and target remaining opportunities for teaching trials. Embedding Graph as You Go as formative assessment in a naturalistic setting or lesson may limit coding responses to one per target each day, but it could also facilitate the evaluation of the target skill in a real-world context.

“Embedding Graph As You Go as formative assessment within a naturalistic setting or lesson may limit coding responses to one per target each day, but it could also facilitate the evaluation of the target skill within a real-world context.”

Step 4: Collect Data

To use the data sheet, each column of the data sheet will be used for one instruction or assessment session. For example, if a teacher works on sight words with a student on Monday, Tuesday, and Thursday, they will complete one column on Monday, the next on Tuesday, and a third on Thursday. Each day of data collection is indicated by the initials and date below the corresponding column of data.

Recording correct responses. Once the session’s data collection column has been identified, begin collecting data on correct and incorrect responses. Indicate all correct responses (correct answers given or correct steps of a skill performed) by circling the number next to that target or step.

Recording incorrect or nonresponses. Indicate incorrect responses (incorrect or no answers given or the step of a skill missed or performed with errors) with a single slash over the number next to that target or step. By the end of that instructional session or assessment, each number in the day’s column should either be circled or slashed, indicating correct or incorrect responses on all active targets or steps of the skill.

Record the data point. At the conclusion of the lesson or assessment, count the number of correct responses, indicated with the open circle over the number. Mark a data point—a closed circle—over the number in that column that corresponds with the number of correct responses. The data point should be marked so that the open circle or slash already over that number, indicating a correct or incorrect response for the corresponding target, is still visible under the data point. See Figure 2 as an example.

In the first 2 weeks of school, Ms. Parker collects some initial data to assess the students’ baseline knowledge of colors. During work time, she brings several monochromatic preferred items to a quiet place in the classroom to work with Dasha. As they engage with the different items, Ms. Parker incorporates the question “What color is this?” and records Dasha’s correct or incorrect responses on the data sheet. Her first day of data collection can be seen in see Figure 3, Step 3. After another day of baseline data, Ms. Parker starts incorporating instruction on the target colors across the day. As they pass signs or play with toys, Ms. Parker discretely inserts instruction across natural times in the day, modeling the identification of colors and prompting Dasha to imitate.

Figure 2

Description of the components of a partially completed Graph as You Go data sheet

Step 5: Connect Data Points and Analyze

Once the teacher has completed data collection and data points in two or more columns, the data points should be connected with a straight line. This will allow instructors to see an increasing or decreasing trend as the target skill is acquired, lost, or stagnant. See Figure 2 for an example of this connecting line. Over time, the data will allow monitoring of the student’s progress and may reveal patterns, such as the following.

Patterns in response errors. If one or several targets or steps of the skill are regularly missed, the pattern revealed in the data can allow the instructor to focus intervention on those steps. For example, if a student regularly misses both “stop” and “soup,” they may be focusing on the first and last letters only. Noticing this pattern can prompt the teacher to work with the student on attending to all letters in the words before reading it by providing an attentional cue.

Patterns in staff use. If different staff members are using the same data sheet to collect data, recording initials may reveal patterns over time related to specific staff members. For example, a student may work better with one staff than another, resulting in a discrepancy in accurate responding across instructors. Or staff members may be recording the data in different ways (e.g., circling for correct responses with a prompt vs. circling for responses that are both correct and independent). Data should be monitored for such patterns around staff to make changes related to fidelity or promoting generalization of skills.

Ms. Parker continues to collect data for color identification on Dasha’s data sheet, connecting the data points and analyzing the data. One pattern begins to emerge in her data—the colors purple and blue are the most frequently missed colors. Ms. Parker takes a look at her instructional materials and realizes the shades of purple and blue may be too close to each other, making it more difficult for Dasha to discriminate between the two during this acquisition period. She switches out the materials for some different shades and resumes instruction. That was a big help, and Dasha started mastering those colors as well! See Figure 3, Step 4.

Figure 3

Visual representations of Steps 1 through 5 of using a Graph as You Go data sheet for Vignette 1

Step 6: Share Data and Outcomes

After data are collected, the Graph as You Go data sheet can be used to easily share the collected data and outcomes with the student, families, and education professionals who also support the student. Teachers may use the data sheet to bring students into the assessment and analysis process, inviting them to look at the data together or allow them to add the data point after each assessment. When sharing with families, the format of the Graph as You Go data sheet allows families to easily see precisely which targets or steps of a skill may need further support and could be targeted at home. Finally, the graph and percentages can be used by teachers and specialists when monitoring progress on IEP goals or present levels, comparing performance levels with goals and collaborating on possible adjustments to instruction.

While collecting data, Ms. Parker observed Dasha noticing the Graph as You Go data sheet. She began allowing Dasha to help her make the data point at the end of each assessment session, supporting her in making the data point with a marker. After the first couple of days of instruction, Ms. Parker sent a picture of the data sheet home to Dasha’s dad. With the picture, she briefly shared an update that Dasha was quickly learning the colors red, green, yellow, and black. She also noted that Dasha seemed to especially struggle with purple and blue. Dasha’s dad responded that he would start pointing out those colors at home.

Step 7: Adapt the Data Sheet or Process as Needed

Teachers using the data sheet may find several adaptations helpful in using and reporting the data it produces. The following are several examples of how to adapt the Graph as You Go data sheet:

Highlight the “mastery” row: If the targeted goal has a specific mastery criteria (e.g., “80% correct”), calculate which row of the data sheet would represent mastering that percentage of the targets (in the case of 80%, mastering 16 of 20 steps would meet mastery). Then, it may be helpful to include shading or highlighting on that row for easy determination of mastery.

Prompting: If a specific type of prompting is used with a student, such as the system of least prompts, include a space at the top of the data sheet to indicate what prompting strategy or level of prompting to use with the student on that particular skill. For example, if a teacher uses modeling in a constant time delay strategy (Wolery et al., 1992) when teaching colors, this may be indicated at the top of the data sheet along with the delay interval. Note that if any prompting is used, a slash (“/”) should be placed across the corresponding number if the skill or target was not performed independently.

Include the IEP goal or lesson objective: When space permits, instructors may also choose to include the specific language of an IEP goal or lesson objective at the top of the Graph as You Go data sheet. Including the specific language may serve as a helpful reminder to the specific expectations of the instruction or data collection and help monitor progress towards the goal.

Use for self-monitoring: One variation of this formative assessment process is to include students in the data collection and analysis process or to use the data sheet as a self-monitoring tool. Research supports self-monitoring as a tool to support student achievement across academic skills and learning behavior, particularly when self-recording is part of the process (Davis et al., 2016; Prater et al., 1992). As a self-monitoring tool, the Graph as You Go data sheet could be used with students to help them set goals (e.g., a percentage or number of correct steps or responses) and monitor their progress toward that goal with visuals through the graphing feature. Furthermore, for more complex skills with steps, the Graph as You Go data sheet could also serve as a visual prompt for the student, enhancing independence by fading reliance on teachers or peers (McConomy et al., 2021).

At the end of the first 6 weeks, Ms. Parker has progress reports due. When she began reporting on the different goals, she realized her Graph as You Go data sheets showed her how many questions students got right, but she needed to report percentage correct. To make the next progress monitoring reporting period easier, she decided to add a row for “percent correct” so she can report the progress toward mastery more quickly while writing reports (see Figure 3, Step 5).

Fuchs et al. (2017) stated that “individualization is a signature feature of special education. A validated process for individualizing intervention is data-based individualization” (p. 41). The Graph as You Go assessment method is a data collection process that enables teachers to do just that in a way that reveals patterns suggesting individual student instructional needs. Using Graph as You Go, teachers can use data to individualize intervention, detect barriers, communicate progress, and even bring students into the process.

Vignette 2: Secondary

Mr. Alvarez teaches high school math in resource and inclusion settings. This semester, he is working with Sam, who has really struggled with quadratic functions and equations. He decides to use a Graph as You Go data sheet to monitor Sam’s progress and understand which steps might be holding up their skill acquisition. First, Mr. Alvarez sets up the data sheet by writing the directions (see Figure 4, Step 1).

Figure 4

Visual representations of Steps 1 through 4 of using a Graph as You Go data sheet for Vignette 2

Next, Mr. Alvarez task analyzes the steps of solving a quadratic equation. He works with Sam’s math teacher to ensure the steps he writes for Sam align with how the teacher provides instruction for this skill. He uses the task analysis—the steps of solving a quadratic equation—to set up the rest of the data sheet (see Figure 4, Step 2). Over the next week, Mr. Alvarez takes 3 days of baseline data so he can determine which steps are giving Sam the most trouble. He notices that Sam is struggling with many of the steps but especially the first several steps. Mr. Alvarez focuses his instruction on those steps, increasing Sam’s opportunities to respond by creating some problems and activities that allow Sam to practice those steps more. It is working! Sam’s performance improves, and he notices an increase in confidence as Sam begins to master the steps (see Figure 4, Step 3).

“Using Graph As You Go, teachers can use data to individualize intervention, detect barriers, communicate progress, and even bring students into the process.”

The next month, Mr. Alvarez sends the data sheet to Sam’s co-taught math classroom to see how this skill transitions out of the resource class. When he gets the data, he notices that when one of the co-teachers, Mr. Avery, collects that data, Sam gets all of the steps correct. However, when the other teacher, Mrs. Rodriguez, collects data, Sam gets more of the steps wrong. Using this pattern in the data collection to address the issue, Mr. Alvarez meets with the two math teachers to review how they are using the data sheet. He learns that Mr. Avery was marking “correct” when Sam responded after receiving help or prompting and that Mrs. Rodriguez was only marking “correct” responses when Sam performed the step independently. He reviews the data collection system with both of them, practices for a round of data collection, and notices a change in the data collection process moving forward. This allows Mr. Alvarez to pinpoint the final steps with which Sam is struggling (see Figure 4, Step 4).

Supplemental Material

sj-docx-1-tcx-10.1177_00400599251346716 – Supplemental material for Graph as You Go: Practitioner-Friendly Data Collection and Analysis

Supplemental material, sj-docx-1-tcx-10.1177_00400599251346716 for Graph as You Go: Practitioner-Friendly Data Collection and Analysis by Erinn E. Whiteside in TEACHING Exceptional Children

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Erinn E. Whiteside

Supplemental Material

Supplemental material for this article is available online.

Erinn E. Whiteside, Texas A&M University, College Station, TX

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