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Analyzing Real-Time Data for Student Advocacy

Using real-time data collection and meaningful data visualizations to support student learning growth

Overview

Real-time data provides insights into individual student progress, allowing teachers to personalize student learning by tailoring their instruction (e.g., differentiated small groups, one-to-one instruction) according to each student's needs. By analyzing the data, educators can identify areas where students are achieving their academic goals and where they need additional support.

Additionally, by analyzing student performance data in real time, educators and administrators can identify trends and patterns to make informed decisions about curriculum, instructional practices, resource allocation, and educational policies, leading to more effective, engaging, and equitable educational outcomes.

Real-time data can take the form of either qualitative (stories) or quantitative (numbers). Qualitative data may include discussions with students, student observations, artifacts (e.g., student assignments), interviews, or focus groups. Quantitative data might include numerical values like test scores or state assessment results.

Analyzing qualitative data should involve the following steps:

  1. Familiarize yourself with the data. With qualitative data, read and listen to the student responses or transcriptions multiple times to gain a deep understanding of the content. Take notes and highlight key themes, ideas, or patterns that emerge.

  2. Organize and code the data. Create a coding system to categorize the data. Assign labels or codes to segments of text that represent different themes or concepts. You can use a spreadsheet, qualitative data analysis software (e.g., NVivoi), or even pen and paper for this task. Be flexible and open to adjusting your codes as you progress through the analysis.

  3. Identify patterns and themes. Once you have coded the data, look for common patterns, themes, or trends across the responses. Group related codes together and identify overarching themes or categories that emerge from the data. This process involves comparing and contrasting different responses to uncover both similarities and differences.

  4. Provide descriptive summaries. Develop descriptive summaries for each theme or category. Describe the key findings and provide illustrative quotes or examples from the data to support your conclusions. This helps to bring the analysis to life and adds credibility to your interpretations.

  5. Interpret and analyze the data. Move beyond the descriptive level and engage in deeper analysis. Explore the underlying meanings, connections, and relationships between the themes. Consider the context in which the data was collected and any relevant theories or frameworks that may inform your analysis. Look for explanations or reasons behind the patterns you have identified.

  6. Triangulate and validate the findings. Consider using multiple sources of data (e.g., interviews, observations, and documents) to validate and corroborate your findings. Triangulation helps to enhance the credibility and trustworthiness of your analysis by ensuring that the results are not solely based on one source.

  7. Reflect and interpret with context. Reflect on the implications of your findings within your broader educational context. Consider the implications for policy, practice, or future research, and reflect on the limitations of your study and areas for further investigation.

Quantitative analysis should examine formative changes over time to gain a better understanding of how students have progressed. One option for quantitative analysis is to examine the change in average scores over time. To do this:

  1. Use a selected spreadsheet or statistical package to calculate the average score for each group and/or subgroup at each point in time for which you have data. (This Google Sheet provides an example in the “Sample Analysis” tab.)

  2. Calculate the average percentage of change between each assessment window. Particularly when comparing growth patterns across different assessment types, percentages between scores can be useful for understanding progress. You may also consider creating and analyzing a frequency table. For each group or subgroup in your sample, a frequency table displays the average score (mean and median), the total number of responses, the standard deviation, as well as the minimum and maximum scores. This can be accomplished using a statistical package or the FREQUENCY command in Google Sheets.

Now that you have analyzed quantitative and/or qualitative data for the purpose of advocating for students, it is important to remember that data analysis should involve an iterative process and help inform decisions to strengthen student progress and achievement. After analyzing data and drawing conclusions, new questions may arise, leading to further data collection, analysis, and refinement of instructional practices and student supports for continuous improvement. As such, using data analysis to advocate for students should involve a continuous process to capture trends in their learning and make adaptations based on new insights.

We also recommend exploring the Making Sense of Your Data Findings for Advocacy strategy card.