How do you use data to monitor and evaluate the impact of your decisions and actions?
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Before you collect and analyze data, you need to define your purpose. What are the questions you want to answer, the problems you want to solve, or the goals you want to achieve? Your purpose should be clear, specific, and relevant to your context and stakeholders. For example, you might want to know how your students are performing in a certain subject, how your staff are engaging with a new initiative, or how your parents are satisfied with your communication. Your purpose will guide your data selection, collection, and analysis methods.
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The purpose for collecting data is to drive change and/or processes. Collecting data for the sake of collecting a defeats the purpose. Yes, the research questions define the purpose. But, what historical data already exists? Is there data that is already available to respond to the new purpose? What actions were taken to implement or to act on this data? What were the results? How will this new question generate new evidence that targets the new purpose? These questions need to be address within the logic model and the statistical framework.
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What must I learn about atoms if I am to reasonably understand chemical equations? Assess for success! Clearly give the student a vivid picture that is honest, goal-directed, specific, positive, and suggests how to move forward and upward. Knowing that Billy is in the 65th percentile on a standardized test is meaningful when used to develop student group progress reports, but it is a group measure and lacks the rich benefits of more individualized feedback. The student only learns "where she/he stands" and not "so what? where next? how?"
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When working in collaboration, getting this jointly owned aim/ purpose is key. Considering the influence (positive and negative) of other players in the system beyond your own makes it difficult to own any impact data singularly.
Once you have defined your purpose, you need to select your data sources. Data sources are the types and sources of information that can help you answer your questions, solve your problems, or achieve your goals. Data sources can be quantitative or qualitative, and can come from various sources, such as assessments, surveys, observations, interviews, focus groups, documents, or artifacts. You should select data sources that are valid, reliable, relevant, and diverse. For example, you might use standardized test scores, student feedback, teacher portfolios, and peer observations to measure student learning and teacher effectiveness.
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It is also important to consider who is not represented in the data that was previously collected. For example, people from lower income households may not be proportionally represented in survey data. This is a form of selection bias that can skew results and lead to faulty conclusions.
After you have selected your data sources, you need to collect and organize your data. Data collection is the process of gathering and recording the information from your data sources. Data organization is the process of sorting, categorizing, and labeling the information for easy access and analysis. You should collect and organize your data in a systematic, ethical, and timely manner. For example, you might use online tools, spreadsheets, databases, or software to collect and organize your data.
Once you have collected and organized your data, you need to analyze and interpret your data. Data analysis is the process of examining, summarizing, and describing the information from your data sources. Data interpretation is the process of explaining, understanding, and drawing conclusions from the information. You should analyze and interpret your data in a rigorous, objective, and transparent manner. For example, you might use descriptive statistics, graphs, charts, tables, or narratives to analyze and interpret your data.
After you have analyzed and interpreted your data, you need to communicate and share your findings. Data communication is the process of presenting, reporting, and disseminating the information from your data sources. Data sharing is the process of making the information accessible, understandable, and usable for your stakeholders. You should communicate and share your findings in a clear, concise, and compelling manner. For example, you might use presentations, reports, newsletters, blogs, or podcasts to communicate and share your findings.
Finally, after you have communicated and shared your findings, you need to act on your implications. Data implications are the actions, recommendations, or suggestions that arise from the information from your data sources. Data action is the process of implementing, evaluating, and adjusting the actions, recommendations, or suggestions based on the information. You should act on your implications in a responsive, collaborative, and reflective manner. For example, you might use action plans, feedback loops, professional development, or policy changes to act on your implications.
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One way to do this is by analyzing the data to determine what areas need improvement. For example, suppose the data shows that a specific group of students is struggling with a particular subject. In that case, providing additional resources and support in that area may be necessary. Additionally, it is important to communicate the data findings to teachers, students, and parents so that everyone knows any sites that need improvement. By working together and using data to guide decision-making, education leaders can help ensure that all students have the opportunity to succeed.
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It's important to not simply act on implications of data, but to use future data collection and analysis to test the hypothesis you originally made. This means that data collection is also an accountability mechanism for your decisions. Remember that most, if not all decisions are provisional - a leader should reserve the right to change course if a solution is not effective.
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It is important that once the data has been collected & analysed it is shared with the pupils in a digestible way. Pupils are inundated with surveys and often don’t hear anything after they have completed them. This leads to lower response rates. However, if pupils are shown what has been done with their data and what it means for them then they are more likely to interact with it in a positive way. I think that today our pupils are so well-informed in so many areas that this has created a thirst for knowledge and understanding that we should seek to satisfy as much as possible. For example with wellbeing data we can start positive conversations with pupils encouraging them to think about how they react and deal with a range of situations.
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