# data[].attributes.body
My biggest take away from this class is a constant reminder that data can and should be used to thoughtfully inform instruction. This class opened my eyes to different ways I can utilize data to create an action plan as well as how to read, interpret, analyze, and display larger sets of data. Although I plan to be a classroom teacher for a while longer, this information will be extremely beneficial to me in the future as I pursue a reading specialist or instructional coach position.

# data[].attributes.body
My biggest takeaway from the course is that often teachers have a "gut feeling" about what they see in their practice, or why they think certain things are occurring with student learning, however sometimes need the scaffolding and support to communicate these hunches in a data-informed manner. For example, the Travers article where the teacher's quantitative data supported his observations about classroom participation, though he just needed a touch of support to translate the information in front of him.

# data[].attributes.body
I learned so much in this course about how to better use data to drive my instruction and how to support teachers as a coach in order to use data to drive instruction. Collaboration is the biggest take away for me. I feel lucky that I got to collaborate with a great team in this class.

I hope everyone has a great summer!

# data[].attributes.body
My most important takeaway from this EDR 604 course is the idea that a student-centered instructional plan is key to success with their academic growth. When a student is approached with proper support and instructional strategies in place of their action plan, they can meet proper goals. Collaborating with families on their child's assessment data can provide a supportive cycle for the students growth.

# data[].attributes.body
My biggest takeaway was the importance of building a story or a picture of student achievement through data. Data is collected on students constantly through the year, and without a team of educators using that data effectively, valuable time is being wasted both on the assessments and on teachers not working to close achievement gaps. Data teams need to be able to analyze what patterns are shown, what this means for the students, and - most importantly - what will be changed in order to support students in the best way.

# data[].attributes.body
I have had several meaningful takeaways from this course. I think my biggest takeaway is the importance of communication between teachers, support staff, administrators, and even families. This class has taught me many ways for how to improve my PLC meetings and make them more meaningful next year. The structure of PLC meetings is something I would like to look at and change, as this class and Data Wise have taught me many strategies to conduct meetings and make informed decisions. I also have developed a better understanding of how I can more effectively communicate with parents about data. I am excited to apply all I have learned into my classroom next year.

# data[].attributes.body
My biggest takeaway from the course is that meaningful data analysis requires looking at multiple sources of evidence. While working on the projects, we analyzed student data and reflected on how it could inform instruction, which was helpful. These experiences helped me better understand the purpose of assessment, the importance of a data-friendly culture, and how ongoing analysis can support continuous improvement. I feel I have a stronger understanding of how to interpret data and use it to make informed instructional decisions that support student learning and growth.

# data[].attributes.body
My biggest takeaway from this course was the movement towards a mindset of collective inquiry around data. We all look at (probably a lot of) data every day, week, and month of each school year, but often teachers don't really know what it means or how they can effectively use it to inform instruction. As a future instructional coach I can work to support my teachers in asking the right questions to understand the information they have collected, and to use data wise practices to find the answers and make the changes! Collaboration and inquiry need to be at the center of anything that we do with data.

Thank you all for the collective learning! Enjoy your summer.

# data[].attributes.body
My biggest takeaway from the class is that data not only needs to be shared with school members, but also with the families and the community. I really enjoyed getting to see how local school district share this data. This course reminding me of the importance of making learning and progress visible to many and celebrating success.

# data[].attributes.body
One of the biggest takeaways from this course is that this work cannot be done alone. Reflecting on our journey through the Data Wise framework, true instructional improvement relies entirely on collaborative teamwork. When harnessed properly, collective teacher collaboration is our absolute greatest asset. However, for that collaboration to be positive, effective, and sustainable, we must have structured protocols and clear processes in place to guide our conversations and keep the focus where it belongs. Alongside collaboration, another major takeaway that will stick with me moving forward is that objective evidence, rather than assumptions or habit, must remain the central focus when having data-informed conversations and letting action plans take shape. Finally, the course reinforced that data is truly multi-faceted, meaning effective decision-making requires looking beyond a single test score and referencing various and multiple sources of data to get a complete, holistic picture of student learning.

# data[].attributes.body
One of the most important takeaways from this course is that data analysis can and should be a collaborative process that involves a team of stakeholders working together to use the information to adjust instructional practices and support student learning. We can use protocols to make this a smooth process that can be replicated again and again with different data. Over time, our teams will learn to communicate and collaborate, enabling them to get to the heart of the matter and identify areas for change that will strongly impact teaching and learning. By using the Data Wise process, we can ensure continuous positive change. Moreover, we can communicate our assessment data, growth, and goals in a way that will enable all stakeholders in the community to understand and support our students.

Have a great summer, everyone!

# data[].attributes.body
My biggest takeaway from this course is the the importance of moving beyond simply collecting data to engaging in collaborative inquiry around what data means. According to the Data Wise process, meaningful school improvement occurs when educators work together to identify learner-centered problems and use relentless focus on evidence to guide instructional action. Also, strengthening our understanding of data literacy should not be limited to only educators, but should also include students and their families. This process reinforces the value of data as a tool for continuous improvement, rather than just accountability. I am looking forward to sharing some of the protocols from this course with my school's data team to identify those we already use and others we can put into practice to promote this collaboration!

# data[].attributes.body
My biggest takeaway is the importance of collaboration between teachers and the consistent use of evidence to support instruction. Though I know it's best practice to collaborate with teachers, it is hard to find time to make this a consistent practice. However, next year I'm looking forward to carving out the time needed to work with my team of teachers. Also, because I know more now about how to interpret and analyze assessment data, I'm excited to take a closer look at our PSSA data to determine the first steps for instruction.

# data[].attributes.body
My biggest takeaway from this course is the importance of how data is communicated. For teachers, data should be presented in a way that makes it easy to analyze, identify patterns, and recognize trends. When sharing data with students, it should be framed with a mastery-oriented focus that emphasizes growth, learning, and progress rather than performance alone. Finally, when communicating with parents, data should be explained clearly so they understand its meaning and can partner with the school to support student achievement and academic growth.

# data[].attributes.body
My biggest takeaway from this course is the importance of how data is communicated. For teachers, data should be presented in a way that makes it easy to analyze, identify patterns, and recognize trends. When sharing data with students, it should be framed with a mastery-oriented focus that emphasizes growth, learning, and progress rather than performance alone. Finally, when communicating with parents, data should be explained clearly so they understand its meaning and can partner with the school to support student achievement and academic growth.

# data[].attributes.body
One of my biggest takeaways from this course is that data should be used to improve learning, not just measure it. I've learned that the most important part of the Data Wise process is looking beyond scores to identify student needs and develop action plans that support growth. This course has also reinforced the importance of communicating data clearly with families and colleagues so it can be used as a tool for collaboration and continuous improvement.

# data[].attributes.body
One of the biggest takeaways from this course for me was the importance of communicating achievement data to all stakeholders. Collecting and analyzing data is important, but it doesn't do much good if people don't understand what the data is telling us. Teachers, administrators, families, and students all benefit from having a shared understanding of what is going well and where improvements are needed. When everyone is on the same page, it is easier to work toward common goals and make informed decisions. It is important to help teachers understand and use data to guide instruction while also communicating student progress in ways that families can easily understand. This course reinforced for me that data should be used as a tool for conversation, collaboration, and continuous improvement rather than simply something that gets collected and reported.

# data[].attributes.body
My biggest takeaway from this five week course is that assessment is only valuable when it leads to action. Data is not just something to collect, it should help identify patterns in student learning and guide specific changes in instruction. I also learned that real improvement happens through a continuous cycle of analyzing data, identifying a problem of practice, and adjusting teaching in response.

# data[].attributes.body
My biggest takeaway from this course is that data is most powerful when educators can truly understand it. Collecting and analyzing assessment results is important, but the real impact comes from using that information to adjust instruction, support students, and communicate effectively with families and colleagues. I also learned that achievement data only tells part of the story. Growth data and ongoing formative assessments provide a much more complete picture of student learning.

# data[].attributes.body
My biggest takeaway is that data is most powerful when it is used to improve instruction and support student growth, not just measure performance. The Data Wise process showed me the importance of using multiple data sources, collaborating with colleagues, and focusing on student learning needs. I also learned that involving students in understanding their progress helps build ownership and motivation.

# data[].attributes.body
Students are much more than one data point. While assessments provide valuable information about students strengths and areas of need, we should consider observations, student work, and conversations to evaluate the whole child. In doing so, we are able to make instructional decisions that support growth and celebrate progress.

# data[].attributes.body
Just because we have a lot of data and are having meetings, doesn't make us "Data Wise". We need to be implementing the full process start to finish, to ensure we are making data driven decisions, that support student growth and full mastery of content.