Standard 2.8 - Data Analysis
Candidates model and facilitate the effective use of digital tools and resources to systematically collect and analyze student achievement data, interpret results, communicate findings, and implement appropriate interventions to improve instructional practice and maximize student learning. (ISTE 2h)
Artifact - 4th Grade Science Data Review
Reflection
Data analysis is an important responsibility of a school leader. Good data analysis can guide school improvement through focusing instruction on student learning weaknesses. It can guide efforts in planning professional learning that is more effective and rewarding. However, analyzing the data is only part of puzzle. For real impact, school leaders need to be able to present their data in a format that is both interesting and understandable to diverse stakeholders. My presentation overview of student performance on 4th Grade Science CRCT represents my ability to model and facilitate the use of digital tools and resources to collect, analyze and interpret student achievement data, and communicate that information to teachers and other stakeholders.
I selected this area for data analysis because our School Improvement Plan described improving science achievement as one of our learning goals. In preparation for this project, it was necessary to learn different levels of data analysis, which is sometimes limited by the data available. As I began to collect the CRCT results from the Georgia Department of Education and Governor’s Office of Student Achievement for the past three years and delve deeper into our schools CRCT results, I realized that it is not possible to disaggregate state data by racial or ethnic group, nor by English language learners because our school populations are too small to be counted in state reports. Even though our school has diverse enrollment, it is often defined by factors that are not measured on Georgia’s annual assessments. Therefore, as I analyzed the data, I decided to focus on differences in test scores between girls and boys. I used Excel to help me interpret the data into formats and table that easily and clearly communicate the information to teachers and administrators at my school. My final narrated PowerPoint presentation communicates what differences exist between the groups, explaining the differences and offering ideas for appropriate interventions.
The first step in data analysis is to make predictions without reviewing the data. I predicted that girls at our school would have higher science test scores. My reasoning was based on the fact that in general, girls perform better on standardized tests than boys. I also inferred that this may be the case because boys often need more hands-on active learning instruction, and hands-on science experimentation has been an area in which our school has struggled. It is also sometimes difficult to translate hands-on experience into standardized test performance.
As I completed this presentation, I learned to think deeply not only about the best way to communicate the data but also about ways to stimulate discussion around the data. Past data analyses have focused simply on presenting a series of graphs and tables with brief explanatory remarks. However, to effectively communicate with stakeholders, it is necessary to draw deeper comparisons while keeping the data simple and clear. To make this presentation more effective, I would like to collect and analyze student-specific and item-specific data for science CRCTs. That level of analysis would yield real results in terms of being able to identify specific groups of students, such as girls who are English-language learners but not students with disabilities, who might benefit from targeted physical science instruction.
This presentation will impact school improvement and professional learning because my school can use the information to inform planning for professional development to improve science instruction to a specific group of students. Effective professional development will change teaching styles and student science learning, as measured by CRCT results as well as the results of unit and benchmark tests.
Data analysis is an important responsibility of a school leader. Good data analysis can guide school improvement through focusing instruction on student learning weaknesses. It can guide efforts in planning professional learning that is more effective and rewarding. However, analyzing the data is only part of puzzle. For real impact, school leaders need to be able to present their data in a format that is both interesting and understandable to diverse stakeholders. My presentation overview of student performance on 4th Grade Science CRCT represents my ability to model and facilitate the use of digital tools and resources to collect, analyze and interpret student achievement data, and communicate that information to teachers and other stakeholders.
I selected this area for data analysis because our School Improvement Plan described improving science achievement as one of our learning goals. In preparation for this project, it was necessary to learn different levels of data analysis, which is sometimes limited by the data available. As I began to collect the CRCT results from the Georgia Department of Education and Governor’s Office of Student Achievement for the past three years and delve deeper into our schools CRCT results, I realized that it is not possible to disaggregate state data by racial or ethnic group, nor by English language learners because our school populations are too small to be counted in state reports. Even though our school has diverse enrollment, it is often defined by factors that are not measured on Georgia’s annual assessments. Therefore, as I analyzed the data, I decided to focus on differences in test scores between girls and boys. I used Excel to help me interpret the data into formats and table that easily and clearly communicate the information to teachers and administrators at my school. My final narrated PowerPoint presentation communicates what differences exist between the groups, explaining the differences and offering ideas for appropriate interventions.
The first step in data analysis is to make predictions without reviewing the data. I predicted that girls at our school would have higher science test scores. My reasoning was based on the fact that in general, girls perform better on standardized tests than boys. I also inferred that this may be the case because boys often need more hands-on active learning instruction, and hands-on science experimentation has been an area in which our school has struggled. It is also sometimes difficult to translate hands-on experience into standardized test performance.
As I completed this presentation, I learned to think deeply not only about the best way to communicate the data but also about ways to stimulate discussion around the data. Past data analyses have focused simply on presenting a series of graphs and tables with brief explanatory remarks. However, to effectively communicate with stakeholders, it is necessary to draw deeper comparisons while keeping the data simple and clear. To make this presentation more effective, I would like to collect and analyze student-specific and item-specific data for science CRCTs. That level of analysis would yield real results in terms of being able to identify specific groups of students, such as girls who are English-language learners but not students with disabilities, who might benefit from targeted physical science instruction.
This presentation will impact school improvement and professional learning because my school can use the information to inform planning for professional development to improve science instruction to a specific group of students. Effective professional development will change teaching styles and student science learning, as measured by CRCT results as well as the results of unit and benchmark tests.