2.8 Data Analysis
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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)
I enjoy working with Excel while trying to understand numbers when they don’t make sense, identify patterns, or even looking at trends over time. This was so strong at one point, that I even created a Data Team Program that our district, among others, currently use for their schools in the data team process. Just before my Data Analysis & School Improvement class (ITEC 7305) was about to begin, I decided that the Excel Data Team Program had reached its limit, and it was time to utilize the power of Google Apps for Education, and create a Google Sheets version of the Excel program.
It took a few weeks to almost complete the Google Sheets, which I refer to as Collaborative Data Teams (CDT). This new program is currently being tested with the entire 8th grade team and each content area has their own copy of this CDT program. It is still in beta form, but for the beginning of the 2016-2017 school year, it will be up-to-date, hopefully bug free, and ready to go as the entire district will be implementing this program instead of the previous Excel version.
The program offers many useful features, but its primary purpose to assist teachers with the collection of data, and then provide an analysis and interpretation of student achievement data. Teachers will need to analyze the data to identify other trends that the file may not identify, but through the multiple filters one could apply, along with looking at student names throughout the entire data team process, the data teams are able to proceed more efficiently. There are a few major steps within the data team process:
• Step 1: Collect and chart pre-assessment data on a standard or element
• Step 2: Analyze data and prioritize student needs (Identify strengths and weaknesses)
• Step 3: Set, review, and revise SMART Goals
• Step 4: Select common instructional & strategies (most collaborative part of this process)
• Step 5: Collect and chart post-assessment data
• Step 6: Develop remediation plan for those students who did not Meet or Exceed the standard
The CDT program that I created runs through each step of the data team process and teachers only need to follow along and contribute to make it successful. Once this occurs, the program automatically creates charts, graphs, and tables of information that is easy to view, understand, and is highly customizable for drilling down to exactly what the teacher is trying to identify. Basically, I created the program to also communicate the findings of each data team pre and post assessment given. As the program collects and organizes the data, the automatically created charts, graphs, and tables communicate clear and concise data and information that will guide the data team members in the decision making process of improving their instructional strategies.
The work that went into creating the artifact not only positively impacts student learning and promotes valuable faculty development opportunities, it also is a tool for school improvement and district improvement.
Student Learning – Teachers meet collaboratively to address student needs based on pre-assessment scores and discuss instructional strategies to guide students through the learning process. Students will continue to learn with a more focused attention to specific instructional strategies to promote the best possible student learning and achievement opportunities.
Faculty Development – Data teams will meet and now be guided through the data team process within the program itself. Not only will the artifact provide each step of a data team process (8 processes actually), but the artifact also promotes an online, collaborative, team building opportunity each time they meet.
School Improvement / District Improvement – As the school continues to work through the data team process each year, teams are becoming more efficient with the process itself. This artifact, which will be used by my school, and all other schools in the Madison County School District, be provide opportunities for all teachers and administrators in the system for the upcoming year and beyond.
Within each data team process, there are two sections that encourage teachers to identify appropriate interventions for implementation to improve the instructional practices while maximizing student learning: The instructional planning section and the remediation plan. These sections allow teachers to post their plans after looking at the student data so that improvements to their teaching can occur. This is the backbone of the entire program which supports the overall purpose of improving student learning and achievement.
The impact of this artifact can be assessed in the growth of student scores within the program itself by looking at pre-assessment scores and post-assessment scores. Teachers will automatically see growth from their students learning based on their collaboratively planned instructional strategies and assessments. Teachers and administrators can also use this document for end of year evaluations for evidence of meeting TKES standards within the school year.
It took a few weeks to almost complete the Google Sheets, which I refer to as Collaborative Data Teams (CDT). This new program is currently being tested with the entire 8th grade team and each content area has their own copy of this CDT program. It is still in beta form, but for the beginning of the 2016-2017 school year, it will be up-to-date, hopefully bug free, and ready to go as the entire district will be implementing this program instead of the previous Excel version.
The program offers many useful features, but its primary purpose to assist teachers with the collection of data, and then provide an analysis and interpretation of student achievement data. Teachers will need to analyze the data to identify other trends that the file may not identify, but through the multiple filters one could apply, along with looking at student names throughout the entire data team process, the data teams are able to proceed more efficiently. There are a few major steps within the data team process:
• Step 1: Collect and chart pre-assessment data on a standard or element
• Step 2: Analyze data and prioritize student needs (Identify strengths and weaknesses)
• Step 3: Set, review, and revise SMART Goals
• Step 4: Select common instructional & strategies (most collaborative part of this process)
• Step 5: Collect and chart post-assessment data
• Step 6: Develop remediation plan for those students who did not Meet or Exceed the standard
The CDT program that I created runs through each step of the data team process and teachers only need to follow along and contribute to make it successful. Once this occurs, the program automatically creates charts, graphs, and tables of information that is easy to view, understand, and is highly customizable for drilling down to exactly what the teacher is trying to identify. Basically, I created the program to also communicate the findings of each data team pre and post assessment given. As the program collects and organizes the data, the automatically created charts, graphs, and tables communicate clear and concise data and information that will guide the data team members in the decision making process of improving their instructional strategies.
The work that went into creating the artifact not only positively impacts student learning and promotes valuable faculty development opportunities, it also is a tool for school improvement and district improvement.
Student Learning – Teachers meet collaboratively to address student needs based on pre-assessment scores and discuss instructional strategies to guide students through the learning process. Students will continue to learn with a more focused attention to specific instructional strategies to promote the best possible student learning and achievement opportunities.
Faculty Development – Data teams will meet and now be guided through the data team process within the program itself. Not only will the artifact provide each step of a data team process (8 processes actually), but the artifact also promotes an online, collaborative, team building opportunity each time they meet.
School Improvement / District Improvement – As the school continues to work through the data team process each year, teams are becoming more efficient with the process itself. This artifact, which will be used by my school, and all other schools in the Madison County School District, be provide opportunities for all teachers and administrators in the system for the upcoming year and beyond.
Within each data team process, there are two sections that encourage teachers to identify appropriate interventions for implementation to improve the instructional practices while maximizing student learning: The instructional planning section and the remediation plan. These sections allow teachers to post their plans after looking at the student data so that improvements to their teaching can occur. This is the backbone of the entire program which supports the overall purpose of improving student learning and achievement.
The impact of this artifact can be assessed in the growth of student scores within the program itself by looking at pre-assessment scores and post-assessment scores. Teachers will automatically see growth from their students learning based on their collaboratively planned instructional strategies and assessments. Teachers and administrators can also use this document for end of year evaluations for evidence of meeting TKES standards within the school year.