Standard 2.7 AssessmentCandidates model and facilitate the effective use of diagnostic, formative, and summative assessments to measure student learning and technology literacy, including the use of digital assessment tools and resources.
(PSC 2.7/ISTE 2g) ![]()
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The artifact representing this standard is a Data Inventory for McConnell Middle School. I conducted this data inventory as a requirement for ITEC 7305, Data Analysis, and School Improvement. The Data Inventory is a comprehensive list of all sources and types of student-level data that are available at McConnell Middle School. The inventory included the type and location of diagnostic, formative, and summative assessments results broken down by content area, collection date, students assessed, accessibility, and current uses of each data set as a measure of student learning.
This artifact provides an extensive list of diagnostic, formative, and summative assessments collected at McConnell Middle School. This inventory includes our state criterion referenced tests, nationally normed reference tests, county benchmarks, grade-level, common assessments, and other digital assessment tools and resources. The data inventory also includes all surveys on staff and student perception and technological literacy. This artifact also includes the current uses of the data and suggestions for enhanced uses of the data. This document has become the first draft in a reference guide for our data team going forward.
Once I completed this assignment I went straight to my principal and asked for permission to form an extended team of teachers to task with exploring each of these data sources in a more robust way. While our school has worked to be data driven for some time, this assignment allowed me to identify “small” data (classwork and formative assessments) that were largely being ignored in favor of “big” data (CoGAT and EOCT). A goal we will pursue this year is to collect more local assessment data and to do so at a higher sample rate. As I reflect on this assignment, I am thankful that I was pushed to take another look at our data from a different perspective. We have used data in a top-down manner for as long as I can remember to evaluate teachers and students as a post mortem to confirm poor performance. However, I hope that going forward we can use data in a bottom up, diagnostic manner to proactively address and enhance student and teacher performance.
This inventory of data sources include data that is frequently used by teachers and data sources that are rarely, if ever, leveraged. The irony is that the most ignored datasets hold the greatest promise to increase student performance proactively. The most popular data is the state and district standardized test results which are rarely available in time to help teachers help students. As I talked to my other local school improvement team members, there was a growing consensus that data analysis would be better served by a team focused on data. In planning for the coming year I asked for the team’s blessing to recruit a diverse group of teachers to form a data team tasked to collaboratively analyze this data and make instructional decisions that might more effectively meet the needs of diverse learners.
Going forward we will use this new data team to analyze, make suggestions for improvement, evaluate and revise plans on an ongoing basis. We have plenty of data to use as a benchmark. Success or failure of this team will be impossible to avoid noting in the team’s report as the very data measured to evaluate and plan will also reveal improvement or entropy against our benchmarks.
This artifact provides an extensive list of diagnostic, formative, and summative assessments collected at McConnell Middle School. This inventory includes our state criterion referenced tests, nationally normed reference tests, county benchmarks, grade-level, common assessments, and other digital assessment tools and resources. The data inventory also includes all surveys on staff and student perception and technological literacy. This artifact also includes the current uses of the data and suggestions for enhanced uses of the data. This document has become the first draft in a reference guide for our data team going forward.
Once I completed this assignment I went straight to my principal and asked for permission to form an extended team of teachers to task with exploring each of these data sources in a more robust way. While our school has worked to be data driven for some time, this assignment allowed me to identify “small” data (classwork and formative assessments) that were largely being ignored in favor of “big” data (CoGAT and EOCT). A goal we will pursue this year is to collect more local assessment data and to do so at a higher sample rate. As I reflect on this assignment, I am thankful that I was pushed to take another look at our data from a different perspective. We have used data in a top-down manner for as long as I can remember to evaluate teachers and students as a post mortem to confirm poor performance. However, I hope that going forward we can use data in a bottom up, diagnostic manner to proactively address and enhance student and teacher performance.
This inventory of data sources include data that is frequently used by teachers and data sources that are rarely, if ever, leveraged. The irony is that the most ignored datasets hold the greatest promise to increase student performance proactively. The most popular data is the state and district standardized test results which are rarely available in time to help teachers help students. As I talked to my other local school improvement team members, there was a growing consensus that data analysis would be better served by a team focused on data. In planning for the coming year I asked for the team’s blessing to recruit a diverse group of teachers to form a data team tasked to collaboratively analyze this data and make instructional decisions that might more effectively meet the needs of diverse learners.
Going forward we will use this new data team to analyze, make suggestions for improvement, evaluate and revise plans on an ongoing basis. We have plenty of data to use as a benchmark. Success or failure of this team will be impossible to avoid noting in the team’s report as the very data measured to evaluate and plan will also reveal improvement or entropy against our benchmarks.