CTAP4 Data Assessment

Many school districts have begun to administer periodic assessments to complement end-of-the-year state testing. Assessments are used to guide instruction, monitor student learning, evaluate teachers and predict scores on future state tests.

The Pivot tables and chartsfeature in Excel lets educators begin disaggregating and analyzing data within seconds (literally) of receiving original data files from state departments, testing companies, and/or school districts. These ten tutorials show how to use what is perhaps the most powerful data analysis tool within Excel

These materials are for school board members who want to know more about how to use data to make good decisions for children in public schools. Trainers who work with school board members also can use these materials at state and national conferences or in local training sessions.

The successful integration of data into a district's decision-making process requires both a culture of change and a data management system to support change. Nice chart of progress quadrants included in the discussion.

Districts are collecting more data than ever, but are they using it to improve performance?

All schools have data about student achievement. To make the most of these data to improve learning, we need to take be aware of many other factors - evidence that describes our students' wider learning environment.

The 118 page report, "Framework for a Comprehensive Education Data System in California - Unlocking the Power of Data to Continually Improve Public Education," recommends significantly expanding and linking information from California's K-12 system to data from pre-K, higher education, workforce, and social services data systems to inform decisions that extend beyond K-12.

Imagine the research possibilities if every student in the country carried a "virtual backpack" stuffed with statistics on his or her entire educational history. The data, traveling with students as they moved from school to school, could be used to update parents on their children's learning progress, register students in school, or import information when they moved to a new city or entered college.

Like never before, today's classroom teachers routinely are being asked to collaboratively analyze student data, develop or implement new mandated curricula, and assess the effectiveness of these innovations. Ironically, few preservice preparatory or in-service professional development programs actively train classroom instructors in the use of team-based inquiry or collaborative data- driven problem solving. Framed within the context of the literature and governmental efforts to achieve school reform, this article describes one such in-service program, in practice at public and charter schools in high-need communities in New York City. The Inquiry Based School Improvement Program (IBSIP) was created and designed to help schools serving high-need communities in New York City engage in the types of team-based inquiry and data-driven problem solving needed to meet the everchanging institutional demands on these schools to improve.

During the last 65 years, ASCD publications have charted the education profession's perennial quest to design and use assessments wisely. ASCD and its members have consistently urged clarity about the purposes of evaluation and advocated the use of assessments that are appropriate for their specific purposes. A thread that runs through six decades of writing about assessment is the belief that assessments should answer two questions: How are we doing? How can we do it better?

The momentum behind building high-quality data systems to harvest better information about student, school, and district performance has never been stronger. Although collecting data is essential, knowing how to analyze and apply this information is just as important for meeting the end goal of improving student achievement. The purpose of this study is to identify, quantify, and report on district-level processes that enable effective utilization of data to increase academic achievement at the classroom level.

Here is a suggestion for the use of this article with colleagues. You will need a list of the 10 understandings and copies of the article written by Carol Ann Tomlinson.
Informative assessment:
1. isn't just about tests.
2. really isn't about the gradebook.
3. isn't always formal.
4. isn't separate from the curriculum.
5. isn't about "after."
6. isn't and end in itself.
7. isn't separate from instruction.
8. isn't just about student readiness.
9. isn't just about finding weaknesses.
10. isn't just for the teacher.

This study from the Rand Corporation explores how teachers and principals use test score data, what features of its use are relevant to them, and what assistance is available to help them use the data for instructional decisionmaking.

Teachers can use information from assessments required under NCLB to inform classroom decisions and provide the best possible instruction for students. [Pamphlet on Standardized and Dynamic Assessment from NCLB].

There's a ton of data being collected. The trick is to know how to use it effectively.

Using Data to Improve Schools: What's Working is an easy-to-read guide to using data to drive school improvement. School system leaders and their staffs can learn from this book how to build a districtwide culture of inquiry that values the use of data for sound decision-making. School board members, parents and community members interested in helping improve schools will find tools for their work as well in this guide. It describes the challenges and the successes of educators from districts both large and small committed to using data.

It's not as easy as it looks to determine which schools are doing better than others. Two different criteria are relevant: is the difference in performance between two schools large enough to matter, which is sometimes termed educational significance or practical significance; and is the difference in performance between two schools real, or could it just be due to chance, which is typically described as statistical significance. Ideally, we are interested in differences that are both practically and statistically significant. But a difference could be large, but not statistically significant (which is often the case when we have a small sample of information about performance), or statistically significant, but very small (in which we are pretty sure that the difference is real, but it's just not very important). (Yes, statistical significance does matter!)

Our goal as educators should be to veer from an equal learning experience toward an equitable learning experience. Our job is to make sure all students have a fair, and possibly unequal, learning experience. Ensuring that each student has a fair opportunity to succeed means that one student's path may look very different from another's.

Coaching moves beyond the gym. Assessment without victims: An interview with Rick Stiggins. [Journal of Staff Development]