No more random acts of teaching: high levels of achievement are the result of systematic, targeted and purposeful instruction. Without data, instruction becomes a series of well-intentioned but essentially random acts of teaching.

The skillful use of classroom data allows us to identify strengths and weaknesses in student learning, monitor student progress toward specified goals, make adjustments in instruction, measure the degree to which students meet standards, and distinguish effective from ineffective instructional practice. Without data, instruction becomes a series of well-intentioned but essentially "random acts of teaching."

High levels of student achievement are the result of systematic, targeted and purposeful instruction. Effective instruction depends on the skillful use of classroom assessment data. Ultimately, the degree to which classroom data are used for instructional decision-making depends on the principal.

The principal is the key to creating and maintaining a school in which data are used for instructional decision-making. The degree to which classroom data become part of daily decision-making depends on the principal's ability to: (1) model effective data-driven decision-making; (2) build the capacity of others to use classroom data; (3) make data a priority for decision-making; and (4) create time within existing structures and practices for working with data.

The question is, where does the principal learn to do these things?

Expanding the principal's knowledge and skills in using classroom data

Over the past year, approximately 450 elementary school principals have wrestled with classroom assessment data during the Assessment Institute for Elementary Principals: Using Classroom Data to Improve Student Achievement. This institute is conducted by the Southern California Comprehensive Assistance Center, Los Angeles County Office of Education.

The goals of the two-day institute are to expand the principal's knowledge, skills and confidence in using classroom data; and provide the principal with strategies to build the capacity of others to use classroom data to improve student learning.

To ensure the principal is prepared to return to school ready to implement new skills, he/she is provided with user-friendly tools and strategies for using classroom data, opportunities to analyze a wide range of classroom assessment data and time to practice new skills.

Principals share their needs: How to meet expectations

The assessment institute was developed in response to interviews in which elementary school principals discussed classroom assessment data and how they are used in schools. According to the principals, they were expected to use data in their decision-making, but had never been provided with the necessary knowledge and skills. Similarly, they were expected to promote the use of data among their teachers, but were not taught how to do so. When questioned as to their professional development needs, the principals:

1. Recognized classroom assessment data were critical to instructional decision-making, but they lacked the skills and confidence necessary to discuss the wide range of classroom assessment data generated by their teachers.

2. Emphasized the limitations of one-day professional development sessions, in which there wasn't enough time to practice new skills.

3. Reported they needed user-friendly tools and strategies for working with data, as they and their teachers could not be expected to become experts in statistics.

4. Stated they needed strategies for helping teachers use classroom data for instructional decision-making. They asked for ideas and examples to help them integrate data into their daily work.

5. Stressed the need for professional development tailored for elementary school principals.

6. Insisted strategies and examples focus on teaching and learning.

Tools and strategies

The institute provides tools and strategies the principal can use immediately upon returning to school. Most importantly, the tools and strategies apply to many kinds of learning, data, grade levels, subject areas and scoring systems.

A principal must be prepared to meet with a kindergarten teacher at 9 a.m. to discuss the results of an alphabet recognition test, and then meet with a fourth-grade teacher at 11:30 a.m. to review data from a project on the California missions. After school, the principal must be ready to meet with a third-grade teacher to discuss the results of a recent math test.

To expect the principal to have different strategies for all the different kinds of data that is generated in classrooms is unrealistic. The challenge: provide the principal with strategies that apply to a wide range of classroom data.

Learning and practicing skills for using assessment data

During the institute the principal has many opportunities to learn and practice new skills for using classroom assessment data for instructional decision-making. All the "lessons" begin with the same scenario: A teacher meets with the principal and states, "My students just completed this assessment (assignment, project, task) and here are the results. Now what do I do?"

What does the principal say to the teacher? How does the principal build the teacher's capacity to use data to improve student learning?

Over the course of the institute, the principal learns how to respond to these questions. He/she analyzes data from five teachers, five grade levels, five subject areas and six scoring systems. He/she examines raw scores, percentages, letter grades, point systems and rubrics. The data come from group and individual assessments, and reflect basic skills, critical thinking and application skills.

The three kinds of data: Outcome data, demographic data and process data

Before the principal begins to work with data, he/she must have a conceptual model for thinking about data. During the assessment institute, the principal learns to use three kinds of data: outcome data, demographic data and process data. Each kind of data plays an important role in decision-making and, when used in combination with one another, leads to effective instruction.

Outcome data is the only thing that distinguishes teaching from learning. They indicate the degree to which an individual student or group of students has achieved specified standards, which is a reflection of the effectiveness of instruction. Sample sources of outcome data include teacher-made tests and assignments, national norm-referenced tests, report cards, observation surveys, portfolios, district-level assessments, exhibitions and performance tasks.

When the principal considers demographic data, the focus is on the experiences, attitudes and perceptions that affect the manner and rate in which students learn. Critical to using demographic data is the distinction between an "explanation" and an "excuse."

When demographic data are viewed as an explanation, they provide insight into student learning and critical information for instruction. Viewed as an excuse, demographic data become a rationale for low achievement.

The principal learns to identify demographic variables that influence student learning; explain how those factors affect learning; and use that information to design the most appropriate learning experiences. Samples of demographic data include language proficiency, preschool experience, attendance patterns, family support, mobility rate, socio-economic status, parent education and primary language.

Process data differ from outcome and demographic data, as they are the only data over which teachers and the principal have control. Process data refer to the structures, attitudes and practices that comprise the instructional program.

Process data are considered the "change" data. While outcome and demographic data paint a picture of "what is," process data refer to teacher and principal behavior, which ultimately affects student learning. Sample process data include curriculum organization, instructional strategies, assessment practices, teacher expectations, instructional time, teacher expertise, class management, materials and supplies and teacher experience.

Organizing data

One of the first things the principal learns to do is to organize classroom data in a user-friendly format. In most cases, classroom data take the form of an alphabetical list of students, which does not easily lend itself to data analysis. The principal learns to organize data so that patterns and trends "jump off the page." Most often, data are reorganized in the form of a range and distribution, a chart or a graph. The principal learns there are three basic approaches to organizing data:

1. By distribution of scores, which answers the question of "how many?" For example, how many students met or did not meet the standard? How many boys or girls met the standard? How many students were close to or far from the standard? How many English language learners met the standard?

2. By distribution of students, which answers the question of "who." For example, who met the standard and who did not? Who was close to meeting the standard and who was not? Who scored the highest? Who scored the lowest?

3. By patterns in student work, which answers the question of "what?" What knowledge and/or skills did the students master and not master? What specific knowledge and/or skills should be the focus of teacher intervention? What knowledge and/or skills should be the focus of enrichment activities?

The principal also learns that there is no "right" way to organize data. Each teacher must be encouraged to organize data in a format that is most informative for him/her.

Analyzing data

Over the course of the institute, the principal learns to use three different strategies for analyzing data. Depending on the teacher, the principal might use a simple two-question process, a four-question sequence or a highly structured set of questions.

In the two-question process, the teacher is asked: (1) What did you learn from analyzing these data? (2) How might you use these data for instructional decision-making?

In the four-question sequence, the teacher is asked: (1) What do you know as a result of examining these data? (2) What do you think (what are your "hunches?") as a result of examining these data? (3) What do you want to know as a result of examining these data? (4) How can you use these data for instructional decision-making?

The highly structured questions are divided into three categories: "descriptive data," "thoughts about data" and "stepping back from data."

"Descriptive data" questions are limited to what the data tell the teacher. For example, how many and which students exceeded, met or failed to meet the standard? How many and which students were close to or far from the standard? How did the performance of subgroups compare with one another? How were students distributed among the performance levels?

"Thoughts about data" questions require the teacher to make inferences from the data. For example, what did you learn from the range and distribution of scores? What patterns did you find in these data? How do these data compare with other data you have? What questions do these data raise for you?

"Stepping back from data" questions address technical issues regarding the task. For example, how well is this task aligned with specified standards? To what degree do these data provide an accurate picture of student performance? Why did you use this particular scoring system for this task? How helpful are these data to students and parents?

Grouping students

The principal also learns to use data to group students in the most effective manner. Specifically, how to distinguish among three levels of need and support: re-do, review and re-teach.

Students who require the "re-do" level of support just need additional practice to meet the standard. For example, a student who knows multiplication facts but can't recall them quickly; a student who knows how to write neatly in cursive but can't write fluidly; and a student who recognizes sight words but can't read them rapidly.

Students who require the "review" level of support generally understand what's expected, but typically have a minor area(s) of weakness. Students only require a brief explanation and additional guided practice. For example, a student who is able to write a strong paragraph but has trouble with quotation marks; or a student who understands a particular math process but has trouble with one specific step in the process.

Students who require the "re-teach" level of support don't understand the basic concepts and/or skills targeted by the teacher. They typically require the teacher to start from the beginning of the lesson or unit. For example, a student who doesn't understand the relationship between letters and sounds -- or a student who doesn't know the difference between fact and opinion -- would require re-teaching.

Students who require special interventions may have very different needs from one another, and those differences are often overlooked. Using data to systematically group students leads to purposeful instruction and rapid student progress.

Identifying obstacles

Throughout the institute, the principal has many opportunities to identify obstacles likely to be encountered when he/she returns to school and begins to make data a part of the decision-making process. The principal is also given the opportunity to develop strategies for overcoming those obstacles.

Among the obstacles most often cited are: insufficient time for working with data; inadequate tools and strategies; absence of staff expertise; fear of change; skepticism toward new ideas; "been there, done that" attitude; complacency; fears and misconceptions about data; concern over implied accountability; lack of technology; building ownership; and the "this too shall pass" position.

We can't discuss all of the obstacles and strategies here, so let's look at the issue that is most often cited: lack of time. Given it is unlikely schools will receive additional time for working with data, it is up to the principal to weave data into existing structures and practices. A sample of the ideas generated during the institute are listed below.

1. Focus on data analysis goals in teacher evaluation plans and conferences.

2. Make data a priority for professional development.

3. Discuss data at faculty meetings.

4. Require a specified portion of department/grade level/team meetings be devoted to data.

5. Require that budget requests include supportive data.

6. Select textbooks, supplemental materials and equipment based on data.

7. Discuss data in meetings with teacher-leader groups.

8. Identify a group of teachers who are open to working with data and provide them with professional development, ongoing technical assistance and a forum to share their expertise and success.

Creating change

The principal can expect obstacles to his/her efforts to build the school's capacity to use classroom data to increase student achievement. During the assessment institute, the principal has an opportunity to identify those obstacles and develop strategies to overcome them.

If schools are ever going to see significant increases in student achievement, the principal and teachers must be able to skillfully use classroom assessment data for instructional decision-making. They must be able to use data to provide targeted, systematic and purposeful instruction to meet the unique needs of every student. The principal is the only one in the system who can create this change, and can look to the Assessment Institute for Elementary Principals: Using Classroom Data to Improve Student Achievement, for the knowledge and skills necessary to make it happen.

RELATED ARTICLE: Strategies: using data.

When the principal leaves the assessment institute, he/she is prepared to discuss any classroom data. The principal is able to:

1. Analyze classroom data in relation to standards.

2. Use a wide range of data to improve student achievement.

3. Organize data in an effective format.

4. Discuss data in simple terms.

5. Apply user-friendly models of data analysis.

6. Create time to discuss data.

7. Make data a part of daily decision-making.

For more information about the institute, call [phone] or e-mail [e-mail].

Dennis Fox is a consultant for the Southern California Comprehensive Assistance Center, Los Angeles County Office of Education.