Assignment: Using Student Performance Data

  

To prepare:Review the following:The two videos Data-driven Instruction and Assessment: An Instructional Observation Process—Bethel School District, Eugene, Oregon and Data-driven Instruction and Assessment: An Instructional Observation Process—Richland II School District, Columbia, South Carolina for the effects of instructional observations conducted by Nancy Gregory and Rhonda Wolter. These two segments demonstrate how data are collected through instructional observation, both traditional and as a “walk-through.”Video: Laureate Education (Producer). (2011f). Data-driven instruction and assessment: An instructional observation process—Bethel School District, Eugene, Oregon [Video file]. Baltimore, MD: Author.Video: Laureate Education (Producer). (2011g). Data-driven instruction and assessment: An instructional observation process—Richland II School District, Columbia, South Carolina [Video file]. Baltimore, MD: Author. The videos and data slides from the focus group interviews in the Bethel School District, which demonstrate how valid data were used to drive quality instruction in the districtReadings in Chapter 2 of the Mandinach and Jackson (2012) textThe vignettes in Chapter 4 of the Bambrick-Santoyo (2010) text (Also, examine the article by Hamilton et al.)To submit:This Assignment is a paper, 6 to 8 pages in length, consisting of the following sections. Use the following headings to organize your work:Section 1: Using Assessment to Drive InstructionDescribe at least two examples of the types of assessments used in examples from this module’s readings and videos. Explain the purpose for using these particular forms of assessment, and how data were utilized to drive instruction. Provide examples of how the data improved student learning. Be specific, and provide details to support your choices.Section 2: District Practices: Standards-Based Teaching and LearningRefer to the district you chose in Module 1 to complete the following component.Review the data provided by the district you selected. Which aspects of the information described in Figure 2.2, “Conceptual Framework for Data Driven Decision Making,” on page 34 of the Mandinach and Jackson (2012) text, were you able to obtain? What does the data tell you about the district’s use of standards-based teaching and learning?Using Student Performance DataReview Figure 2.6, “Abbott’s Framework of Improvement and Readiness,” on page 39 in the Mandinach and Jackson (2012) text. How would the district you selected fare in response to this process? Based on what you know about the district, explain what you perceive to be the use of student performance data to lead instruction and school improvement efforts. You may complete this task focusing on a specific grade level and/or content area. Be specific, and provide examples.Section 3: Describing the RelationshipFinally, address the following question: What is the relationship between assessment, data, and instruction? As a CIA leader, what strategies will you implement to make sure the relationship remains stable and effective? Use scholarly research to support your strategies.Length: 6 to 8 pages
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4
Culture
Creating Conditions for Success
AN OPENING STORY
Iri our first year of implementation of data-driven instruction, we knew that
one teacher in particular was going to be very resistant. As one of the most
veteran teachers on the staff and well respected by her peers, she also wielded
great influence on others. Although we had invited her to join a leadership team
to launch the initiative, she was still unprepared for the poor results her students
received on their first interim assessment. As we followed the protocols established
in Chapter Two and Chapter Three, her students’ performance notably improved,
but she remained very unhappy and completely unconvinced that data-driven
practices had anything to do with these improvements.· She regularly sent us
signals of her displeasure with this initiative and felt it was stifling her teaching.
At the end of the year, students gained thirty points in proficiency from the
previous year’s cohort, despite the fact that this cohort had been even lower­
skilled when they started the year! Despite all the signs of her accomplishments,
the teacher was still un:willing to acknowledge any impact of data-driven practices
and continued to advocate for removing these systems.
Two years later, however, we had a faculty meeting and were discussing
whether we should shorten our analysis protocol and action plan to make it
easier for teachers to complete. In the middle of the meeting, this same teacher
raised her hand and said, “This is a critical reason why o}r students learn so
effectively; we shouldn’t shorten it at all.”
.
It took two full years’ for the teacher to buy in to data-driven instruction,
but in the meantime, her students still made dramatic gains in achievement.
When implemented well, data-driven instruction drives achievement from the
beginning-a critical factor that distinguishes it from many other initiatives that
require teacher buy-in before they have any chance of success.
DEVELOPING CULTURE
If you feed “culture of high expectations” to an Internet search engine, you will
find hundreds of articles devoted to the topic. More concretely, .studies of high­
achieving schools often talk about the influence of “culture” or “shared vision”
in their success.1 The question to ask, however, is not whether high-achieving
schools h~ve a strong culture of high expectations-they universally do-but
what were the drivers that created such a culture in each school?
In traveling around the country, I have yet to meet any teachers or school
leaders who did .not believe they had high expectations for student learning.
The difference, then, is not in what is said but what is practiced. How can a
school demystify the process of improving expectations and. operationalize it
with concrete actions that have proven to yield results? Just as standards are
meaningless until you define how to assess them, working to build a data-driven
culture is fruitless until you define the concrete drivers that guarantee it.
Building Buy-In
Initial faculty buy-in is not a prerequisite for starting to implement data-driven
instruction. (Which is just as well; it’s easy to argue that any initiative that
106
Driven by Data
I
requires complete buy-in prior to implementation is likely to fail.) The best
initiatives in schools-and elsewhere-do not require buy-in, they create it. In
fact, the Camden County, Georgia, School District published a very persuasivM
article about the phases of data-driven instruction. It illustrated how teachers in
their district moved from Phase 1 to Phase 5:
• Phase 1: Confusion and overload-“This is too much!”
• Phase 2: Feeling inadequate and distrustful-“How can two questions
on a test possibly establish mastery of an objective? These questions are
terrible!”
• Phase 3: Challenging the test-“That is a poor question. Answer ‘b’ is a
trick answer.”
• NP hase 4: Examining the results objectively and looking for
causes­”Which students need extra help and in what topic? Which
topics do I need to re-teach in different ways?”
• P hase 5: Accepting data as useful information, seeking solutions, and
modifying instruction- “Their inability to subtract negative integers
affected their ability to solve the algebraic equation. I need to re-visit the
concept of negative numbers and how to use them. “2
Rather than hope that teachers enjoy the process from the very beginning,
school leaders should anticipate that it will take various phases for everyone to
see the value of data-driven instruction.
The article from Camden County, Georgia, is one of the few publications to
discuss the hurdles and challenges that occur early on in the implementation
of data-driven instruction. If you would like to look at an even more concrete
example, read the case study included in the CD-ROM about Douglass Street
School. While the names were changed to allow for a candid sharing of the
details, the case study is a true story and can give more insight into how schools·
make dramatic gains in achievement despite initial resistance.
Culture
107
Data-Driven Success Story
Chicago International Charter School: Winning Converts
The Results
Illinois !SAT Exam: Percentage of Chicago International Charter,
School Students at or Above Proficiency
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2005-06
58.8%
61.5%
2006-07
72.1%
83.1%
4
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Figure 4.1
Chicago International Charter School Students’ Scores on the
Illinois ISAT Exam: Percentage· at or Above Proficiency.
90%
—–
86%
83%
— English and
Language Arts
— Mathematics
60%
50 %
+—– ———
2005-06
2006-07
2007-08
The Story
In 2005, the Chicago International Charter School (C.I.C.S.) Bucktown Campus was
stagnating. With ineffective leadership and an unmotivated faculty, the school had seen
almost no change in test scores since 2000. Turon Ivy set out to change this upon
108
Driven by Data
becoming principal at C.I.C.S.-Bucktown. Taking what he learned from the Data-Driven
Instruction Comprehensive Leadership Workshop (see Chapter Twelve]. Ivy introduced
interim assessments to the school.
Yet although the new principal was enthusiastic about data-driven instruction, his
teachers were considerably more wary. During the 2005-06 school year, resistance from
the faculty was strong, a problem greatly compounded by the lack of communication
and transparency that had been practiced by Ivy’s predecessor. Rather than abandon
the project of data-driven instruction, the leadership at C.I.C.S.-Bucktown put systems in
place to win staff over and secure faculty participation. One of the most important parts of
this process was running detailed professional development sessions to introduce faculty
members to data-driven instruction and to show them its value in improving education.
More important, during a time where many faculty members were apprehensive about
testing, Ivy presented data-driven analysis not as a way for the administration to catch
poor teachers but as an opportunity for the school to succeed as a whole.
In the next year, 2006-07, Ivy continued to win staff over, making professional
development more systematic than it had been in the past and creating a transparent
school calendar to allow for faculty participation and input. As more and more staff
bought in, the few holdout teachers eventually left on their own accord to work elsewhere.
Ivy continued to visit other high-achieving schools and attend data-driven workshops
to bring additional best practices to the school. Additionally, C.I.C.S.-Bucktown began
having teachers from different grade levels meet with each other so that teachers of
younger students could coordinate their curricula to the demands of later academic
years. As a result of this strong emphasis on effective professional development and staff
involvement, Ivy was able to bring a formerly dysfunctional school from stagnation to
success!
Key Drivers from Implementation Rubric
• ] Introductory professional development: If great care is not taken when setting
up the professional development session that introduces data-driven instruction,
the result can be a seemingly insurmountable level of faculty distrust and
resistance. Framing assessments as opportunities for the entire school to
improve its teaching as a whole is a great strategy for persuading wary staff
to give them a try.
• ] Build by borrowing: Ivy looked for best practices in other high-achieving schools
that ]he could bring to C.I.C.S.-Bucktown, and he also built systems for
teachers to learn from each other within the school.
• ] Implementation calendar: By developing a transparent implementation calendar,
I vy removed the mystery of data-driven instruction and allowed teachers to
understand clearly what was occurring each step of the way. Even teachers who
were resistant knew what was expected of them and achieved stronger results.
Culture
109
Core Idea
•
Data-driven instruction properly implemented does not require teacher buy­
in – it creates it.
Much of what builds an effective data-driven culture is embedded within the
drivers of assessment, analysis, and action. This chapter focuses on the remaining
explicit structures that build buy-in and guarantee an effective data-driven culture.
In my experience, following the drivers identified in this book will lead directly
to increased student achievement.
IDENTIFYING AND DEVELOPING THE RIGHT
LEADERSHIP TEAM
At the heart of this work is the identification of the school leadership team. School
leaders should identify and cultivate relationships with key faculty leaders, ties that
can be thought of as bridges to buy-in. As long as structures exist to ensure the
participation of key school leaders, improved results will win over the rest
of the faculty in time.
In the Harvard Business Review article “Informal Networks: The Company
Behind the Chart,” David Krackhardt and Jeffrey Hanson argue about the
importance of making sure that the leadership team includes. members of
two important networks in an organization: the expert network and the trust
network.3 The expert network consists of those members with the greatest
expertise: in the case of a school, your strongest teachers. These are the people
teachers admire for the quality of their teaching. The trust network in a school,
by contrast, consists of teachers to whom others turn for personal support or
guidance. While riot necessarily the strongest teachers, they are the ones with the
greatest influence on their peers in the day-to-day working of the school.
Most school leadership· teams already consist of leaders of the expert net­
work. Securing the .input and involvement of leaders of the trust network
11 0
Driven by Data
as well will go a long way toward creating a solid culture of data-driven
instruction.
Involvement now, buy-in later: Once these staff members are identified, every
effort should be made to include them in the process of implementing data­
driven instruction. Of course, not every school leader will instantly embrace
data-driven instruction, and some will initially dislike it. By keeping such faculty
leaders involved in the process, however, the principal will be able to minimize
resistance and at least ensure participation on the part of the most influential
teachers. This is extremely significant, because as long as leaders are involved and
willing to stay with the plan, then buy-in will inevitably follow.
THE CALENDAR
A story that sticks (author unknown): during one lecture, a time management
expert set out a large glass container and a box of fist-sized rocks. After carefully
placing rocks in the glass container, he came to a point where no more would fit.
He then turned to the audience and asked: “Is it full?”
“Yes,” came the reply.
He then produced a box of smaller pebbles and managed to fit a few into the
container. “Is it full?” he asked again.
“Yes, it is now,” was the answer.
From a small bucket he began to pour gravel into the spaces between the rocks
and pebbles, every now and then shaking the container until no more would go
in. “Is it full?”
“Probably not!” the audience replied.
Out came some fine sand, and he began to pour. With just a few gentle shakes,
he was able to bring the contents of the container to the very brim. “Is it full?”
“No!”
Next came a pitcher of water and this he allowed to drip slowly into the
container until, in time, the pitcher was empty.
“So,” he asked, “what have you learned today?”
“Well,” someone responded, “the lesson is that there is always room for
more.”
Culture
111
“Nope. The lesson is that if you don’t put the big rocks in first, they won’t fit.”
The lesson of the story is clear: if certain key fundamentals are not secured
first, then nothing else will be possible. Although this principle applies to many
/facets of life, it is especially apparent in data-driven instruction when it comes to
creating a culture in which assessment, analysis, and action can thrive. The “jar”
in this arena is the school calendar. The “big rocks” are interim assessments,
analysis, and action. Without the “big rocks” firmly in place within this calendar,
it is almost impossible to create a truly excellent data-driven school.
Schools live and die by their calendars: whatever makes it onto the schoolwide
calendar trumps other activities that come later. Given that data-driven instruction
is based upon timely and regular analysis, assessment, and. action, placing these
events on the school calendar first is essential for student achievement. Without
being embedded in the structure of the calendar and school schedule, analysis
and action are likely to be ignored, overlooked, or delayed, causing the project to
fail. There are too many moving pieces in a school year to expect effective data, driven instruction to “just happen”; schools must con,sciously craft a calendar
that lays the foundation for genuine progress.
Core Idea
• School calendars drive priorities: Make sure to schedule assessments, scoring,
analysis, and professional development before placing any other events on the
school calendar.
Here are the keys for developing an effective data-driven school calendar:
• YMake time for data: The first critical feature of the calendar is that it blocks
off time for interim assessments to be administered, scored, and analyzed.
All too often, schools will maketime to test but leave no time to grade
exams, a situation that gives teachers and school leaders an excuse to
postpone analysis until it is useless.
• YNote end-goal tests when placing interim assessments: Beyond fixing the time
for interim assessments, the schoolwide calendar must also take into
account the state and national tests taken by students during the year.
Given that interim assessments are most effective in six- to eight-week
112
Driven by Data
periods, plan the timing of the interim assessments working backward
from the summative state and national tests, and then working forward for
the rest of the school year after these assessments. (For example, if your
state test is in February, plan for an interim assessment cycle the leads up
to the February state test, and then after February you can start working
toward the standards of the following year, aj.lowing you to have a full
calendar year of interim assessments).
• Mark professional development: As a further important feature, plan for
professional development days before and after each round of interim
assessments to allow for implementing each step of the data-driven
process. This will also allow the school to provide content-focused
professional development in response to the learning needs identified on
the assessment.
• kLeave room for re-teaching: Finally, and perhaps most important, an,
effective calendar is one that builds in time for the re-teaching necessitated
· by the assessment analysis. North Star Academy, for example, formally
allots a week following assessments to re-teaching and reviewing earlier
standards. Of course, this is not to say that this entire week is spent in
review; in most cases, teachers integrate and spiral re-teaching while
presenting new material. Nevertheless, the very existence of this re-teach
week sends a powerful signal that assessment results will guide curriculum
and that data results are to be taken seriously.
Exhibit 4.1 is an example of a yearlong assessment calendar. As can be seen
from Exhibit 4.1, an effective calendar need not be overly complex or difficult
to create, but it must include the basic elements outlined here if it is to be
successful.
A second question often asked is how to structure the week itself when
assessments occur .and then analysis meetings and re-teaching. Chapter Two
(Analysis) highlighted a one-week schedule used by Greater Newark Academy,
and that can serve as a model.
Build by Borrowing
In building a data-driven culture, few skills are as vital as the ability to identify
and adapt best practices from other successful schools. All the highest-achieving
Culture
113
Exhibit 4.1
Assessment Calendar.
4 Weeks (5/28-6/221
Unit 6 and Final Perfor­
mance Task Preparation
YEAR-END (6/W-6/291
Final Performance
Taosks
Oral presentations and
large math projects
schools highlighted in this book are masters of “building by borrowing.” They
visited schools that were achieving better results than their own and borrowed
any and every tool that could increase their own results. Leaders should strive
to create an ethos in which teachers and school leaders perpetually seek out
the best ideas beyond their building. During their initial roll-out of data-driven
instruction, leaders should make an effort to visit effective schools and see data
in action. Such visits will surely provide important insights into the mechanics of
data-driven instruction, but they also provide something more important: hope.
By seeing data-driven instruction succeed with their own eyes, school leaders and
teachers will gain the confidence to articulate a compelling and coherent vision
of what data-driven excellence looks like and what it will take to truly succeed.
114
Driven by Data
One individual has taken this concept to another level. Doug Lemov, a fellow
managing director at Un,common Schools and manager of True North Rochester
Prep (see success story), has devoted the past few years to 1finding the most
accomplished urban school teachers in the country-“Master Teachers.” He has
videotaped them in action and identified the shared strategies that they all use
to be so successful. He compiled these experiences into Teach Like a Champion,
which includes a framework, actual video clips, and resources to be used in
training teachers. Lemov is proving that teachers don’t have to be born great;
they can also be developed into high-achieving teachers. It is also much easier to
believe in success when you can see examples of success with students like your
own. This happens naturally in the assessment cycle when teachers see their own
students improve on subsequent assessments. In these video clips, Lemov makes
it possible for school leaders and teachers to “build by borrowing” without ever
leaving their own schools!
Getting to Why
As you lead your school to build a culture of data-driven instruction, the most
frequent and important question you will face is also among the simplest: why?
Very often, people will ask why such dramatic changes are being made and, more
fundamentally, why data-driven instruction matters at all. Implementing the coWe
principles of effective professional development and building by borrowing will
answer these questions effectively for most school staff members. However, other
staff members will have lingering questions, and they will need a brief, personal
“sales pitch.” Indee …
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