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Malow Junior High School: Professional Collaboration

Kim Charland

Jason Larsen

Scott Palmer

Sofia Papastamatis

EA 751

Lindson Feun, Ph, D.

Oakland University

April 6, 2013

PROFESSIONAL COLLABORATION 2

Table of Contents

Acknowledgements……………………………………………………………………1

Abstract………………………………………………………………………………..2.

Chapter 1……………………………………………………………………………….5 Introduction

o Background o Current Professional Development Plano Assumptions and Limitations o Research Questions

Chapter 2…………………………………………………………………………………9 Review of the Literature

Chapter 3…………………………………………………………………………………12 Method of the Study

o Selection of Studyo Research Designo Description of Instrumentso Data Analysis

Chapter 4………...………………………………………………………………………15 Results of the Study

o Results

Chapter 5…………………………………………………………………………………25 Conclusions and Recommendations

o Conclusionso Recommendationso Implications of future research

References……...…………………………………………………………………………23

Appendices...…………...…………………………………………………………………24


Acknowledgements

The researchers would like to thank our professor, Dr. Lindson Feun for his dedication

and mentorship for our overall project. We would also like to give thanks to the

administration, staff, and students at Malow Junior High School. Special recognition to Mr.

Robert Hock, Principal and Mrs. Janice Fusco, Assistant Principal for allowing us to conduct

this study with their staff and provide us with opportunities to engage in conversations about

their Data Team process and collaboration. Thank you to our Troy cohort members for

inquiring about Professional Learning Communities, collaboration, and giving constructive

criticism in regards to the direction of the research. Lastly, thank you to our families for all the

support and patience throughout the two years of our research and writing.


Abstract

This action research report will provide an understanding of the level and quality of

professional collaboration among staff members in a junior high school setting. This will aid

in the development of skills needed for effective professional collaboration within the current

professional development structure. The publication provides pedagogical aids- including a

staff survey, staff interviews, and a review of district common assessments- to illustrate the

concepts and principles of the data team process at Malow Junior High School. The action

research is organized into the following 5 chapters: (1) Introduction; (2) Review of Literature;

(3) Method of Study; (4) Results of Study; (5) Conclusions and Recommendations. Our

findings conclude that the data teams have seen moderate success; however, a deeper

commitment to the data team process is needed by all staff members to continue to foster a

culture in which data driven decision-making is used to drive student achievement.


Chapter 1

Introduction

Background

Utica Community Schools (UCS) is the second largest school district in the State of

Michigan, educating approximately 29,000 students. Geographically, it encompasses the city

of Utica, Shelby Township and parts of Sterling Heights, Macomb Township, Ray Township

and Washington Township. The district consists of four high schools, seven junior high

schools, and twenty-five elementary schools. Additionally, there are two senior high alternative

education schools and several programs which provide specific learning opportunities for

interested students; Utica Center for Science and Industry, Utica Center for Mathematics,

Science and Technology and the Utica Academy for International Studies. In UCS, 90% of the

student population is Caucasian, approximately 4.7% is African American and 2.8% are of

Asian descent.

While our overall enrollment has shown a five-year decrease, the diversity of the

district has been growing at an approximately equivalent rate. The number of students eligible

for Free or Reduced Lunch has increased steadily over the past five years to its current rate of

21% of the students in the district, while enrolling over one thousand students in our English

Language Learners courses.

As a large school district, the ability to make comprehensive change efficiently is

difficult. We believe that to make the systematic changes that will propel UCS from a great

district to a destination district, a cohesive district improvement plan should focus on a culture


that values collaboration among school personnel, and an appreciation and understanding of

how data can be used to positively shape instruction.

Malow Junior High School is one of seven junior high schools in the district. It is

located at the north end of the district. This school educates 1,152 seventh through ninth grade

students. There are 3 administrators, 3 counselors, and 50 teachers. Malow Junior High

School student population is 93% Caucasian, 1.3% Asian American, 3% Black or African

American, and 1.3% Hispanic or Latino. The number of students qualifying for Free and

Reduced lunch is approximately 12% of the student population. The English Language

Learners have increased over the past year from 1 student to 9 students.

Current Professional Development Plan

The plan consists of a minimum of thirty Professional Development hours embedded

into our instructional calendar. Throughout the year, six hours are met through agreed upon

faculty meetings; nine hours are completed in August, six hours in September, six hours in

November, and three hours in March. The current plan relies upon the embedded six hours of

faculty time, in addition to fourteen hours of faculty meetings for school improvement teams to

shape building goals, shape instructional plans, and work collaboratively to make decisions

that will increase student achievement.

Data Teams

During the months of September through October each building is to review their goals

and highlight one to two strategies for each goal. Once these decisions are made, the

administration is to select evidence to showcase each goal that reflects the success of each

strategy.


Throughout the school year, the building data teams develop a school improvement

plan that focuses on the strategies aligned with the district’s goals and strategies. The team

members determine the needs, specific shared instructional strategies to be utilized, and

monitor the effectiveness of these strategies on student learning. If necessary, the team

participates in professional development to support instructional strategies and make

adjustments based on their data.

Assumptions and Limitations

Evaluating collaboration at the data team process required us to make certain

assumptions. The first of which was the effectiveness of the dissemination of the data team

framework by district curriculum leaders to building administrators. We assumed that the

building administer was trained properly and subsequently provided professional development

opportunities for the staff to effectively implement the data team process. Our second

assumption was that the teaching staff is engaged in the data team process. By contract they are

required to meet in data teams and following through the required tasks given by their

administration. Some limitations we encountered throughout this study were building turnover

and lack of consistently in the data team members. We found that data teams from one

semester to the next may be different due the courses taught. We also found that when a new

teacher enters an existing data team collaboration was difficult.

Research Questions

In order to frame the essential questions of our research, we looked to current research

on effective collaboration. According to Saunders & Goldenberg (2009), “significant

achievement gains were achieved when grade-level teams were provided with consistent

meeting times, school-wide instructional leadership, and explicit protocols that focused


meeting time on students’ academic needs and how they might be instructionally addressed.”

Thus, our research questions looked into collaboration and its effects on instruction and

learning at Mallow Junior High School.

Does the data team framework foster collaboration at Malow Junior High School?

A school of our size (50 teachers), needs a framework in place that ensures that everyone is

focusing on the same goals, vision, and mission of the school. Collaboration amongst staff

members will allow professional dialogue to exist in efforts to increase student achievement.

The teaching and learning will be continually challenged to meet the needs of all the students

at Malow Junior High School.

How does professional collaboration impact teachers’ instructional strategies/practices?

To evaluate the current collaboration process/data teams to ensure the program is meeting the

needs of today’s learner. The evaluation is a process assessment to enhance professional

collaboration in order to impact instructional strategies being delivered to the students. The

results of the interviews assisted in the understanding the level and quality of collaboration

existing at Malow Junior High School.

How has the data team collaboration process impacted student achievement?

The goal of the data team is to increase student achievement throughout Malow Junior High

School. We conducted a cross sectional data analysis of the district mid-term from January

2012 to January 2013. Our goal is to see an increase in student achievement based on the

findings from the data team meetings. Teachers began to recognize the areas of difficulties and

changed their instruction to meet those needs.


Chapter 2

Review of Literature

The need for educators to work together in developing common practices and culture

has been validated through an analysis of current educational research. In their meta-analysis

of research on the effective institution of learning communities in schools, Wells & Feun

(2007) describe the need for schools to provide more than professional development; rather,

schools and districts should focus on two areas of staff-development: structural improvements

and content changes. According to the researchers, structural changes involve the process of

transitioning from educators working alone to becoming learning communities; cultural

content changes involve human behaviors associated with change and the "reflective, deeper

analyses of teaching and learning or the way that the professionals discuss student

achievement" (Wells & Feun, 2007, p.143). The researchers also note that schools generally

feel more pressure in the area of culture when moving to collaboration as a way of enhancing

student and professional growth.

A second model for increasing collaboration in the design, implementation, and the

cultural shift in professional learning is exemplified by Richard Dufour, one of the nation's

leading experts on educational collaboration. In his description, Dufour describes the need for

teachers to drive collaboration. He writes:

"When teachers work together to develop curriculum that delineates the essential knowledge and skills each student is to acquire, when they create frequent common assessments to monitor each student’s learning on a timely basis, when they collectively analyze results from those assessments to identify strengths and weaknesses, and when they help each other develop and implement strategies to improve current levels of student learning, they are engaged in the kind of professional development that builds teacher capacity and sustains school improvement." (Dufour, 2004, p. 63)


Thus, school improvement is tied to scope and sequence of learning, with educators

monitoring student achievement through the effective use and analysis of data, and once again,

the professional development of the educator to become more effective in instruction and

collaboration. The problem most districts encounter is in this process is finding the time in the

school calendar for teachers to collaborate (structure) and adapting to working together, rather

than independently (culture).

Thirdly, Caweleti & Protheroe (2003) suggest implementing program development

through a system similar to Dufour's. They call for schools to use assessments to analyze

student and teacher performance via shared accountability, with people closest to the student

driving decision-making. Extensive staff development, with a focus on curriculum alignment

and a no excuses approach to the idea that "all students can learn” is the ideal plan (p.3). What

is interesting in each of these descriptions is the necessity for those closest to learning-teachers

and building administrators-to be the driving force behind school improvement through

collaboration and mutual accountability.

Lastly, a plan for effective structural and cultural change is detailed by Lambert (2002).

The author describes collaboration giving many benefits to a school or district, especially in the

connection between learning and shared-leadership. Effective collaboration, according to

Lambert, involves the following: study groups on best educational practices, action research

teams, vertical learning communities, school improvement teams, and a direct involvement of

the building principal as an instructional leader in setting the school's vision. This

collaboration, as described by the author, detail each group accomplishing the following tasks

at successful schools:


Study Groups: At Chief Justice Milivan School in Calgary, Alberta, study groups read

pertinent educational research focusing on four areas: Building a Learning Community;

Teaching for Understanding; Representing, Assessing and Responding; and Access and

Management of Resources.

Action Research Teams: These groups study the policies and ways of educating

students of the school and determine if the school is effective in meeting student needs.

The school's "way of doing business" is then compared to research and "real world

examples" of schools that are successful at meeting student needs.

Vertical Learning Communities: This process allows "looping of students" or focusing

on student needs through a "common community in which teacher leaders have the

authority to work closely with students in instruction, curriculum design, discipline, and

family relations." Wyandotte High School in Kansas City, Mo is offered as example

where students are able to work with the same teacher throughout high school.

Leadership teams: Here, teacher leaders work with principals and district leadership to

create a common mission and vision, therefore sustaining positive change.

Integrated School Improvement Committees: These teams are open to all and are

consistently monitoring the School Improvement Plan, thereby connecting daily school

activities to continuous improvement.

Changing Role of the Principal: Lambert describes this change in collaboration and

effective leadership: " Today's effective principal constructs a shared vision with

members of the school community, convenes the conversations, insists on a student

learning focus, evokes and supports leadership in others, models and participates in

collaborative practices, helps pose the questions, and facilitates dialogue that addresses


the confounding issues of practice. This work requires skill and new understanding; it is

much easier to tell or to manage than it is to perform as a collaborative instructional

leader." (Lambert, 2002. p. 40)


Chapter 3

Method of Study

Selection of Subjects

To help evaluate the effectiveness of the professional learning/data team structure for

Malow Junior High School to date, our program evaluation team conducted a process

assessment. The evaluation determined if the current allocation of 30 hours of staff

professional development time, divided between two ½ release days, three full release days,

and six after school data team meetings is adequate to meet the building and district goals of

the data team structure. Because we are looking to evaluate our existing data team model in

order to improve it going forward, if needed, we viewed this project as primarily a formative

evaluation. Our evaluation team is comprised of professionals with experience at all three

levels of the K-12 education system: elementary, junior high school, and high school. We have

practicing elementary and junior high school administrators, as well as a county level leader.

The diversity of this team, coupled with approximately sixty years of combined experience in

public education, makes this informal evaluation credible.

We consider our evaluation to be more of an internal process, as 75% of our evaluation

team is, or has been, employed by UCS. Our remaining team member will provide a valuable

external perspective. Several years ago his district evaluated and implemented a similar

framework for data teams.

Due to budget constraints, the decrease in administration has required teachers to

perform more non-instructional duties. This phenomenon has increased the emphasis on

transparency, accountability, data driven decision making, and reform in public education.


Consequently, more time and training is needed to complete these tasks. Evaluating the

process that Malow Junior High School has implemented to attack these challenges will require

an objectives-oriented approach. We examined the objectives of our current data team

structure for data teams and the collaboration to whether or not we are meeting these goals in

an efficient and sustainable manner.

Research Design

The data collection process for this project began by surveying the fifty person Malow

staff during the fall semester of the 2012-2013 school year. The staff survey asked the teachers

about their satisfaction with the effectiveness of the current data team process in meeting their

building goals of using data driven decision making to improve student achievement.

Following the paper and pencil survey, we interviewed the following Malow data

teams; 7 members the social studies team, 5 members of the science team, 4 members of the

foreign language team, and 6 members of the elective team. Our interviews were focusing the

questions more specifically on the role of collaboration within their respective data team. We

asked them in groups if they felt their data team lends itself to changing instructional practices

in the classroom to meet the needs of the students. The sample set included teachers from

different content areas-with varying degrees of teaching experience-and of both genders (sees

Appendix A for the consent form).

Lastly, we completed a data analysis of district common assessments throughout all

content areas. The data came from mid-term assessments administered in January 2012

compared to January 2013. The data teams were given time to analyze their data from January

2012 and were asked to make changes with their current teaching practices to increase student

achievement.


The time-line for this action research was approximately one year. The time line was a

sufficient amount of time to gather the data and evaluate the data team process at Malow Junior

High.

Data Analysis

The types of data used to conduct this action research were gathered from surveys,

interviews, and a cross sectional data analysis of district common assessments. We began by

surveying teachers individually at a staff meeting. We asked them to evaluate how they felt the

data team process impact instructional practices in their classroom. Next, we conducted

interviews with every data team to gain perspective on what they believed collaboration was

amongst their team members. This allowed us to gain a clear picture of the multiple

interpretations of data teams. The last data we analyzed was the district common mid-year

assessment for all content areas. We looked at the effectiveness of the data team within the

mid-year achievement growth of the assessment.


Chapter 4

Results of Study

At Malow Junior High, we found that the curriculum departments divide into data teams,

e.g. World History, Instrumental Music, and Mathematics. Concluding the data analysis we

found that teachers were self selecting themselves into sub-groups. These sub-groups were

more focused into their curricular areas. From the interviews conducted, we found that this

lead to less collaboration between departments because there were less teachers within the

team. An example of this is evident within the language department. The administration

considered they were a language data team and the teachers created three separate data teams:

French, German, and Spanish (see Appendix B for interview questions).

Results

After analyzing the data for question one, we found that one data team fully understood the

data team process. Most of the other data teams focused on individual tasks and the common

summative assessments. There was no mention of creating formative assessments or common

lesson plans as ways to improve instructional practices and strategies implemented in the

classroom. The focus was being less critical of one another while analyzing data and student

growth.

After analyzing the data for question two, we found that they created their own data team

collaboration process. They have their own interpretation of the expectations and procedures

of the process. They are analyzing the results of summative assessments and are entering their

scores into the districts online data management system.


After analyzing the data for question three, we found some common language. The staff

expressed the strengths of the team were to share data and ideas for improvement. They

seemed to understand that they are a critical component to the data team process; the sharing

and analyzing of data is the core of the process.

After analyzing the data for question four, we found that the majority of the staff struggled

with the time to meet within their data teams and implement new instructional strategies.

Another concern was the time to cover the curriculum within one school year.

For question number five, we asked the data teams to create a smart goal for the data team

in the area of collaboration. We found that most centered around setting a time to meet and

discuss their data as a team. Most teams mentioned that they would like to come up with

strategies together that will drive student achievement.

Our last question asked the teachers about additional resources or professional

development they felt would support their data team. We found that the teachers wanted more

time to meet with their data teams. Within their current contract they are required to meet two

hours a month as a staff.

Data Team Survey Results

Analyzing the results of the survey we divided the twenty-eight questions into nine

specific categories and focused our analysis on the average percentage of teams that answered

(1) very true and (2) true to the groups of questions. The survey results are recorded by the

entire staff, math, science, social studies, foreign language and elective data teams.

Questions 1 – 3 pertain to the team setting meeting norms for the group to follow.

Overall 68% of the data teams create and set team norms. Mathematics 88%, Science 63%,


English 50%, Social Studies 60%, Foreign language 66.6%, and Electives 85% (see Appendix

C to view the survey questions).

Questions 4 – 7 pertain to the teams having a clearly given task/ objective/goal for the

meeting that relate directly to student learning goals. Overall 83% of the teams answered very

true or true. Mathematics 85.5%, Science 94%, English 75%, Social Studies 75%, Foreign

language 100%, and Electives 82% (see Appendix C to view the survey questions).

Questions 8 – 11 rate the overall communication among the team members. Overall

88.5% of the data teams rated their skills as being productive. Mathematics 67.25%, Science

75%, English 56.25%, Social Studies 60%, Foreign language 75%, and Electives 82% (see

Appendix C to view the survey questions).

Questions 12 & 13, the teams are rating their attachment to their data team. Overall

56% feel this process is beneficial. Mathematics 64.5%, Science 63%, English 35%, Social

Studies 50%, Foreign language 100%, and Electives 50.5% (see Appendix C to view the

survey questions).

Questions 14 & 15, the teams rate the impact the data teams have on their individual

instruction and professional practice. Overall 66.5% of the data teams feel this process has

made an effect on their instruction and professional practice. Mathematics 65%, Science 63%,

English 70%, Social Studies 30%, Foreign language 100%, and Electives 85.5% (see


Questions 16 – 18 ask the teams if the process has assisted the members to establish the

important student learning goals for their curriculum. Overall 74% answered very true or true.

Mathematics 66.3%, Science 84%, English 80%, Social Studies 53.3%, Foreign language

100%, and Electives 67% (see Appendix C to view the survey questions).


Questions 19 – 25 pertain to teams creating a rubric to score common assessments,

reviewing individual student work on a common assessment, or analyzing common assessment

data. Overall 72.14% of the teams agreed that most of their time is spent working

on/evaluating data for common assessments. Mathematics 39%, Science 56%, English

70.57%, Social Studies 22.86%, Foreign language 57.14%, and Electives 37.57% (see


Question 26 asked team members if they implemented academic interventions for

students who may be struggling with the content. Overall 70% provide numerous

interventions. Mathematics 75%, Science 76%, English 70%, Social Studies 40%, Foreign

language 100%, and Electives 72% (see Appendix C to view the survey questions).

Questions 27 & 28 pertain to a team member making a change in their instructional

practice that leads to changes in student learning and then sharing their strategy with their

team. Overall 78% of the members stated this does occur within their data team. Mathematics

69.5%, Science 94%, English 83.5%, Social Studies 50%, Foreign language 100%, and

Electives 71% (see Appendix C to view the survey question and Appendix D for the survey

results).

The final part of the survey asked the team members to divide 100% of their data team

time into seven categories; analyzing, comparing, or scoring student work samples, developing

common assessments, analyzing assessment data, discussing grade-level or school business

priorities, analyzing instructional practices, planning curriculum or instruction, and other (see

Appendix C to view the survey questions and Appendix E for the survey results).


Common Assessments

In March of 2012, after the initial district-wide common assessments were administered

in January of 2012, the administration developed a task for the data teams to analyze their

common assessment data (see Appendix F for the exam results).

1. The administrators identified the five lowest scoring questions on each of the common

assessments.

2. Each team received a description of the GLCE’s (grade level content expectations)

addressed on the common assessment.

3. The teams were asked to examine the answer distribution for each of the five identified

questions and discus the possible rationale for each answer option selected and record their

conclusions.

In October of 2012 the teams revisited their conclusion statements submitted in March

2012. The teams were asked to identify the month in which the five lowest scoring questions

would be/have been addressed in the curriculum. The teams were asked to identify strategies

used or could be used to address the needs of the students to become more proficient in these

areas of weakness.

In January of 2013 the teams were given the results of the district-wide common

assessments administered in January of 2013. The teams were asked to compare the five

weakest questions identified in March of 2012 and to compare the proficiency levels to the

current assessment and provide a statement explaining why the data team felt the proficiency

level changed either for the positive or negative.

Most of the data teams shared, that this task was very beneficial. The teams were able

to identify weaknesses in vocabulary, lack of experience with “real’ world problems, study


skills, and the lack of time spent on a particular topic may have attributed to low scores on the

first exam. Knowing and identifying these weak areas, have attributed to the teachers changing

their instruction to assist students to become more proficient on these questions for the current

exam. Due to the success of this activity, we will repeat this process for the end of the year

district wide common assessment administered in 2011-2012 and again in 2012-2013.


Chapter 5

Conclusions and Recommendations

Overview

Survey results indicated that the data team process at Malow Junior High School had

positive effects on student achievement; however, we concluded that collaboration must

become a more organic process rather than an administrative directive. The data team process

has to become a part of the teaching and learning culture. The research and data collected in

this study suggested that the staff improved instruction as evidenced by the scores of the test;

however, there is real need for the staff to see that the data collaboration has fostered greater

student achievement.

Conclusion

The data team framework at Malow Junior High fosters collaboration through the

professional development hours and staff meetings twice a month. The staff is given directives

from administration in terms of “what” data to analyze and “how” they will interpret the data.

While this is a sound research-based practice, teachers feel a disconnect between the work in

their data team and the activity in their classroom.

We have found that the professional collaboration has impacted teachers’ instructional

practice and improved test scores as based upon mid-term data. Some teachers lack the

understanding that collaboration amongst fellow team members contributed to the increase in

test scores.

Recommendations

These findings enhance our understanding of the data team process and collaboration

at Malow Junior High School. We recommend they continue with the process and expand to


include formative assessment. A second recommendation would be to increase teacher data

team autonomy by creating a culture in which data drives instruction and collaboration is the

norm. A third recommendation would be to implement the teacher lab concept within the data

team model.

Implications for Future Research

A further study could assess the cross sectional data at Malow Junior High School

utilizing district, state, and local assessments. By using the local assessment as the baseline

data to compare the students’ strengths and weakness within the other assessment given

throughout the school year. This will align the data with the current curriculum guidelines

teachers use to plan instruction. Future research should be concentrated on the investigation of

other junior high schools within Utica Community Schools.


References

Cawelti, G. and Protheroe, N. (2003). Supporting School Improvement: Lessons from

districts successfully meeting the challenge. Arlington, VA: Educational Research

Service, 2003.

Dufour, R. (2004). The best staff development is in the workplace, not it a workshop. The

Journal of the National Staff Development Council, 25(63-64).

Lambert, L. (2002). A framework for shared leadership. Educational Leadership, 59:8(37-

40).

Saunders, W.M., Goldenberg, C.N. & Gallimore, R. (2009). Increasing achievement by

focusing grade-level teams on improving classroom learning: A prospective, quasi-

experimental study of Title I schools. American Educational Research Journal. 46,

No.4, 1006.

Utica Community Schools. (2011). Annual Reports. Retrieved from

http://www.uticak12.org/districtinfo/di_annualreports.asp. November 30, 2011.

Wells, C. and Feun, L. (2007). Implementation of learning community principles: A study of

six high schools. NAASP Bulletin, 91:141. Retrieved October 14, 2011, from

http://bul.sagepub.com/content/91/2/141.


Appendix A

Personnel Research Consent Form

(To be completed by district personnel participating in research study)

Project Name: Malow Junior High School Professional Collaboration

Sponsoring Organization(s): Oakland University

Researchers: Kim Charland, Scott Palmer, Jason Larsen, Sofia Papastamatis

Telephone:

Project Location(s): Malow Junior High School

Participant’s Name:

Home Address: Telephone:

Participant/Parental Rights and AssurancesI have received a copy of the approved Consent Letter for the aforementioned research project. Having read the application I am familiar with the purpose, methods, scope, and intent of the research project.

Please check one of the following:

I am willing I am not willing to participate in the research project.

I understand that during the course of this project my responses will be kept strictly confidential and that none of the data released in this study will identify me by name or any other identifiable data, descriptions, or characterizations. Furthermore, I understand that I may discontinue my participation in this project at any time or refuse to respond to any questions to which I choose not to answer. I am a voluntary participant and have no liability or responsibility for the implementation, methodology, claims, substance, or outcomes resulting from this research project. I am also aware that my decision not to participate will not result in any adverse consequences or disparate treatment due to that decision.

I fully understand that this research is being conducted for constructive educational purposes and that my signature gives my consent to voluntarily participate in this project.

Participant’s Signature Date


Appendix B

Data Team: ___________________________________________________________

Member(s): ___________________________________________________________

___________________________________________________________

___________________________________________________________

____________________________________________________________

Please answer the following questions in your data teams. (Attach additional paper if needed)

1. What does collaboration look like in an ideal data team?

2. What does collaboration look like in your data team?

3. What are the strengths of your team?

4. What are the challenges of your data team?

5. Create a smart goal for you data team in the area of collaboration.

6. What additional resources/professional development/support does your data team need to meet your smart goal?


Appendix C

Data Team Survey

This survey is intended to help us, as a school, learn more about the type of work that has occurred in data teams so far this year and how we can best plan our data team work for the remainder of the school year. This survey is divided into two sections: the ways in which your team has managed data team meetings and the types of tasks in which your team has focused. Thank you for completing this survey in an honest and thoughtful manner.

Your grade level and primary subject area: ______________________________________Team-Based Collaboration: Meeting ManagementPlease indicate the extent to which each of the statements below is true by circling one of the four numbers using the following scale:

1 = Very true 2 = True 3 = Somewhat true 4 = Not true Question Number

1We have an agreed-upon set of meeting norms in our data team(for example, expectations for participant behaviors during meetings). 1 2 3 4

2 We follow our meeting norms consistently at data team meetings. 1 2 3 4

3 Our norms help us to have productive, effective conversations. 1 2 3 4

4 We have clear tasks to perform at our data team meetings. 1 2 3 4

5 Our tasks relate directly to student learning goals. 1 2 3 4

6 Our tasks are determined by consensus among our team members. 1 2 3 4

7 A large majority of our data team time (80% or more) is spent on tasks related to student learning goals. 1 2 3 4

8 During data team conversations, team members sometimes disagree about ideas or practices. 1 2 3 4

9 When team members disagree about ideas or practices, we tend to discuss those disagreements in depth 1 2 3 4

10 When I disagree with something a member of my data team has said, I almost always voice that disagreement 1 2 3 4

11 Within data team meetings, we try to avoid emotionally charged or difficult topic or conversations. 1 2 3 4

12 I feel a strong sense of attachment to my team. 1 2 3 4

13 If we were given the option of no longer meeting as a data team, I would still want to continue meetings. 1 2 3 4

14 I have improved as a classroom teacher as a result of the conversations and work we have done in our data teams. 1 2 3 4

15 I have made changes to my teaching practices as a result of the work that we have done as a data team. 1 2 3 4


Team-Based Collaboration: Teaching and Learning Tasks

Please indicate the extent to which each of the statements below is true by circling one of the four numbers using the following scale.

1 = Very true 2 = True 3 = Somewhat true 4 = Not true Question Number

16 My data team has worked to define the most important student learning goals in our content areas. 1 2 3 4

17If you were to ask each of the members of my data team to list the most important student learning goals in our content area independently, we would all come up with nearly identical lists.

1 2 3 4

18 I could explain to a parent, in simple language, the most important grade level learning goals for his or her child in the content areas I teach. 1 2 3 4

19 In my data team, we regularly (at least monthly) administer common assessments to our students (in other words, all students complete the same assessment). 1 2 3 4

20 In my data team, we regularly use rubrics to score students’ common assessments. 1 2 3 4

21 In my data team, we have developed our own rubrics to help us score students’ common assessments. 1 2 3

4

22 As a data team, we regularly (at least monthly) assess student work samples as a team. 1 2 3 4

23As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 1 2 3 4

24I adjust the instructional practices in my classroom based on my students’ performance on common assessments. 1 2 3 4

25As a data team, we regularly (at least monthly) make adjustments to our instructional practices across all classrooms based on students’ performance on common assessments. 1 2 3 4

26 I have implemented numerous academic interventions in my classroom for struggling students. 1 2 3 4

27As an individual teacher, I regularly think about how my specific instructional practices affect student learning and how changes in my instructional practices might lead to changes in students learning.

1 2 3 4

28As a data team, we regularly discuss how our specific instructional practices affect student learning and how changes in our instructional practices might lead to changes in student learning.

1 2 3 4


Review the tasks in the following chart and list the percent of time your data team spends on each of these tasks. Your total should add up to 100%.

Percent of Time Spent on Taskat Data Team Meetings

Task

Analyzing, comparing, or scoring student work samples.

Developing common assessments

Analyzing assessment data

Discussing grade-level or school business priorities (for example, field trips, scheduling, and so on)

Analyzing instructional practices (for example, discussing videotaped lessons, critiquing an instructional strategy)

Planning curriculum or instruction

Other (please specify):________________

Comments:


Appendix D

Overall Data n=43 teachers 1 V. True 2 TRUE 3 S. True 4 N. TrueWe have an agreed-upon set of meeting norms in our data team (expectations for behaviors) 16 37% 12 28% 8 19% 5 12%We follow our meeting norms consistently at data team meetings. 16 37% 13 30% 8 19% 5 12%Our norms help us to have productive, effective conversations. 16 37% 16 37% 7 16% 3 7%We have clear tasks to perform at our data team meetings. 18 42% 19 44% 4 9% 1 2%Our tasks relate directly to student learning goals. 19 44% 18 42% 3 7% 2 5%Our task are determinded by consensus amoung our team members 19 44% 14 33% 5 12% 5 12%A large majority of our data time(80% +) is spent on tasks related to student learning goals. 16 37% 20 47% 5 12% 2 5%During data team conversations, team members sometimes disagree about ideas or practices 10 23% 22 51% 8 19% 3 7%When team members disagree about ideas or practices, we tend to discuss those diagreements in depth. 10 23% 21 49% 10 23% 1 2%When I disagree with something a member of my data team has said, I almost always voice that disagreement. 10 23% 18 42% 13 30% 1 2%Within data team meetings, we try to avoid emotionally charged or difficult topic or conversations. 8 19% 17 40% 12 28% 5 12%I feel a strong sense of attachment to my team. 14 33% 9 21% 14 33% 6 14%If we were given the option of no longer meeting as a data team, I would still want to continue meetings. 13 30% 12 28% 9 21% 9 21%I have improved as a classroom teacher as a result of the conversations and work we have done in our data teams.

12 28% 16 37% 10 23% 3 7%I have made changes to my teaching practices as a result of the work that we have done as a data team. 12 28% 17 40% 10 23% 3 7%My data team has worked to define the most important student learning goals in our content areas. 14 33% 20 47% 6 14% 2 5%If you were to ask each of the members of my data team to list the most important student learning goals in our content area independently, we would all come up with nearly identical lists. 6 14% 17 40% 18 42% 2 5%

I could explain to a parent, in simple language, the most important grade level learning goals for his or her child in the content areas I teach. 23 53% 15 35% 3 7% 2 5%

In my data team, we regularly (at least monthly) administer common assessments to our students (in other words, all students complete the same assessment). 13 30% 15 35% 7 16% 8 19%

In my data team, we regularly use rubrics to score students’ common assessments. 9 21% 10 23% 12 28% 11 26%In my data team, we have developed our own rubrics to help us score students' common assessments 7 16% 9 21% 13 30% 13 30%

As a data team, we regularly (at least monthly) assess student work samples as a team. 3 7% 11 26% 12 28% 17 40%

As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 3 7% 19 44% 11 26% 10 23%I adjust the instructional practices in my classroom based on my students’ performance on common assessments. 15 35% 21 49% 3 7% 3 7%

As a data team, we regularly (at least monthly) make adjustments to our instructional practices across all classrooms based on students’ performance on common assessments. 9 21% 12 28% 12 28% 9 21%

I have implemented numerous academic interventions in my classroom for struggling students. 13 30% 17 40% 9 21% 3 7%

As an individual teacher, I regularly think about how my specific instructional practices affect student learning and how changes in my instructional practices might lead to changes in students learning. 27 63% 11 26% 3 7% 2 5%

As a data team, we regularly discuss how our specific instructional practices affect student learning and how changes in our instructional practices might lead to changes in student learning. 16 37% 13 30% 11 26% 2 5%

Task

% of Time Spent on Task

Analyzing, comparing, or scoring student work samples 15%Developing common assessments 20%Analyzing assessment data 20%Discussing grade-level or school business priorities 8%Analyzing instructional practices 13%Planning curriculum or instruction 38%Other 5%

Social Studies n= 5 teachers 1 V. True 2 TRUE 3 S. True 4 N. TrueWe have an agreed-upon set of meeting norms in our data team (expectations for behaviors) 2 40% 0 0% 1 20% 2 40%We follow our meeting norms consistently at data team meetings. 2 40% 1 20% 1 20% 1 20%

Our norms help us to have productive, effective conversations. 1 20% 3 60% 0 0% 1 20%We have clear tasks to perform at our data team meetings. 2 40% 1 20% 2 40% 0 0%

Our tasks relate directly to student learning goals. 2 40% 2 40% 1 20% 0 0%Our task are determinded by consensus amoung our team members 2 40% 2 40% 1 20% 0 0%

A large majority of our data time(80% +) is spent on tasks related to student learning goals. 2 40% 2 40% 1 20% 0 0%During data team conversations, team members sometimes disagree about ideas or practices 3 60% 1 20% 1 20% 0 0%

When team members disagree about ideas or practices, we tend to discuss those diagreements in depth. 1 20% 3 60% 1 20% 0 0%When I disagree with something a member of my data team has said, I almost always voice that disagreement. 3 60% 0 0% 2 40% 0 0%

Within data team meetings, we try to avoid emotionally charged or difficult topic or conversations. 0 0% 1 20% 2 40% 2 40%I feel a strong sense of attachment to my team. 1 20% 1 20% 2 40% 1 20%

If we were given the option of no longer meeting as a data team, I would still want to continue meetings. 0 0% 3 60% 0 0% 2 40%

0 0% 2 40% 2 40% 1 20%

I have made changes to my teaching practices as a result of the work that we have done as a data team. 0 0% 1 20% 4 80% 0 0%

My data team has worked to define the most important student learning goals in our content areas. 0 0% 3 60% 2 40% 0 0%If you were to ask each of the members of my data team to list the most important student learning goals in our content area independently, we would all come up with nearly identical lists. 0 0% 0 0% 3 60% 2 40%


1 20% 0 0% 1 20% 3 60%



As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 0 0% 2 40% 0 0% 3 60%

0 0% 3 60% 2 40% 0 0%


I have implemented numerous academic interventions in my classroom for struggling students. 0 0% 2 40% 3 60% 0 0%As an individual teacher, I regularly think about how my specific instructional practices affect student learning and how changes in my instructional practices might lead to changes in students learning. 4 80% 0 0% 1 20% 0 0%


Task


Analyzing, comparing, or scoring student work samples 36%

Developing common assessments 12%

Analyzing assessment data 24%

Discussing grade-level or school business priorities 16%

Analyzing instructional practices 12%

Planning curriculum or instruction 16%

Other 0%


Mathematics n= 8 teachers 1 V. True 2 TRUE 3 S. True 4 N. TrueWe have an agreed-upon set of meeting norms in our data team (expectations for behaviors) 4 50% 3 38% 0 0% 1 13%We follow our meeting norms consistently at data team meetings. 4 50% 3 38% 0 0% 1 13%

Our norms help us to have productive, effective conversations. 4 50% 3 38% 1 13% 0 0%We have clear tasks to perform at our data team meetings. 2 25% 5 63% 1 13% 0 0%

Our tasks relate directly to student learning goals. 3 38% 4 50% 0 0% 1 13%

Our task are determinded by consensus amoung our team members 4 44% 3 33% 1 11% 1 11%A large majority of our data time(80% +) is spent on tasks related to student learning goals. 3 33% 5 56% 0 0% 1 11%

During data team conversations, team members sometimes disagree about ideas or practices 0 0% 6 67% 3 33% 0 0%When team members disagree about ideas or practices, we tend to discuss those diagreements in depth. 1 13% 4 50% 3 38% 0 0%

When I disagree with something a member of my data team has said, I almost always voice that disagreement. 0 0% 5 63% 3 38% 0 0%



4 44% 1 11% 3 33% 1 11%

I have made changes to my teaching practices as a result of the work that we have done as a data team. 4 50% 2 25% 1 13% 1 13%My data team has worked to define the most important student learning goals in our content areas. 1 11% 5 56% 2 22% 1 11%If you were to ask each of the members of my data team to list the most important student learning goals in our content area independently, we would all come up with nearly identical lists. 0 0% 4 44% 5 56% 0 0%





As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 0 0% 5 56% 3 33% 1 11%I adjust the instructional practices in my classroom based on my students’ performance on common assessments. 2 25% 5 63% 0 0% 1 13%



2 25% 2 25% 4 50% 0 0%

Task

% of Time

Spent on Task



Foreign Language n = 4 teachers 1 V. True 2 TRUE 3 S. True 4 N. TrueWe have an agreed-upon set of meeting norms in our data team (expectations for behaviors) 2 50% 1 25% 1 25% 0 0%We follow our meeting norms consistently at data team meetings. 2 50% 0 0% 2 50% 0 0%Our norms help us to have productive, effective conversations. 2 50% 1 25% 1 25% 0 0%We have clear tasks to perform at our data team meetings. 4 100% 0 0% 0 0% 0 0%Our tasks relate directly to student learning goals. 4 100% 0 0% 0 0% 0 0%Our task are determinded by consensus amoung our team members 4 100% 0 0% 0 0% 0 0%A large majority of our data time(80% +) is spent on tasks related to student learning goals. 4 100% 0 0% 0 0% 0 0%During data team conversations, team members sometimes disagree about ideas or practices 1 25% 2 50% 1 25% 0 0%When team members disagree about ideas or practices, we tend to discuss those diagreements in depth. 3 75% 0 0% 1 25% 0 0%

2 50% 2 50% 0 0% 0 0%



I have improved as a classroom teacher as a result of the conversations and work we have done in our data teams. 1 33% 2 67% 0 0% 0 0%

I have made changes to my teaching practices as a result of the work that we have done as a data team. 2 50% 2 50% 0 0% 0 0%My data team has worked to define the most important student learning goals in our content areas. 4 100% 0 0% 0 0% 0 0%

2 50% 2 50% 0 0% 0 0%



In my data team, we regularly use rubrics to score students’ common assessments. 0 0% 2 50% 0 0% 2 50%In my data team, we have developed our own rubrics to help us score students' common assessments 2 50% 0 0% 0 0% 2 50%As a data team, we regularly (at least monthly) assess student work samples as a team. 0 0% 2 50% 0 0% 2 50%As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 0 0% 2 50% 0 0% 2 50%I adjust the instructional practices in my classroom based on my students’ performance on common assessments. 4 100% 0 0% 0 0% 0 0%




Task



Science n = 8 teachers 1 V. True 2 TRUE 3 S. True 4 N. TrueWe have an agreed-upon set of meeting norms in our data team (expectations for behaviors) 3 38% 2 25% 3 38% 0 0%We follow our meeting norms consistently at data team meetings. 3 38% 2 25% 3 38% 0 0%Our norms help us to have productive, effective conversations. 3 38% 2 25% 3 38% 0 0%We have clear tasks to perform at our data team meetings. 4 50% 4 50% 0 0% 0 0%Our tasks relate directly to student learning goals. 5 63% 3 38% 0 0% 0 0%Our task are determinded by consensus amoung our team members 4 50% 3 38% 1 13% 0 0%A large majority of our data time(80% +) is spent on tasks related to student learning goals. 3 38% 4 50% 1 13% 0 0%During data team conversations, team members sometimes disagree about ideas or practices 2 25% 5 63% 1 13% 0 0%When team members disagree about ideas or practices, we tend to discuss those diagreements in depth. 1 13% 6 75% 1 13% 0 0%When I disagree with something a member of my data team has said, I almost always voice that disagreement. 1 13% 4 50% 3 38% 0 0%Within data team meetings, we try to avoid emotionally charged or difficult topic or conversations. 4 50% 1 13% 3 38% 0 0%I feel a strong sense of attachment to my team. 4 50% 1 13% 1 13% 2 25%If we were given the option of no longer meeting as a data team, I would still want to continue meetings. 4 50% 1 13% 1 13% 2 25%

3 38% 2 25% 3 38% 0 0%I have made changes to my teaching practices as a result of the work that we have done as a data team. 3 38% 2 25% 2 25% 1 13%My data team has worked to define the most important student learning goals in our content areas. 3 38% 5 63% 0 0% 0 0%If you were to ask each of the members of my data team to list the most important student learning goals in our content area independently, we would all come up with nearly identical lists. 1 13% 3 38% 4 50% 0 0%I could explain to a parent, in simple language, the most important grade level learning goals for his or her child in the content areas I teach. 4 50% 4 50% 0 0% 0 0%In my data team, we regularly (at least monthly) administer common assessments to our students (in other words, all students complete the same assessment). 5 63% 3 38% 0 0% 0 0%In my data team, we regularly use rubrics to score students’ common assessments. 3 43% 1 14% 3 43% 0 0%In my data team, we have developed our own rubrics to help us score students' common assessments 1 13% 2 25% 3 38% 2 25%As a data team, we regularly (at least monthly) assess student work samples as a team. 1 13% 1 13% 3 38% 3 38%As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 2 25% 4 50% 1 13% 1 13%I adjust the instructional practices in my classroom based on my students’ performance on common assessments. 5 63% 3 38% 0 0% 0 0%

2 25% 1 13% 4 50% 1 13%I have implemented numerous academic interventions in my classroom for struggling students. 3 38% 3 38% 2 25% 0 0%

6 75% 2 25% 0 0% 0 0%

2 25% 5 63% 1 13% 0 0%

Task


Analyzing, comparing, or scoring student work samples 15%Developing common assessments 43%Analyzing assessment data 34%Discussing grade-level or school business priorities 10%Analyzing instructional practices 23%Planning curriculum or instruction 14%Other


English n = 10 teachers 1 V. True 2 TRUE 3 S. True 4 N. TrueWe have an agreed-upon set of meeting norms in our data team (expectations for behaviors) 2 20% 4 40% 3 30% 1 10%We follow our meeting norms consistently at data team meetings. 3 30% 3 30% 2 20% 2 20%Our norms help us to have productive, effective conversations. 3 30% 4 40% 2 20% 1 10%

We have clear tasks to perform at our data team meetings. 4 40% 5 50% 1 10% 0 0%Our tasks relate directly to student learning goals. 3 30% 5 50% 2 20% 0 0%Our task are determinded by consensus amoung our team members 3 30% 2 20% 2 20% 3 30%

A large majority of our data time(80% +) is spent on tasks related to student learning goals. 3 30% 5 50% 2 20% 0 0%During data team conversations, team members sometimes disagree about ideas or practices 2 20% 4 40% 2 20% 2 20%When team members disagree about ideas or practices, we tend to discuss those diagreements in depth. 3 33% 2 22% 4 44% 0 0%When I disagree with something a member of my data team has said, I almost always voice that disagreement.

2 20% 3 30% 5 50% 0 0%

Within data team meetings, we try to avoid emotionally charged or difficult topic or conversations. 2 20% 4 40% 4 40% 0 0%

I feel a strong sense of attachment to my team. 1 10% 2 20% 6 60% 1 10%If we were given the option of no longer meeting as a data team, I would still want to continue meetings. 2 20% 2 20% 4 40% 2 20%I have improved as a classroom teacher as a result of the conversations and work we have done in our data teams. 2 20% 5 50% 3 30% 0 0%


My data team has worked to define the most important student learning goals in our content areas. 4 40% 4 40% 2 20% 0 0%

2 20% 5 50% 3 30% 0 0%

6 60% 3 30% 1 10% 0 0%

1 10% 6 60% 2 20% 1 10%


As a data team, we regularly (at least monthly) assess student work samples as a team. 2 20% 3 30% 3 30% 2 20%As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 1 9% 5 45% 4 36% 1 9%I adjust the instructional practices in my classroom based on my students’ performance on common assessments. 3 30% 6 60% 1 10% 0 0%




Task

% of Time

Spent on Task



Electives n = 7 teachers 1 V. True 2 TRUE 3 S. True 4 N. TrueWe have an agreed-upon set of meeting norms in our data team (expectations for behaviors) 3 50% 2 33% 0 0% 1 17%

We follow our meeting norms consistently at data team meetings. 3 43% 3 43% 0 0% 1 14%

Our norms help us to have productive, effective conversations. 3 43% 3 43% 0 0% 1 14%

We have clear tasks to perform at our data team meetings. 2 29% 4 57% 0 0% 1 14%

Our tasks relate directly to student learning goals. 2 29% 4 57% 0 0% 1 14%

Our task are determinded by consensus amoung our team members 2 29% 4 57% 0 0% 1 14%

A large majority of our data time(80% +) is spent on tasks related to student learning goals. 1 14% 4 57% 1 14% 1 14%

During data team conversations, team members sometimes disagree about ideas or practices 2 29% 4 57% 0 0% 1 14%

When team members disagree about ideas or practices, we tend to discuss those diagreements in depth. 1 14% 5 71% 0 0% 1 14%

When I disagree with something a member of my data team has said, I almost always voice that disagreement. 2 29% 4 57% 0 0% 1 14%

Within data team meetings, we try to avoid emotionally charged or difficult topic or conversations. 1 14% 4 57% 1 14% 1 14%

I feel a strong sense of attachment to my team. 2 29% 2 29% 2 29% 1 14%

If we were given the option of no longer meeting as a data team, I would still want to continue meetings. 1 14% 2 29% 2 29% 2 29%I have improved as a classroom teacher as a result of the conversations and work we have done in our data teams. 2 29% 4 57% 0 0% 1 14%


My data team has worked to define the most important student learning goals in our content areas. 2 29% 4 57% 0 0% 1 14%If you were to ask each of the members of my data team to list the most important student learning goals in our content area independently, we would all come up with nearly identical lists. 1 14% 2 29% 3 43% 1 14%



In my data team, we regularly use rubrics to score students’ common assessments. 1 14% 1 14% 3 43% 2 29%

In my data team, we have developed our own rubrics to help us score students' common assessments 1 17% 2 33% 2 33% 1 17%


As a data team, we regularly (at least monthly) analyze data from students’ common assessments. 0 0% 1 14% 3 43% 3 43%

I adjust the instructional practices in my classroom based on my students’ performance on common assessments. 1 14% 4 57% 1 14% 1 14%


I have implemented numerous academic interventions in my classroom for struggling students. 3 43% 2 29% 1 14% 1 14%As an individual teacher, I regularly think about how my specific instructional practices affect student learning and how changes in my instructional practices might lead to changes in students learning.

4 57% 1 14% 1 14% 1 14%


Task

% of Time

Spent on Task

Analyzing, comparing, or scoring student work samples 12%Developing common assessments 10%Analyzing assessment data 13%Discussing grade-level or school business priorities 20%Analyzing instructional practices 24%Planning curriculum or instruction 54%Other


Appendix E

Overall Math Science English Social Studies

Foreign Language

Electives

Analyzing, comparing, or

16% 13% 15% 12% 36% 10% 12%


scoring student work samplesDeveloping common assessments

20% 14% 43% 12% 12% 35% 10%

Analyzing assessment data

20% 12% 34% 20% 24% 15% 13%

Discussing grade-level or school business priorities

8% 4% 10% 7% 16% 8% 20%

Analyzing instructional practices

13% 7% 23% 12% 12% 8% 24%

Planning curriculum or instruction

18% 54% 14% 41% 16% 30% 54%

Other 6% 6% 5%

Appendix F

DataExam % proficient on the

5 low scoring questions

% of increase or decrease

% proficient on the 5 low scoring

questions


2011-2012 exam 2012-2013 examMath 7 This exam could not be compared due to changesAdvanced Math 7 50% (+) 6 % 56%Math 8 37% (+) 15% 52%Geometry 49% (+) 7% 56%Algebra I 62% (+) 25% 87%Science 7 35% (+) 42% 77%Science 8 35% (+) 40% 75%Science 9 (Chemistry) 22% (+) 19% 41%Science 9 (Physics) 20% (+) 25% 45%Geography 34% (+) 13% 47%U.S. History 8 41% (+) 35% 76%World History 51% (+) 21% 72%English 7 This exam could not be compared due to changesEnglish 8 44% (+) 17% 61%English 9 56% (+) 26% 82%Spanish I This exam could not be compared due to no prior dataFrench I 49% (+) 24% 73%German I 56% (-) 25% 31%

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