Vocal Coach Agreement

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					Data! Dialogue!
  Decisions!
 Rebecca Stinson, MA
        2010

   www.robinfogarty.com
Data!
Dialogue!
Decisions!




             2
Data! Dialogue! Decisions! Pre/Post Questions

 1. What is a team goal for today’s topic:
       “Data Driven Instruction”
 2. What is your biggest concern with using data?
 3. What data set will you bring?
 4. How will your team share this information with
 your school teams following the workshop day?
 5. What would be the most helpful thing the presenters
 could do to for your team concerning the use of data?
     Gap Facts

Reference pages 6-10
 The Three Musketeers

             Find two others
Share how each of you has used data of
    some kind during the past week…
The Three Musketeers
   Exchange email addresses

  Commit to one communication
   before the next session.
Mediated Journal (1)
   Data! Dialogue!
   Decisions!




    Name
    Date
Mediated Journal
Take Away Window
Mediated Journal
    Agree/Disagree
        Data! Data! Data!
Agree or Disagree with the following statement:

1. Data are plural.
2. Data reveal patterns, trends and gaps.
3. I haven’t been on a data in a long time.
4. Data represented in graphs make the information easily accessible.
5. To disaggregate data, aggravate some.
6. Data-driven decisions, through data dialogues, get the best results.
7. The following terms are ranked in order of importance in managing data:
          #1 Gather         #2 Analyze      #3 Interpret
8. I say tomato not tomato? I say data not data?
9. Hard data is more reliable and more valid than soft data.
10. Technology helps to manage information /data.
           Results: Schmoker
“…thoughtfully established, desired end-product, as evidence that
something has worked (or has not worked). p.3

“The average school puts faith in process, not in results.” p.4



                    Results
                  Managed Data
               Meaningful Teamwork
                Measurable Goals

    Results: Keys to Continuous School Improvement Mike Schmoker 1996 ASCD
                  Managed Data
“What gets measured, gets done”.
                            Peters in Schmoker p.29



  “The monitoring of effective instruction is the heart of
  effective instruction.”
                            Lortie in Schmoker p.29




Source: Results: Keys to Continuous School Improvement-Mike Schmoker 1996 ASCD
 Meaningful Teamwork
Edison: “multiplier effect”… placed his team of inventors
near each other to encourage them to consult, share,
reflect and energize each other.

“Collegiality among teachers, a measured by frequency of
communication, mutual support, help, etc. was a strong
indicator of implementation success. Virtually every
research study on the topic has found that to be the
case.”
                                    Fullan in Schmoker, p.10.


“Good teamwork among grade level, department, school
and ad hoc teams will give us results we once only
dreamed of.”          Little in Schmoker, p. 16

Source: Results: Keys to Continuous School Improvement-Mike Schmoker 1996
ASCD
  Measurable Goals
“We did not find a single case in the literature where
student learning increased but had not been a
central goal.”
            Joyce, Wolfe, Calhoun in Schmoker p.17



“When specific goals do not exist, one-shot staff
development…often fill the void. Reporting that an
entire staff learned a new program is easy…easier
still is presuming this new practice will benefit
students.” Schmoker p.25

       Source: Results: Keys to Continuous School Improvement-Mike Schmoker 1996
ASCD
       Data-Savvy Graph



Low               Medium                     High

 Place the code letter along the continuum
 *S . . . Self
 *L . . . Leadership
 *F . . . Faculty
 *S . . . Students
 The Breakthrough Strategy
                     Rapid Results!
“…where significant improvement has happened, it has happened
rapidly… Innovations can be implemented and gains seen in student
achievement within a year”
                                       Joyce, Wolfe, Calhoun 1993



“They (Joyce,Wolfe, Calhoun) insist… that the key is to pay attention
to already existing approaches that work and work fast”
                               Schmoker p.51
                 The Card Game
Create four cards … one for each element …
Place them in the order of importance for PLC work


  Managed Data
  Meaningful Teams
  Measurable Goals
  Mapped Results
                                                Schmoker
       “Simpsons”
   Using the data well…
       both summative (tests)
       and formative
       (walkthroughs and
lookfors)
      TAG
Toss Around the
Group
     The Data Book

What? What else?
So, What?
Now, What?
Debrief the Coaches’ Data Story with the 4 questions.
   Four Key Questions
What Data Do We Have?
What Else Do We Need To Know?
So, What Does the Data Reveal?
Now, What instruction will work?
  Tiny Transfer
Book       Data!
         Dialogue!
          Decision!


           Name
Data! Dialogue! Decisions!
        Data! What?
  Data! Dialogue!
Decisions!
      Dialogue! So, What?
Data! Dialogue! Decisions
   Decisions! Now, What?
Data! Dialogue! Decisions
      SMART Goal
      S
      M
      A
      R
      T
 Data Book
What?
So, What?
Now, What?
Who will do
what, when?
READERS’ THEATER

 THE COACHES’ STORY



    Robin Fogarty and Brian Pete
The Coaches’ Meeting…
The following conversation between three basketball coaches
illustrates the difference between process-driven and
results-driven solutions.


Varsity Coach: I have been looking over the stats for this year
             and comparing them to last year.
             You know what I see?

Assistant Coach: We were better last year.

Varsity Coach: Yes, but do you know why?

Assistant Coach: We won more games last year.

Varsity Coach: Brilliant observation . . . I know we won more
             games last year, but I noticed something else in
             the statistics. Something besides wins and losses.

Freshman Coach: I think what you mean is rebounding. We
               out-rebounded the other team in every game
               we won.
Varsity Coach:   Exactly, rebounding. This year we are not
               rebounding. Get rebounds and you get the wins,
               it’s that simple.
Assistant Coach: We had taller players last year. Rebounds come
               off the rim, the rim is high, tall players have an
               advantage. Tall players make better rebounders.
               You tell me how to get kids to grow and our
                rebounding will improve. When our rebounding
         improves, we’ll win more games.
Freshman Coach: But, when you look deeper into the statistics,
               you see that our big guys always rebounded about
               equal to the other team’s big guys. It was our
               guards who got the rebound in the victories. The
               short, quick players get the rebounds
        that matter.
Assistant Coach: I’d like to see who got the rebounds in the
               losses, and I would also like to see how other
               teams rebounded against us versus how they
               rebounded against other people.
Freshman Coach: OK, sure, and then I could go back a couple
              years and compare those numbers to the numbers
              of the best teams in the state by running an
              analysis on a spreadsheet program on my computer.
Varsity Coach: Now wait a minute. We want to improve our win–loss
         record. I don’t think we have to crunch numbers
                like Price Waterhouse. I have been coaching for 18 years,
                you about 13, and you, 10. If, between us, we can’t figure
                out how to improve our team, we are in sorry shape.

Assistant Coach: What do you mean, Coach?

Varsity Coach: Let’s look at the numbers that tell us when we rebounded
         well and when we didn’t. Let’s look at our practice
                schedule and see when we worked on rebounding. If we do
                not emphasize rebounding in practice, how can we expect
                the kids to do it during the game?

Freshman Coach: I got a great drill to teach technique, and the kids can do
             the drill on their own.

Assistant Coach: I think if we tell the kids what we want them to do, really
        make it clear why it is important to the team . ..

Varsity Coach:    Until they understand, let’s have them set some goals.
                 Every kid has to get five rebounds a game.
Assistant Coach: In the next game?

Varsity Coach: OK, maybe too much too soon. How about, three games
         from now every kid will get at least five rebounds a game
          and we, as a team will out-rebound every opponent the
          rest of the season.

Freshman Coach: Well, we said rebounding leads to victories, so this seems
       like a plan.
Assistant Coach: If it doesn’t get the results we want, then we’ll try
        something else. The next day the three coaches meet in
       their office.

Freshman Coach: Hey, remember what we were talking about yesterday?
       About, how we were going to work on rebounding in
       practice?
Assistant Coach: Work on rebounding with all the players, not just our
                big guys.
Varsity Coach:   Yeah, what about it?

Freshman Coach: I went back through my files and I found the
handouts that I got at the coaches clinic.

Assistant Coach: I can’t believe you found it in that trash heap
you call a office.

Freshman Coach: I have copies for all of us and I underlined
the key parts, the parts that I think will get the best results.

Varsity Coach: Let’s find time this week to sit down and look
at this together. We have to really come to an
          agreement on what we are trying to teach and
          how we are trying to teach it.

Assistant Coach: Like a book study?
Varsity Coach: Call it a book study, call it a professional learning
         community, call it three old coaches trying to
         learn some new tricks. Let’s just agree to make
         time to work on this one goal.

Assistant Coach: I have everything but time . . . can we talk online?

Varsity Coach:   Excuse me?

Assistant Coach: I am getting my Masters online. It’s great. I set my own
        schedule, my instructor can monitor my progress . . .
        online is where it’s at for some of us.

Freshman Coach: I can handle some emails but we have to schedule a
       meeting about teaching rebounding. We have to come
       to an agreement and put it into action.
Varsity Coach: So, we agree to meet, and you agree to show us this
         rebounding drill.

Assistant Coach: OK, but do you mind if I print off a couple of things
        from my online course? I have something that I’m
        sure will help.

Freshman Coach: Every little bit helps.

Varsity Coach: I think we all agree that we have to leave nothing to
         chance if we are going reach these kids.




                 Brian M. Pete & Catherine A. Sambo
                 Adapted from Data! Dialogue! Decisions! The Data Difference
                 Robin Fogarty & Associates, Publisher 2004
  Data Book
What? What else?
  So, What?
 Now, What?
Four Key Questions
 Debrief the Coaches’ Data Story with the 4 questions.



     1.What?
     2.What Else?
     3.So, What?
     4.Now, What?
Four Key Questions
Debrief the Coaches’ Data Story with the 4 questions.



          What?
       Wins? Losses?
        Rebounds?
Four Key Questions
 Debrief the Coaches’ Data Story with the 4 questions.



      What Else?
Guards….Shorter Players
Four Key Questions
Debrief the Coaches’ Data Story with the 4 questions.



       So, What?
More explicit practice
might be needed to
improve in this area…
   Four Key Questions
     Now, What?
 Goal: Increase # of rebounds
       for more wins

SMART Goal: Guards increase rebounds
   by 20% in next five games for
   more wins
Four Key Questions
 Now, What?
 Goal: Increase # of rebounds
         for more wins

 SMART Goal
 Specific…Guards
 Measurable… increase rebounds by 20%
 Attainable…revised goal
 Results-oriented…more wins
 Time Bound…next five games
  Intervention
What? What else?
  So, What?
 Now, What?
 Who will do
 when?
   Data and RTI: Response to Intervention
            Questions to think About…


What is the intervention?
What is the specific strategy that they will practice?
What is the drill and who and how long will they do it?
Who will do What? When?
Where is the fidelity of implementation?
How will we know when they know it?
What will we do if they don’t? or do?
Where is the SMART GOAL?
     Heads Together
 Managed Data       Meaningful Teams Measureable Goals
What do you do to work on rebounding?
 Varsity Coach: I cover the basket…no balls go in,
every ball rebounds for the taking!
 Assistant Coach: I use the relay drill before, during and after
the practice session; every day, everybody!
 Freshman Coach: I have a contest and the players compete
for rebounds during the scrimmage games!
 Heads Together
 Which intervention or combination will we use?
Who? When? How often? How long?
How do we account for fidelity of implementation?


  Varsity Coach: I cover the basket…no balls go in,
     every ball rebounds for the taking!
  Assistant Coach: I use the relay drill before, during and after
     the practice session; every day, everybody!
 Freshman Coach: I have a contest and the players compete
    for rebounds during the scrimmage games; winner
 Four Key Questions
  Now, What?
Specific Interventions:
    Drill #1: Cover basket;
              ball always rebounds
    Drill #2: Rotation for rebounds
    Drill #3: How to “block and pivot”
 How Do We Do It?


Another Example!
     (off the top of the head…)
Data! Dialogue! Decisions
         WHAT?

What Data Do We Have?
Middle School- 600 students
Grades 6-8; 4000 referrals
 Data! Dialogue! Decisions
       WHAT ELSE?
Dialogue: What Else Do We Know?
•Increase of 20% referrals from
   previous year
•New principal
•New zoning
•“Study hall atmosphere”
  Data! Dialogue! Decisions
        WHAT ELSE?
Dialogue: What Else Do We Need To
Know?
•Who? Students? Teachers?
•When? Hours? Days? Weeks?
Months?
•Where? Locations?
•Consequences?
  Data! Dialogue! Decisions
         SO, WHAT?
Dialogue: So, What Does the Data
Reveal?
 •Re-zoning add 168 ELL
 •65% referrals from 5 teachers
    Data! Dialogue! Decisions
           SO, WHAT?
Dialogue: So, What Can We Infer?
 •Detention is not a real consequence
 •Some teachers interpret differently
 •Some not using referral system
 •Consequences need to be more
 rigorous
  Data! Dialogue! Decisions
        NOW, WHAT?
Now, What interventions will work?
 •Review the referral system
 •PD on discipline
 •Use detention for additional
 instructional time…
     Data! Dialogue! Decisions
           NOW, WHAT?
Decisions: Now, What is the SMART goal(s)?
    Reduce referrals each quarter by 20%
         •S pecific… Reduce referrals
         •M easurable… by 20%
         •A ttainable… 20% over 10 weeks is reasonable
         •R esults-Oriented…evidence of better classroom
                 management/discipline with fewer referrals
         •T arget Date … each quarter
  Data! Dialogue! Decisions
        NOW, WHAT?
Decisions: Now, What professional
development supports the instructional
goal?
      •Discipline Strategies
      •Classroom Management
      •Literacy Comprehension
      Strategies
      •Math Literacy Strategies
  Data! Dialogue! Decisions
        NOW, WHAT?
Decisions: Now, Who will do What,
When?
 Staff Development Team will
 arrange for professional
 development about discipline
 ideas, literacy and math strategies
 for detention room
Four Key Questions
  1.What?
  2.What Else?
  3.So, What?
  4.Now, What?
    Your Turn!
   Use your own data you
brought with you or… some piece
of data that you just know about
your district, school or class…
off the top of your head.

Fold and Label Four Sections
Data! Dialogue! Decisions!
 What?
   What Else?
         So, What?
            Now, What?
 Data: What
  Code the Data…
High Points/Strengths       +
Low Points/Weaknesses           -
Trends/Patterns       -->
Gaps   ?
Striking Points   *
Surprises   !
      Data: What
       Code the Data
High Points/Strengths…circle
Low Points/Weaknesses… box
Trends/Patterns… arrow
Gaps…?
Striking Points… *
Surprises…!
Significant data…[ ]
     Data: What
        Code the Data…
High Points/Strengths…green
Low Points/Weaknesses… red
Trends/Patterns…yellow
Gaps…blue
Striking Points…magenta
Surprises…gray
Significant data…orange
    Data: What
      What Did You Find?
Describe the existing data in
some detail. Connect to
previous data or to an existing
concern. Observe and
describe as best you can at
first glance.
        Dialogue:
       WHAT ELSE?
   What else do you know?

Look for additional information
about the situation in order to
describe and analyze the data more
thoroughly. Identify supporting
evidence and/or conflicting
evidence… but trust the data you
have and work with it.
         Dialogue:
        WHAT ELSE?
  What else do you NEED know?

Look for missing information about
the situation in order to describe and
analyze the data more thoroughly.
Identify data that would be helpful to
complete the picture of the data you
have targeted.
Dialogue: SO, WHAT ?
   So,What does the data say?

Discuss the data collaboratively, as a
team of experts. Avoid “finger pointing”
by disguising the names and
classrooms. Approach the data as a
problem-solving scenario. Put your
heads together and seek meaningful
inferences and logical conclusions about
the data you have.
 Decision: NOW,WHAT ?
Now, What do you need to do?

 What will you change?
 What is implied?
 What is indicated by the data?
 What instructional goal(s) might you set?
 How might you use the data to increase
 student achievement?
 Decision: NOW,WHAT ?
Now,what do you need to do?

Based on the instructional goal(s),
what support do teacher need in terms
of professional development to do
what needs to be done?
What kind of skill development will be
helpful to teachers?
 How Do We Do It?


Another Example!
    District/School Data
Data! Dialogue! Decisions
         WHAT?
What Data Do We Have?
     •Elementary K-8
     •Only 22% Grade 3-6 meet
     or exceed national reading
     norms on ITBS
Data! Dialogue! Decisions
      WHAT ELSE?
Dialogue: What Else Do We Know?
  •63% of ITBS Reading-inferential
           questions
  •80% of teacher/textbook questions
       are factual/recall questions
  •43% of math errors are reading
  errors
   Data! Dialogue! Decisions
         WHAT ELSE?
What Else Do We Need To Know?
•Does the curriculum emphasize inference skills?
•What synonyms can be used to search for
inference skills in the curriculum?
    Data! Dialogue! Decisions
           SO, WHAT?
So, What Does the Data Reveal?
•“Inference” word search with curriculum yields
little support
•“Drawing conclusions” (synonym) showed
some support across the curriculum
  Data! Dialogue! Decisions
         SO, WHAT?
Dialogue: So, What Can We Infer?
 Need to focus on HOT-higher order
thinking…
          •inferring
          •drawing conclusions
          •hypothesizing
          •predicting
     Data! Dialogue! Decisions
           NOW, WHAT?
Decisions: Now, What instruction will
work?
     Unpack the language for the kids:
Infer means … to guess, draw conclusions
      and suggest: a hint, a hunch, an
educated guess; beyond the given; 2+2=4…
•Thread “inference” through all content areas
 Data! Dialogue! Decisions
       NOW, WHAT?
Decisions: Now, What professional
development supports the
instructional goal?
Workshop on integrated curriculum using the
threaded model… threading the skill of
“making inferences” across content…
   Data! Dialogue! Decisions
         NOW, WHAT?
Science: infer from observations in lab
experiments
SS: historical inferences, economic trends,
cultural           patterns
Math: Statistical inference, graph trends
LA: “Read between the lines”… setting,
characters, mood,    tone…
PE/Health: body language, facial expression
Fine Arts: Infer life of artist via “periods of work”
    Data! Dialogue! Decisions
          NOW, WHAT?
 Decisions: Now, What is the SMART
goal(s)?
      S… 25% increase in comprehension for Gr.3-6 next
        quarterly local assessment in reading

      M… 25% is quantifiable

      A… 25% is attainable with inference skill taught in all
      classes next quarter; HOT PD

      R … results targeted

      T… next quarter
Data! Dialogue! Decisions
      NOW, WHAT?
Decisions: Now, Who will do what?
•Grade 3-6 staff will explicitly teach inference
skills in every subject area
•Principal to arrange PD on higher order
thinking skills
 Problem Scenarios

Dealing with the Data
  No Data Available!

The teacher teams have completed
a book study on data-driven
instructions and the teachers feel
like they are ready to try to use
some data to improve student test
scores on reading. However, they
don’t have any ready-to-use data.
What will they do?
     Data Doubters!
The team members are
skeptical about the data they
have. The data seem old, not
current. The data seem
unreliable and not credible.
The data seem irrelevant,
rather than meaningful. How
do they use this data?
     Data Overload!
When the teams have too
much data to analyze, teachers
become confused and
overwhelmed. They don’t know
how to analyze the vast
amount of data in order to
narrow their focus. The team
seems to be going in circles
with no progress being made.
 Phantom Technology!
Although many school
systems have invested in a
technology management
system to produce student
achievement data regularly,
some data teams do not have
this advantage. How might
they manage the data so it is
accessible to them when
needed?
   Quality or Quantity?
There is a heated argument going on
about selected data pieces. One is a
qualitative piece of anecdotal data
gathered during an observation of a
small group of readers. The other is a
data point on the reading
comprehension test. The data do not
complement each other, but rather,
one seems to negate the other. The
argument is about which data are
more valid.
  Finger-Pointing.
There seems to be a lot of
“finger-pointing” during the
data team meeting. Members
are concerned with “why” the
data show deficit areas and
whose fault it really is. The
finger pointing is getting so
bad, that it is almost impossible
to move forward with any real
progress in setting a data-
driven goal. How does the team
move on?
        Taking Over!
There are often strong members of
the group, who are very vocal in the
meetings. In some cases, these
members, inadvertently, take over
group. It’s just a fact. How do teams
gently reign in these informal
leaders of the group, yet appreciate
their leadership qualities and their
good ideas.
 Classroom
Applications
    Set High Expectations
    Challenge Kids to Think
         Require Rigor
   Leave Nothing to Chance
      Make No Excuses
       Insist on Results
               Literacy Matters

Learning to learn - meta-cognition
Inquiring readers - visualize, infer
Tapping into prior knowledge - predict
Extensive reading - fiction/non-fiction
Research on the brain - prior knowledge
Analysis of words - word attack skills
Cooperative learning - cognitive rehearsal
You-are-a-reader attitude - data/feedback
               Literacy Matters

Mediate with interventions - do differently
Appeal to parents - homework/reading
Teach vocabulary - word boxes, booklets
Technology impacts literacy - online
reading
Entry points honor multiple intelligences - 8
Read aloud, along, appropriately - every
day
Strategic reading is guided - DRTA/SQ3R
         Classroom Instruction That Works
                              Marzano, Pickering, Pollock (ASCD)
#1SD -Identifying Similarities and Differences
                     (compare/contrast; classify; metaphor, analogy)
#2SN -Summarizing and Note taking
                   ("delete, substitute, keep"; reciprocal teaching; summary frames; Outlines; webbing)
#3RR -Reinforcing effort and providing Recognition
                    (Effort rubric; effective praise, pause, prompt, praise; concrete symbols)
#4HP -Homework and Practice
                 (homework policy; focused practice)
#5NR -Nonlinguistic Representations
                     (graphic representations; models; mental pictures, drawing, kinesthetic activities)
#6CL -Cooperative Learning
                   (2-4…small, heterogeneous groups; informal - formal ,base groups)
#7OF -Setting Objectives and providing Feedback
                    (specific, but flexible goals ; contracts); timely, specific feedback)
#8GH -Generating and Testing Hypotheses
                   (Problem Solving; Decision-Making; Investigations), Invention, Inquiry)

#9QCA -Questions, Cues, and Advance Organizers
                   (HOT questions; wait time; pre-learning; cues for inferences; narrative,
                   skimming, graphics as advanced organize)
       People Search: Data/Technology
Find someone who . . .
  1. Admits to having an abandoned a piece of exercise equipment
      and will give some reasons why.
  2. Can share a good idea to motivate teachers to use data and
      technology.
  3. Has completed an on-line search with an interesting result.
  4. Will compare and contrast a job done by hand and now done on-
      lie or with the use of computer technology.
  5. Counts calories or “carbs” or “points” and will tell you why.
  6. Can explain and will justify how they select the “essential
      standards” from the “supplemental standards” at their grade
      level or in their discipline.
  7. Completes the analogy Technology : Data :: _____:______.
  8. Ranks the following the same as you do.
  9._____Grades
    _____Local Assessments
    _____Regents Scores
    _____State Data
           Articles
Results Are the Reason - Sparks
Up and Away - Schmoker
The Three Musketeers


  Commit to one communication
    before the next session.
Data! Dialogue! Decisions!


     AHA!
    Oh, No!
Data! Dialogue! Decisions! Alternative Pre/Post Questions
   1. Think about how you’ve used data in the past week.
      (personal life; classroom or school)
   2. Agree/Disagree:
      “The monitoring of effective instruction is the heart of
      effective instruction.” Lortie in Schmoker p.29
   3. Think about the differences-benefits/drawbacks-between
      quantitative and qualitative data.
   4. When working in data, what is a problem or
      challenge you encounter seem to encounter quite often?
   5. Bring team data to work with during the session:
   • School Data
   • Classroom Data
   • Student Data

				
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