What do you know about Leonardo da Vinci

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(No Transcript)
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What do you know about Leonardo da Vinci?
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Leonardo da Vinci was a renaissance painter,
architect, engineer, mathematician and
philosopher, a genius the world has never seen
again so far.

• "Leonardo da Vinci was like a man who awoke too
  early in the darkness, while the others were all
  still asleep" Sigmund Freud 

Art is never finished, only abandoned.
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INSTRUCTIONAL DATA-DECISION MAKING

• Principals Meeting
• October 25, 2007

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Objectives

• (1) To develop and model a data-driven process
  for principals to use with teachers in guiding
  instructional decision-making.
• (2) To make connections between academic
  performance data and curriculum.
• (3) To create an instructional focus for lesson
  design and planning.

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What is knowledge?

• Declarative What is?
• Procedural How to?
• Relational Whats the connection?
• Conditional What is the reason?
• Evaluative What is the value?

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Instruction

• Instruction is composed of --
• evidence based teaching techniques, materials and
  strategies provided within the classroom.
• To meet the needs of all students --
• instructional practices within the educational
  setting should include differentiation,
  appropriate resources, supplemental and intensive
  instruction, and changing the pace.
• Inherent in this process is the
• understanding that students respond differently
  to instruction, and data collected regarding
  student performance and other personal factors
  must guide instruction.

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Instructional Data

• Data is the
• quantitative or qualitative representation of
  students academic behaviors.
• performance of students on diagnostic, formative,
  and summative assessments.
• results of students responses to an interest,
  learning style, multiple intelligences, and/or
  engagement survey.

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How do you define data?

• Data is information that is organized for
  analysis and decision making. Data drives
  instruction.
• Data is a way to help make uniformed decisions
  that can lead to improvement of student
  achievement in a school.
• Data helps us with
• Where we are this is the data analysis and
  interpretation
• How do we get there -- this is the curriculum
  and instruction
• Where do we want to go -- this is the evidence
  of achievement
• Collecting Data
• Brainstorm with your table What are examples of
  data collected in your school?

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What are the tools needed for instructional data
decision-making?
Data that represent student learning
Data that represent student classification
1. Body of knowledge 2. Cognitive skills
Data that represent how a student like to learn
Data that represent what student must learn
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ACADEMIC PERFORMANCE DATA

Identify the strand for the instructional focus
Identify the benchmark for the instructional
focus

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ACADEMIC PERFORMANCE DATA
Identify the benchmark for the instructional
focus
Identify the strand for the instructional focus
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DEMOGRAPHIC DATA
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CURRICULUM DATA
Related to Q19 (32)
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CURRICULUM DATA
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PERSONAL- MI/LS/INTEREST/ENGAGEMENT DATA
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Student Optimal Learning Inventory
PERSONAL- MI/LS/INTEREST/ENGAGEMENT DATA
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How to organize and interpret the data?
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• What are the academic strengths and weaknesses
  indicated by the data?
• Identify related sub-group academic deficits.
• How do you apply the data to curriculum targets?
  (Begin to think about this)

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What are the results of the groups work?
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• What are the implications of the results of the
  data analysis for
• professional development,
• student grouping,
• teacher assignment,
• scheduling,
• curriculum resources,
• and fund allocation?

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Modeling the analysis of the data
Grouping for differentiation
peer tutoring cooperative
learning small group direct
instruction
Benchmark Assessment Data Grade 7
Subject Science Teacher 089
School Sample Middle Quarter 1
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Modeling the analysis of the data
Identified strand for the instructional focus
Identified benchmarks for the instructional focus
Grouping students for differentiation
 
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Modeling the analysis of the data
Benchmark Assessment Data Grade 7
Subject Science Teacher 089
School Sample Middle Quarter 1
Identified students to check for 1.
comprehension skills 2. conceptual
misunderstanding/ mental models 3. Background
knowledge 4. test strategy skills 5. other
academically related needs
 
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Modeling the analysis of the data
Grouping for differentiation
peer tutoring cooperative
learning small group direct
instruction
Benchmark Assessment Data Grade 6
Subject Mathematics Teacher 054
School Sample Middle Quarter 1
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Modeling the analysis of the data
Identified strand for the instructional focus
Identified benchmarks for the instructional focus
Grouping students for differentiation
 
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Modeling the analysis of the data
Benchmark Assessment Data Grade 6
Subject Mathematics Teacher 054
School Sample Middle Quarter 1
Identified students to check for 1.
comprehension skills 2. conceptual
misunderstanding/ mental models 3. Background
knowledge 4. test strategy skills 5. other
academically related needs
 
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Determine the relationships between the data and
the curriculum content and skills.
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Determine the relationships between the data and
the curriculum content and skills.
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Develop an instructional focus for what students
need to know and be able to do.What level of
Blooms Taxonomy is expected to meet the
curriculum requirement?
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Develop an instructional focus for what students
need to know and be able to do.What level of
Blooms Taxonomy is expected to meet the
curriculum requirement?
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Through a facilitative process, design and plan a
lesson outline for teaching the identified
curriculum target.
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What have you learned from the experience?Evaluat
e the content and methods of the instructional
design and delivery of this lesson.
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Mindset

• In the absence of data, what factors determine
  instructional decisions?

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• EFFECT DATA CAUSE DATA

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Effect Data

• Student achievement results from various
  measurements may be state, district, school,
  grade level or classroom initiated.
• Results of Measurements
• Different levels of data
• Formative and summative

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Cause Data

• Cause data is based on actions of the adults in
  the system, materials used, curriculum, frequency
  of lessons, duration of lessons, etc. It can be
  linked to the effects (results).

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Importance of Data Types

• Cause data provides insight and reasons for the
  effect data.
• Without cause data, the effect data is not as
  useful.
• For strategic teaching and leadership to occur,
  monitoring both cause and effect data is vital.

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Data Collection Analysis Process

• COLLECTION OF DATA
• Data should focus on student demographics and
  achievement.
• Categorize information.
• Original data should be kept for
  cross-referencing.
• DISAGGREGATE DATA
• ANALYZE DATA
• Data team members ask questions about the
  collected data.
• REFLECTION
• Data team members identify goals for improving
  student achievement

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Data Analysis

• How should data be analyzed in your school?
• Three Principles of Data Analysis
• Exploring and determining the antecedents for
  success
• Collaborating with colleagues
• Embracing Accountability- Learning from our data

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Antecedents

• The conditions, structures, and strategies that
  correlate with improved student achievement.
• Escaping the rear view mirror effect
• Knowing the causes that produce results
• Replicating success- Leading to sustainability

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Collaboration

• Data analysis is a team sport.
• Doug Reeves
• Collaboration
• Develops team thinking
• Promotes insights that numbers alone cant
  produce
• Provides a forum for legitimizing practice
• A characteristic of Schools that Learn

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Accountability
Accepting responsibility to act on our data.

• Collecting
• Disaggregating
• Analyzing
• Reflecting

• Take action to make the necessary changes.

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Data Teams

• Guidelines for effective Data Teams
• Have collaborative teams
• Provide adequate time for collaboration
• Engage in collective inquiry
• Focus on the cause and effect data
• Post graphs and charts so they are visible
• Subscribe to action orientation and
  experimentation
• React to our data with sound instructional and
  curricular decisions
• Implement an effective communication
• Are results driven
• Are devoted to continuous improvement

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There are three classes of people those who
see. Those who see when they are shown. Those who
do not see.

• I have been impressed with the urgency of doing.
  Knowing is not enough we must apply. Being
  willing is not enough we must do.

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URL for Instructional Strategies

• http//www.specialconnections.ku.edu/cgi-bin/cgiwr
  ap/specconn/main.php?catinstructionsectionteach
  ertools
• http//www.mitest.com/
• http//www.bgfl.org/bgfl/custom/resources_ftp/clie
  nt_ftp/ks3/ict/multiple_int/index.htm