Using Assessments and Data to Improve Student Learning Day 3 1

Day 2 Exit Cards Theme: Meeting the needs of the students Observations: Willingness to reflect 2

Day 3 Goals Evaluate strengths and limitations of different types of assessments Understand how to adapt instruction based on evidence of student thinking Gather, represent and interpret summative assessment data 3

ANALYZING AND USING SUMMATIVE ASSESSMENT DATA 4

Applying our Framework to Summative Assessments 1. Organize for Collaborative Work 5. Examine Instruction 4. Dig into Student Data 3. Create Data Overview 2. Build Assessment Literacy 6. Develop Action Plan 7. Plan to Assess Progress 8. Act and Assess 5 Boudett, City, and Murnane (2014)

A Framework for Adapting Instruction Based on Data 1. Organize for Collaborative Work 5. Examine Instruction 4. Dig into Student Data 3. Create Data Overview 2. Build Assessment Literacy 6. Develop Action Plan 7. Plan to Assess Progress 8. Act and Assess 6 Boudett, City, and Murnane (2014)

Analyzing Overall Class Achievement Mean Median Range Top (Upper Quartile) Bottom (Lower) Quartile Scores 7

Mean (Average) The sum of a list of numbers divided by the total number of numbers in the group. Example: = 320 [Sum of numbers] 320/4 = 80 [Divided by the number of numbers] 8

Median The middle value of data listed in rank order. – If there are two central values, take the average. Example: 100, 95, 90, 86, 85, 82, 77, 60, 41 Mean: 79.5 Median: 85 9

Range The difference between the highest and lowest values. Example: 100, 95, 90, 86, 85, 82, 77, 60, 41 Range = 59 10

Quartile Scores Top/Upper/4 th Quartile: Looking at the highest 25%. Excel calculates by giving the number for which 25% is greater than or equal to. Bottom/Lower/1 st Quartile: Looking at the lowest 25%. Excel calculates by giving the number for which 25% is less than or equal to. 11

Set of Statistics 98, 94, 89, 86, 79, 70, 68, 65 Mean: Median: 82.5 Range: 33 Top Quartile: 94 (Excel: 90.25) Bottom Quartile: 68 (Excel: 69.5) 12

A Framework for Adapting Instruction Based on Data 1. Organize for Collaborative Work 5. Examine Instruction 4. Dig into Student Data 3. Create Data Overview 2. Build Assessment Literacy 6. Develop Action Plan 7. Plan to Assess Progress 8. Act and Assess 13 Boudett, City, and Murnane (2014)

Entering Grades 14

Descriptive Statistics 15

Histogram 16

Box-and-Whiskers Plot (modified) 17

A Framework for Adapting Instruction Based on Data 1. Organize for Collaborative Work 5. Examine Instruction 4. Dig into Student Data 3. Create Data Overview 2. Build Assessment Literacy 6. Develop Action Plan 7. Plan to Assess Progress 8. Act and Assess 18 Boudett, City, and Murnane (2014)

Histogram 19

Box-and-Whiskers Plot (modified) 20

Descriptive Statistics 21

Entering Grades 22

A Framework for Adapting Instruction Based on Data 1. Organize for Collaborative Work 5. Examine Instruction 4. Dig into Student Data 3. Create Data Overview 2. Build Assessment Literacy 6. Develop Action Plan 7. Plan to Assess Progress 8. Act and Assess 23 Boudett, City, and Murnane (2014)

STEP 5: Examining Instruction WHY ARE STUDENTS STRUGGLING? HOW DOES MY TEACHING RELATE TO THE ‘WHY’? 24

A Framework for Adapting Instruction Based on Data 1. Organize for Collaborative Work 5. Examine Instruction 4. Dig into Student Data 3. Create Data Overview 2. Build Assessment Literacy 6. Develop Action Plan 7. Plan to Assess Progress 8. Act and Assess 25 Boudett, City, and Murnane (2014)

STEPS 6 – 8: Develop a Plan, Act, & Reassess Concept 1.Target the adaptation 1.Action needs to address the deficiency 1.Action must move beyond re-teaching Self-Reflection 1a. Would the whole or just part of the class benefit from new instruction? 1b. What should I pre-emptively change for next year? 2. What strategy best addresses the gap between the goal and current student thinking? 3. How is the adapted instruction different from what I did before? 26

Applying the Framework in Practice How can I do this analytical process when I have 100+ students and three different classes to prep? 27

CASE STUDIES 28

Summative Case Studies Data Wise Framework – Step 4: Digging into the data Case Study Overview – Review data overview  Dig into data  Examine lessons  Suggestions for adapted instruction 29

Guiding Questions Step 4: Digging into the data 1.What information do these descriptive statistics provide? What is your evidence? 2.How are the scores distributed among students? What does this tell you? 3.What more might you want to know? 30

REVISITING OUR INITIAL MODELS 31

Conceptual Models of Assessment Task: Develop a new/revised/enhanced visual representation of assessment in your science classroom. 32

Pair Discussion 1.What new items do you notice? 2.What similarities exist between the original and revised model? 3.Are there any shifts in emphasis? What are they? 4.Which area(s) of the model will be the easiest to enact? 5.Which area(s) of the model will be challenging? How might you overcome those? 33

SCHOOL YEAR LOGISTICS 34

Project Logistics August/September PD School-based meetings – Focus: using data in different ways – Based on your 5-Day Notebooks – 4 in fall; 4 in spring 2 5-Day Notebooks – 1 in fall; 1 in winter 10-Day Notebook – Late spring; same notebook 35

CLOSING DAY 3 36

Final Exit Card 1.How has your science assessment framework changed over the course of the PD? 2.Which dimensions (frequency, cognitive complexity, student feedback, etc.) do you see being able to incorporate into your practice this Fall? 3.What elements of your school context might facilitate your intended assessment practices this year? What elements might hinder your efforts? 4. What questions still remain? 37

Thank You! 38