1. Welcome to Sharp School’s Data Day!1

2. Agenda Morning: 8:30 – 9:00 Welcome/Norms/Do-Now
9:00 – 11:00 Data Driven Instruction/Data Analysis
11:00 – 12:00 Lunch
12:00 – 1:30 Scoring Writing Interim Assessment
1:30 – 2: Data Analysis/Deep Dive In
Grade Level Sessions 2

3. ASSESSMENTS (Interim, Transparent, Aligned, Reassess)
THE FOUR KEYS:
ASSESSMENTS (Interim, Transparent, Aligned, Reassess)
ANALYSIS (Quick, User-friendly, Teacher-owned, Test-in-hand, Deep)
ACTION (Action Plan, Ongoing, Accountability, Engaged)
in a Data-driven CULTURE 3

4. Data-Driven Instruction & Assessment
Paul Bambrick-Santoyo
Data-Driven Instruction & Assessment

5. Dr. Spock and Captain Kirk on Data Driven Instruction5

6. Our first “mission:” Analyze our Data
Use the pre-printed data for your classroom
Using the large, lined Post-it’s provided in the bags, write 3 observations that are evident from your data. They must be free of speculation or inference – they should be based on fact, from objectively looking at the data 6

7. And then:
We will each read out one new observation from our Post-it. The facilitator will write the observation on chart paper. Teachers will share observations until everyone has a chance, or until we are out of chart paper!  All observations will be shared without discussion. 7

8. Next:
Each teacher will take their Post-it and jot down 3 suggestions or question statements based on any observations. These will attempt to offer possible explanations for the observations or pose suggestions for using additional data. No attempt should be made to solve the problems that surface; the idea is to gain new insights into what the data implies. 8

9. Finally:
Each participant reads one new thought that has not yet been shared.
After the exercise, put your Post-it on the grade-level chart. 9

10. For Grade-Level Deep Analysis - PM
PART 2—DIG IN:
“Squint:” Bombed questions—did students all choose same wrong answer? Why or why not?
Compare similar standards: Do results in one influence the other?
Break down each standard: Did they do similarly on every question or were some questions harder? Why?
Sort data by students’ scores: Are there questions that separate proficient / non-proficient students?
Look horizontally by student: Are there any anomalies occurring with certain students? 10

11. Youtube: Data Driven Instruction
Analysis
Analysis 2 11

12. Youtube: Data Driven Instruction
Action
Assessment 12

13. Mr. Holland’s Opus:
What made the difference? How did Lou Russ finally learn to play the drum?
What changed Mr. Holland’s attitude and actions?
Lou Russ 13

14. Data-Driven Instruction & Assessment Paul Bambrick-Santoyo
Conclusions