Transcript Document

Digging into Data 2014
SECONDARY SCIENCE
(6-12)
Common Board Configuration
(CBC)
DATE: February 2014
OBJECTIVE:
Identify which reports to use to
maximize Data Driven Decisions in a
school-site.
ESSENTIAL QUESTION:
How can data impact teaching and
increase student achievement?
HOME LEARNING:
Review today’s lesson and develop
your next steps to share this
information with teachers at your
school
Data Movement
 Identify top 45% of students per class period (or
percentage according to SIP plus 10-15%)
 Collaborate with department and Specialist to
complete documents and Data Reflection Guide and
Top 45 % (Target Groups)
 Build a Custom Group to track student progress.
Data Movement
Rationale:
Specific data used to create IFC’s
gives a platform to track student
data.
Accountability areas: by period
Non-accountability areas: by course
BELL RINGER
WRITE-PAIRSHARE
What does Data
Driven Decision
Making look like in
the Science
classroom?
ESSENTIAL QUESTION
How can data
impact teaching and
increase student
achievement?
What is Data Driven Decision Making ?
 Data-driven decision-making (DDDM) is a system of
teaching and management practices that gets better
information about students into the hands of classroom
teachers.
 Teachers are finding that intelligent and pervasive uses of
data can improve their instructional interventions for
students, re-energize their enthusiasm for teaching, and
increase their feelings of professional fulfillment and job
satisfaction.
How is Data Driven Decision Making Different
from No Child Left Behind?
Data Driven Decision
Making
 Data-driven decision-
making is about getting
better information into
the hands of classroom
instructors
No Child Left Behind
 NCLB is about
accountability to the
federal government for
the education money it
sends to the states.
Why embrace Data Driven Decision Making?
 Data Driven Decision Making principles and
practices have been shown to have positive impacts
on student learning and achievement gaps
 Data-driven activities existed in some schools long
before NCLB was passed and will continue in many
schools regardless of what happens with the federal
legislation.
Data-driven educators
should be able to
articulate the essential
elements of effective
data-driven education
Major Elements of Data-Driven Instruction
1. Baseline data
1. FCAT/District Provided
2. Measurable
2. School Improvement
3.
4.
5.
6.
instructional goals
Summative
Assessments
Frequent formative
assessments
Professional learning
communities
Focused instructional
interventions.
3.
4.
5.
6.
Plan/IPEGS/Action
Plan
Interims
Progress Monitoring
(FOCUS)
Weekly – Common
Planning
Addressed after each
assessment
Data Driven Decision Activity
What does Data
Driven Decision
Making look like in
the Science
classroom?
DATA POINTS
•
FCAT
•
CURRICULUM GUIDE ASSESSMENTS
•
MINI-LESSON ASSESSMENTS
•
FORMATIVE/SUMMATIVE ASSESSMENTS
Data Reports
Reports are an important tool in understanding
and analyzing the results of student assessments.
 Performance Band reports show you either average
scores distributed by performance band or the number and
percent of scores that fall into each band.
 Class List reports show you how each student in a class or
group performed on an assessment. This report is like a
gradebook.
 A Student Performance report shows you an individual
student’s performance on one or more exams.
Communicating Data to
Stakeholders
PDCA Inst ruct ional Cycle
PLAN
• Data Disaggregation
• Calendar Development
ACT
DO
• Direct Instructional
Focus
CHECK
• Tutorials
• Assessment
• Enrichment
• Maintenance
• Monitoring
DATA CHATS - ADMINISTRATION
 Work together to assist in identifying and implementing
new research-based curricula and teaching practices.
 Collaborative coordinate support for teachers by
connecting them with appropriate training opportunities
and instructional experts.
 Collaborate together in making teachers recognize what
is working (and what is not) in their classrooms and
vigorously support faculty as they transform ineffective
instructional practices into those that result in desired
outcomes.
 Identify interventions and enrichment plans, use of
personnel, materials, and monitoring tools.
Sharing Data
 Data-driven decision-making practices are only
possible in school climates where data is valued and
visible.
 In many data-driven schools, graphs, tables, and
other indicators of data usage permeate the school
environment.
 Discussions about data are frequent and analysis of
student data is considered to be integral to the
teaching and learning process.
ESSENTIAL QUESTION
How can data
impact teaching and
increase student
achievement?
Valid and Reliable Data
 What are some of the issues with using
CGA data?
 What data do you value?
 How can the data that is valued be used
to develop an IFC?
Mini-Lessons
 Created to assist teachers in delivering data driven
instruction in a concise manner.
 Replace bell ringers, do now activities, and warmups in science accountability areas as FCIM
Lessons.
 20-30 minute lessons that are based on item
specifications using the GRRM.
 Are released by specialists after IFCs have been
developed in a group of 3.
Progress Monitoring
 Determines which benchmarks will appear on mini-
lesson assessments. (Progress Monitoring)
 Assists teachers in planning lessons for
differentiated instruction.
 Computer generated programs can be used to track
students progress.
GIZMOS
FCAT EXPLORER/FOCUS (5th-8th-Biology)
Instructional Focus Calendar (IFC)
EXIT SLIP
What can we do with this data that will
give us insight into areas for improving
student performance?
Within each data source, what are the
most important questions the data
should answer for the school to specific
school improvement goals?
QUESTIONS/CONCERNS