Download
1 / 27
270 likes | 406 Views
Digging into Data 2014. Secondary Science (6-12). Common Board Configuration (CBC). ESSENTIAL QUESTION: How can data impact teaching and increase student achievement?. DATE: February 2014. OBJECTIVE: Identify which reports to use to maximize Data Driven Decisions in a school-site.
E N D
Digging into Data 2014 Secondary Science (6-12)
Common Board Configuration (CBC) ESSENTIAL QUESTION: How can data impact teaching and increase student achievement? DATE: February 2014 OBJECTIVE: Identify which reports to use to maximize Data Driven Decisions in a school-site. 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-PAIR-SHARE 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 No Child Left Behind Data-driven decision-making is about getting better information into the hands of classroom instructors 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 • Baseline data • Measurable instructional goals • Summative Assessments • Frequent formative assessments • Professional learning communities • Focused instructional interventions. FCAT/District Provided School Improvement 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.
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 warm-ups 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)
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?
