Presentation on theme: "Strong Leadership Positive Belief and Teacher Dedication Data Utilization and Analysis EﬀectiveScheduling Professional Development ScientiﬁcallyBased."— Presentation transcript:
Strong Leadership Positive Belief and Teacher Dedication Data Utilization and Analysis EﬀectiveScheduling Professional Development ScientiﬁcallyBased Intervention Programs Parent Involvement
Assessments are designed to determine the student's abilities and needs in relevant aspects of the curriculum and instruction. Educators need this essential tool to guide day-to-day instruction.
The range of assessments may range from classroom assessment practices all the way to district and statewide assessment programs.
Screening Assessment: (First Alert) These quick, easily administered assessments are used to identify those students who are at-risk for reading difficulties. (Dibels, AimsWeb) Progress Monitoring Assessments: These assessments are used to ensure that students are making adequate reading progress. They must be easily administered and sensitive to growth over short instructional intervals. These assessments quickly determine the need for instructional change when progress is not at the expected level. (Dibels, AimsWeb, 4Sight) Diagnostic Assessments: These in-depth assessments determine a student's specific instructional needs and what and how much intervention may be required. They measure the component skills of reading, and determine where in the scope and sequence of those skills the student falls. (Woodcock Reading Mastery Test, Peabody Picture Vocabulary Test) Outcome Assessments : These assessments are used to determine whether students have achieved grade-level or proficiency standard performance. (PSSA)
Collect and chart data Analyze strengths and obstacles Establish goals: set, review, revise Select instructional strategies Determine results indicators
Demographic Data Student Learning Data Perceptions Data School Processes Data
Standardized test scores Grade point averages Standards assessments Authentic assessments
Summative district and state assessments (aggregated, disaggregated; strand, item, and student work) Data about people, practices, perceptions (e.g. demographic, enrollment, survey, interview, observation data, curriculum maps) Benchmark common assessments (e.g., end of unit, common grade-level tests reported at item level) Formative common assessments (e.g., math problem of the week, writing samples, science journals, other student work) Formative classroom assessments for learning (e.g., student self-assessments, descriptive feedback, selected response, written response, personal communications, performance assessments) Annually 2-4 times a year Quarterly or end of unit 1-4 times a month Daily/ Weekly
Teachers must spend most of their time using this form of assessment Information is used to: ▪ adjust teaching ▪ provide feedback to students to help them improve their learning ▪ Examples: descriptive feedback to students, use of rubrics with students, multiple methods of checking for understanding, examination of student work, and tests and quizzes
Frequently analyzed by the data team Importance: Identifying student-learning problems Generate short cycles of improvement Frequently monitor progress toward the overall student-learning goal
Administered at the end of a unit or quarter Importance: Assesses the student’s mastery of concepts and skills recently taught Excellent sources of student-learning data because they are timely, closely aligned with local curriculum and has item level analysis Can be used to immediately improve instruction Can be used to provide data for programmatic changes.
Data about people, practices, and perceptions Examples: Student populations Teacher characteristics data Course enrollment data Dropout rates
Includes state assessments and annual district tests Data is used to: Determine accountability Identify a student-learning problem Set annual improvement targets
October 20 – 31, 200812 th Grade Retest for Math, Reading & Writing February 9 – 20, 20095 th, 8 th & 11 th Grade Writing Assessment March 16 – 27, 20093 rd, 4 th, 5 th, 6 th, 7 th, 8 th & 11 th Grade Math and Reading Assessment April 27 - May 8, 20094 th, 8 th & 11 th Grade Science Assessment
The process of looking more and more deeply at data to gather more information. Love 2008
The deepest underlying cause, or causes of positive or negative symptoms within any process, which if dissolved, would result in elimination or substantial reduction of the symptom. Pruess 2007
Helps dissolve the problem Eliminates patching and wasted effort Conserves scarce resources Induces discussion and reflection Provides rationale for strategy selection
1. Would the problem have occurred if the cause had not been present? No, then it is a root cause. Yes, then it is a contributing cause.
2. Will the problem reoccur as the result of the same cause if the cause is corrected or dissolved? No, then it is a root cause Yes, then it is a contributing cause
3. Will correction or dissolution of the cause lead to similar events? No, then it is a root cause Yes, then it is a contributing cause
You run into a dead end Everyone agrees that it is the root cause The cause is logical, makes sense, and provides clarity to the problem The cause is something that you can influence and control Pruess 2003
1. The Questioning Data Process 2. The Diagnostic Tree Process 3. The Creative Root Cause Analysis Team Process 4. The Five Whys 5. Force Field Analysis 6. Barrier Analysis 7. Fishbone Diagram
RCA can be used at any level within a school district or building A teacher working to improve classroom instruction A grade level or department seeking to improve learning School Improvement A council working to remove barriers to student success at the district level
Love, Nancy; Stiles, Katherine; Mundry, Susan; DiRanna, Kathryn (2007).The Data Coach’s Guide to Improving Learning for All Students. Thousand Oaks, CA: Corwin Press. Pruess, Paul G. (2003) School Leaders Guide to Root Cause Analysis Using Data To Dissolve Problems. Larchmont, NY 10538. Eye on Education. Pruess, Paul G. (2007) Data-Driven Decision Making and Dynamic Planning. Larchmont, NY 10538. Eye on Education. Reeves, Douglas B. (2008) Data for Learning. Englewood, Colorado: The Leadership and Learning Center.