Data analysis 2011
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This presentation is based on research regarding best practices for data analysis. The sources are listed on the last slide.
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Data Driven Instructional Decision MakingA framework.docx
Data Driven
Instructional Decision Making
A framework
Data –Driven Instruction
Data-driven instruction is characterized by cycles
that provide a feedback loop
in which teachers plan and deliver instruction, assess student
understanding through the collection of data, analyze the data, and
then pivot instruction based on insights from their analysis.
From: Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-TeachersKnowBest-MakingDataWork.pdf
Data-Driven Decision Making Process Cycle
Data Planning
and
Production
Data Analysis
Developing
an Action
Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data is used
From : Teachers know best: Making Data Work For Teachers and Students
Bill & Melinda Gates Foundation
https://s3.amazonaws.com/edtech-production/reports/Gates-
TeachersKnowBest-MakingDataWork.pdf
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Data –Driven Instruction Feedback Loop
Data Planning
and
Production
Data Analysis
Developing an
Action Plan
Monitoring
progress
Measuring
Success
Implementing
the Action
Plan
Instructors need to
facilitate this data –driven
instruction decision loop
in a timely and smooth
fashion
…and on an ongoing basis
• Per student
• Per class
• Per group
Data –Driven Instruction Feedback Loop
Roles Inherent in the Data-Driven Instruction
Decision Making Loop
• Planner
• Data Producer
• Data Analyst
• Monitor
• Reporter
• Data End User
• IT
• Operations and Logistics
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Introduction To Evaluation
This document provides an introduction to program evaluation. It defines evaluation as the systematic collection of information to improve a program's effectiveness and involves asking good questions and using the answers to strengthen the program. The key reasons for conducting evaluations are to help with program design and planning, facilitate program improvement, provide justification and validation to funders, and involve multiple perspectives. The main steps of evaluation outlined are to clarify the program's mission and goals, establish measurable objectives, collect and analyze data, prepare reports on findings, and use that information to improve the program.
Data/Reform
This document discusses using data to inform curriculum and instructional decisions to improve student achievement. It defines key terms like core curriculum, core maps, and diary maps. It emphasizes the importance of aligning curriculum, instruction, and assessments to standards and collecting various assessment data. Data should be analyzed by teacher teams to identify strengths, weaknesses, and root causes in order to guide goal setting and improve practices. Benchmark assessments administered periodically can provide useful data for progress monitoring and curriculum development. Software tools are available to track assessment data over time.
Wheres My Action Plan? Mo SW-PBS SI 2008
The document discusses guidelines for developing a School-Wide Positive Behavior Support (SWPBS) action plan. It emphasizes that an action plan is important to implement a school's strategic plan. It provides tips for developing goals, activities, timelines and assigning responsibilities using data sources. Regular monitoring and evaluation of the plan is also recommended to ensure goals are being achieved and the plan stays relevant.
Sacred heart 2014
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Practical Evaluation Workshop
This document summarizes a workshop on evaluating mentoring programs. The goal is to promote skills and confidence in program evaluation. Participants will learn how evaluation relates to quality, potential benefits, evaluation steps and resources. Evaluation is important to improve programs, ensure accountability, use resources effectively and avoid harm. Types of evaluation include process, which examines implementation, and outcome, which examines effects. Challenges to evaluation include time, capacity, and buy-in, but strategies can overcome barriers. A seven-step process is outlined to design an evaluation plan and integrate it into ongoing practices.
IPAS Eco-System: Moving from Analysis to Action with the Student Success Plan
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Action research in Applied Linguistics: An Introduction
Action research is a systematic process that involves teachers examining their own educational practices. It is conducted by teachers for themselves, focuses on practical issues in their own classrooms or schools, and aims to improve student outcomes and teacher practices through reflection and data analysis in iterative cycles. Some key characteristics of action research include that it is teacher-led, focused on a specific issue or problem, uses multiple data collection methods, and informs future action through an ongoing reflective process.
Improvement by Using Root Cause Analysis
This document provides an overview of using root cause analysis to determine the underlying causes of noncompliance issues and improve services. It discusses shifting from a compliance focus to performance outcomes, and using root cause analysis to identify and resolve problems rather than just addressing symptoms. A 6-step process is outlined for conducting a root cause analysis: 1) organize a team, 2) define the problem, 3) analyze data, 4) determine the root cause, 5) develop an improvement plan, and 6) evaluate progress. Examples of tools for determining root causes, like the "Five Whys" technique, are also presented.
Action Research Pd
This document outlines the process of action research, which involves systematically studying a problem or issue in one's own practice. It discusses identifying a focus or problem, developing questions to guide the research, collecting and analyzing data to answer the questions, and sharing results. Some key aspects of action research covered include developing focused research questions, choosing appropriate data collection methods directly related to the questions, and creating a plan for gathering, analyzing, and reporting data. The overall goal of action research is to gain insights to improve one's own teaching practice and student learning.
Online Course Assessment Part 1
The document summarizes key points from a presentation on designing online course assessments. It discusses foundations of online assessment including validity, reliability, and alignment. It also covers developing assessments, such as specifying objectives, selecting appropriate assessment types, and ensuring alignment between objectives, activities and assessments. Finally, it addresses creating an assessment toolkit, including choosing appropriate tools, criteria, and ensuring privacy compliance.
Using Naviance Data to Drive College & Career Readiness
Using Naviance data can help schools focus on college and career readiness outcomes. Schools should (1) use reports to identify careers and colleges to target, (2) focus on measurable outcomes like increasing the percentage of students attending 4-year colleges, and (3) collect relevant career and college planning data from students through activities and surveys to evaluate progress toward goals. Building a data culture requires collaboration across staff and students to make data-driven decisions that improve student success.
School counselors using data
School counselors can use data to evaluate programs, measure outcomes, assess cost-effectiveness, make decisions, and monitor student progress. Data identifies needs, describes problems, discovers patterns, and targets interventions. It can convince stakeholders of the need for change, uncover otherwise invisible problems, confirm or discredit assumptions, and guide resource allocation. Focusing on data allows objective, evidence-based conversations and prevents overreliance on standardized tests or quick fixes. School counselors use various types of student achievement, psychosocial, career development, diversity, and school data to implement comprehensive, data-driven programs based on national models and local needs.
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Data analysis 2011
- 1. Putting Data Analysis to Work Using data analysis to answer the questions, “What do the data tell us about our students’ learning and what do we do next?” Presented by: Ginny Huckaba
- 4. Minimize bird walking (M. Hunter)
- 6. AGENDA Welcome, Introduction, Goals Group dynamics What do you already know? What do you want to know (goals)? Using Data to Enhance & Improve Student Learning HIVE Item & Trend Analysis Data analysis-needs assessment Planning for Back-home Colleagues/PD Close
- 8. It is NOT data disaggregation (that stops at the breaking-down stage)
- 10. looking at data closely and objectively.
- 11. using it to make improvements.
- 13. What does “it” measure and how is that information used?
- 16. Provide students with feedback on proficiency
- 17. Get a common understanding of exemplary performance/work and how to achieve it
- 19. Rescuing kids who are falling through the cracks
- 20. Learn what programs are yielding desired results
- 21. Getting to the root cause of a problem
- 22. Accountability
- 25. “Why” may come from secondary data
- 26. Teacher training
- 28. Student demographics, norms, behaviors
- 29. Interventions
- 31. Find out: “How do the prominent stakeholders feel about our actions/efforts?”
- 32. Examine: “What programs and processes are in place in our school that meet our students’ needs (or don’t meet them)?”
- 34. Student achievement drives change (avoid getting PD cart before the horse)
- 35. Don’t implement what kids don’t need
- 36. Look first at the data, then decide on the PD
- 37. Focus on use of data at the classroom level
- 38. To be effective, teachers must be provided:
- 39. Tools
- 40. Time
- 42. Strengths & Success (celebrate)?
- 43. Challenges (to be met)?
- 45. What has to be done
- 46. External factors
- 47. “Do now” (meet AYP)
- 48. Rank Ordering (determining most pressing)
- 49. Based on areas of greatest need
- 51. Requires change at the systems level
- 56. Making decisions from resulting data
- 61. Data-driven instructional design, feedback
- 62. Formative and summative assessments
- 64. Explicit
- 65. Focused
- 66. Make sure all goal statements are designed to be:
- 67. Specific
- 68. Measurable
- 69. Achievable
- 70. Relevant
- 72. Reviewing
- 74. Requires: teacher training and consistent practice in classroom instruction
- 76. Is composed of Instructional Strategies
- 79. Proper implementation of instructional design (strategies)
- 80. Effectiveness (intended effect on student performance taking place)
- 82. Explicitly communicated to staff, parents
- 83. Administrative backing of implementation and sustainability
- 86. Identify the Problem (key indicators for student success/failure)
- 87. Hypothesize and articulate hunches
- 88. Identify the data to be examined and gather it
- 89. Analyze the data (strengths/weaknesses/trends/challenges)
- 90. Begin developing an Action Plan by:
- 91. Setting Goals for the plan and implement the plan
- 92. Evaluate using interim assessments/measures
- 93. Make decisions for improvement based on evaluation results
- 94. Put Action Plan to work
- 96. Who is going to do what work and when?
- 97. Team roles (not limited to):
- 98. Lead project
- 99. Input data
- 100. Produce reports
- 103. Is there a process in place whereby reflections and insights are used to make modifications in instructional/school practices?
- 104. Is there a habit of taking action as a result of patterns/trends that come out of the data?
- 105. Are there redundant or ineffective practices that need to be eliminated?
- 106. Is data collection followed by their analyses and changes/improvements?
- 108. Blink, R.J. (2007), Data-Driven Instructional Leadership. Larchmont, NY: Eye on Education, Inc.
- 109. Love, N. editor (2009), Using Data to Improving Learning for All. Thousands Oaks, CA: Corwin Press
- 110. White, S.H. (2005), Beyond the Numbers. Englewood, CO: Advanced Learning Press
- 111. White, S.H. (2005), Show Me the Proof. Englewood, CO: Advanced Learning Press