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Data Coaching Services Concepts 1
| Date post: | 25-Dec-2015 |
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Data Coaching Services
Concepts
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o Triangulation
o Data Analysis Terms & Techniques
o Data Sources
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o What is it?
o Why is it important?
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o What is it?◦ Using multiple data
sources, data collection procedures, and analytic procedures.
o Why is it important?
◦ It can ensure a more accurate view that will help in making more effective decisions.
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Triangulation:A Multidimensional View
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Data Analysis Model and ProcessWhen using a process to analyze data it is important to practice a multidimensional view.
Triangulation:A Multidimensional View
o Collecting and reviewing baseline data
o Discuss / define student data points
o Disaggregating student data and digging deeper
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Data Analysis Techniques to Review:
o The Data Analysis Model and Process
o Graphing and visually displaying data to share with teachers, campuses and district staff
Baseline Data:
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Definition
Non-examples
Facts / Characteristics
Examples
Baseline data
Initial student (assessment) information and data that is collected prior to program interventions and activities.
It can be used later to provide a comparison for assessing the interventions impact / success. Usually collected at the: BOY, MOY, EOY.
Data: Readiness Inventories, ACP Tests, ISIP, ITBS, Fluency Probes, Texas Middle School Fluency Assessment (TMSFA), TAKS.
Unspecific or non-measurable item.
Student Data Point:
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Definition
Non-examples
Facts / Characteristics
ExamplesStudent data point
A data point is one score on a graph or chart, which represents a student’s performance at one point in time.
Can be collected at different intervals (daily, weekly, monthly). Can be plotted on a graphical display. Trends and patterns can be observed.
Unspecific or non-measurable item.
o Disaggregating data involves separating student-learning data results into groups of data sets by race/ethnicity, language, economic level, and or educational status.
o Normally student achievement data are reported for whole populations, or as aggregate data. When data is disaggregated, patterns, trends and other important information are uncovered.
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Disaggregating student data and digging deeper:
o Why is it important?
o By looking at data by classrooms in a school, by grade levels within a school or district, or by schools within in a district; disaggregated data can tell you more specifically what is affecting student performance.
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Disaggregating student data and digging deeper:
o Why is it important?
o Disaggregators allow the ability to focus in on a particular group of students and to compare them with a reference group.
o For example, a campus may want to see how the Limited English Proficient (LEP) students are performing relative to other students.
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Disaggregating student data and digging deeper:
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Disaggregators can include the following:
oRaceoEthnicityoGenderoSpecial Education StatusoLunch Status (Income Level)oEnglish Proficiency (LEP)oGradeoAttendance RatesoRetentionoCurrent and Prior Programs, Supports, and Interventions
Example:oFourth-grade African American, White, Hispanic, Native American, and Asian students’ performance in math.
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Practice a consistent process to analyze data such as: The Data Analysis Model and Process
Data Analysis Model Layers
Process Steps
Embedded Data Practices
District Initiatives
Student Achievement
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Further information over The Data Analysis Model and Process, tools and resources can be found at:http://www.dallasisd.org/Page/12258
o Data Walls can:
o Create visual displays of data, and student / teacher progress toward goals
o Build a shared vision of campus and teacher ownership and awareness toward goals
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Graphing and visually displaying data to share with teachers, campuses and district staff
o Data Walls can:
o Facilitate team engagement and learning
o Create visuals that anchor teachers and campuses work and can be shared with other audiences
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Graphing and visually displaying data to share with teachers, campuses and district staff
◦ Assessments◦ Academic Behavior◦ On-Track /Graduation◦ College Readiness◦ Course Enrollment◦ Demographics
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Student Data Specific Examples of Student Data:
o Elementary (PK-5):o ISIP, ITBS/Logramos,
STAAR, TAKS, Readiness Inventory, Interim Assessments
o Secondary (6-12):o Readiness Inventory,
Interim Assessment, Writing Assessment, ACP, TAKS/STAAR, Texas Middle School Fluency Assessment (TMSFA), Fast ForWord Reading Progress Indicator (RPI), EOC, Readistep, PSAT
o AEIS – Academic Excellence Indicator System : http://ritter.tea.state.tx.us/perfreport/aeis/
o AYP – Adequate Yearly Progress : http://www.tea.state.tx.us/ayp/
o District performance standards and campus information found in Dallas ISD Campus Data Packets: http://mydata.dallasisd.org/SL/SD/cdp.jsp
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Examples of Campus Data & Locations:
