data driven teaching: advice on using data to inform teaching
TRANSCRIPT
Data Driven Teaching: AdviceUsing Data to Inform Teaching. Practical Tips and Examples from Faculty and Grads of The University of Texas of Arlington.
Hosted by:
Peggy Semingson, Ph.D.Nely Tinajero, Master’s Candidate and Teacher Ali Capasso, Master’s Candidate and Teacher
University of Texas at ARLINGTONDept. of Curriculum and InstructionNew teacher WEBINAR: Fall 2015
Recordings will be available of webinars. No names will be visible in the recordings.
The recording will be available on our YouTube channel: http://www.youtube.com/utanewteachers
SATURDAY, SEPTEMBER 12, 2015 1:00-1:45 PM, CST
These are our opinions and suggestions!
The opinions of each the presenters in the series are their own individual viewpoints and do not necessarily reflect the views of UT Arlington.
Our goal is for you to hear a variety of viewpoints to help support you in your first years of teaching! We have been down the road you are going!
– Support– Respect– Dialogue– Sharing
• Ask questions and post comments along the way.
• Main Q/A at the end.• Make a list of “Things to Google”
later.• Use chat window often. • We will check the chat window
throughout the session and respond in “real time” as we can.
Tips for your own learning
RecordingsArchive
Social Media:
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Upcoming Webinar Events
• October 10, Webinar• Topic: Teaching with
EdModo in K-12 Settings
• Guest speaker: Dr. Harrison McCoy (UTA Alumni)
• Thanks for joining us! Please use the marker/pen tool to mark a small x below where you are at. You can also type it in the chat window.
WHERE WE ARE NOW:Use the pen tool to mark your location
Poll question: • Where are you in your teaching career?
• Select A-E ptional! We will display the results!• The drop down polling area is in the participants window next to the “hand”
tool.
I am currently a: A. Pre-service teacherB. 1st-3rd year teacher & UTA graduateC. 1st-3rd year teacher & non-UTA graduateD. 4th year+ teacherE. Faculty or none of the above
Prior Knowledge: Understanding “Data Driven Teaching”Overview of the text tool: type about what comes to mind when you hear the term “Data Driven Teaching” in the box below using the text tool. (Or, use the
chat window.)
Hello! I amDr. Peggy Semingson, Associate Professor at The University of Texas at Arlington, Dept. of Curriculum and Instruction (2008-Present)
• Former bilingual/ESL teacher and reading specialist (8 years, elementary, public schools)
• Ph.D. in Language and Literacy from UT Austin
• Six years as professor at UT Arlington• Associate Professor of Literacy
Studies in the Department of Curriculum and Instruction
Key ideas:Do not “teach to the test”!
Involve students in the process
• Collecting Data
• Analyzing Data
• Action!
Collecting Data: Terminology and Types of Data• Baseline data-initial data collection “starting point”• Formative (ongoing data)• Summative (cumulative at end of unit)• Informal-classroom-based data collection• Formal-standardized tests are an example• Screening-check for students who might face challenges• Progress Monitoring -systematic data collection (informal)• Digital assessment, e.g., iStation http://www.istation.com/ Schoolology
https://www.schoology.com/home.php Google Classroom https://classroom.google.com/ineligible
Analyzing Data
• Spreadsheets! Learn how to use Excel! • Include multiple measures-not just one data source• Involve students in the analysis and help them to set learning
goals.• Help students chart progress, e.g., reading fluency chart. • Decide action steps and interventions based on data.
Example from Hello Literacy(Used with Creative Commons License CC-BY)
http://www.helloliteracy.com/2012/06/progress-monitoring-vs-progress.html
Action Steps
• Determine who needs intervention and on what skills.• Keep intervention flexible.• Grade-wide discussion of data helps.• School-wide planning/coordination of intervention is ideal.• Student Self-assessment– Checklists for students– Student written reflection
Obtaining Baseline Data
By: Nely Tinajero SantoyoMasters in Curriculum and Instruction
with Literacy Studies Emphasis
About Me• UTA alumni and current
graduate student.• 5 years in early childhood• Has worked with youth
and adults for 11 years.• I love to teach and
empower others.
The Importance of Baseline Data
Gives you a starting point and let’s you know how much you need to help each student grow
It shows what a student can do without interventions Baseline data collected is formative Common assessments or school district assessments
can be used
Common Assessments• Having consistency is key across your grade level
when developing teacher made assessments.• Meeting with your vertical teams can help in
deciding what concepts to introduce early on.• After giving a common assessment, meet with your
PLC’s (Professional Learning Communities) and discuss trends and areas of concern.
Progress Monitoring• Develop measurable objectives to meet the students
areas of concern.• Tier students according to their growing abilities. • Continue to assess students throughout the year and
document their progress.• The data and work samples that are collected can be
useful when referring students for additional academic support.
In conclusion, baseline data is……..
Baseline
datacommon assessme
ntsReview
the data
Tier studen
ts
Progress
monitor
Keep record
of progre
ss
“This is OUR classroom.”How to involve students in data analysis and
instructional planning.Alison Capasso,
1st and 2nd grade teacher
Why involve your students in the planning process?
o Students gain a sense of ownership and understanding of their own learning.
o Students trust that you truly value their input about your instructional practices.
o These practices build community in the classroom.
o Analyzing data together builds metacognition and encourages the growth mindset.
How Do I Start? Start each week by displaying a weekly
objective using your curriculum and the TEKS/Other standards. This should be something which can be measured using data from an assessment.
Inform the students of what strategy/strategies will be used to learn about this material.
Over the week, remind the students of the learning goal each day.
Assess mastery in some way (ideally several ways) toward the end the week.
Discuss results as soon as possible after assessment and compare with your learning plan.
The power is in the discussion. Students will be made
aware of their individual proficiency with the skill.
The class can decide together how to proceed with the learning.
The class can analyze what aspects of the skill confuse them.
Opportunities can be given for input into instructional strategies.
Give them the opportunity to
make an individual
growth plan.
Remember-Rome wasn’t
built in a day! The more you
discuss performance,
the deeper you can go.
Data Types, Graphing and Describing Them*Dr. Mohan Pant, UT Arlington• Data can be textual (qualitative) or numerical (quantitative)• Quantitative data can be classified as ordinal, interval, or ratio scale
• Store data in an Excel file using columns for variable names and rows for participants• Graphing data may involve drawing a Bar graph, Pie Chart, Line Graph,
Scatterplot, which can be done Excel.• Describing data may involve both graphical and numerical summaries
(e.g., measures of central tendency and measures of dispersion).• Excel can be used for computing basic descriptive statistics such as
mean, standard deviation, and correlation. • If you have any questions, write email at [email protected].
What do you think?type in the chat window!
• What information stood out to you from The presentation?
• What questions do you have?
• “I hope to explore.…”• “I learned….”• “ I want to try….”• “I want to know….”
Graduate Program in Literacy Studies
• http://www.utcoursesonline.org/programs/programinfo/med/curriculumandinstruction/index.html
• Email Dr. Kathleen Tice about Literacy Studies: [email protected] • Our other Master’s programs in Curriculum and Instruction:
https://www.uta.edu/coed/gradadvising/programs/curricandinstruct/index.php
UT Arlington Master’s in Mind, Brain, and Education
Our work at the SW Center for Mind, Brain and Education seeks to advance the quality of teaching based upon insights gained from the cognitive and neural sciences as well as contribute to research in this new and evolving field.
We build collaborative research relationships with schools, develop research trajectories that profit from the strengths of our faculty and students and maintain a working and teaching laboratory for researchers and graduate students.
1. Courses include: Neuroscience of typical and atypical language development Neuroscience of typical and atypical mathematical reasoning Complex dynamic systems Research design EEG research methodology
2. Individual work: Research-based capstone project encouraged - Conference presentations encouraged - Publishing in peer-reviewed journals
For more information on the Mind, Brain, and Education Master’s degree, please contact Dr. Marc [email protected]