This document provides an overview and recording of a webinar on using data to inform teaching. The webinar was hosted by faculty and graduate students from the University of Texas at Arlington's Department of Curriculum and Instruction. It included presentations on collecting and analyzing various types of student data, using data to determine instructional strategies and student groupings, and involving students in the data analysis process. Links were also provided to recordings of the webinar and upcoming related events.

1. Data Driven Teaching: Advice
Using 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 ARLINGTON
Dept. of Curriculum and Instruction
New 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

2. Recordings/Links
• Link to the original Smore flyer:
https://www.smore.com/wb17y
• The link to the recorded session is here:
https://elearn.uta.edu/webapps/bb-collaborate-
bb_bb60/recording/launchGuest?uid=1efa4a5a-f648-488a-
95be-cbdaadc3bec4

3. 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

4. Recordings
Archive
Social Media:
YouTube [video]:
http://www.youtube.com/utanewteachers
Slideshare [PowerPoints]
http://www.slideshare.net/utanewteachers
Facebook Page [interaction/updates]:
https://www.facebook.com/UTANewTeacherProject
Upcoming Webinar
Events
• October 10, Webinar
• Topic: Teaching with
EdModo in K-12 Settings
• Guest speaker: Dr.
Harrison McCoy (UTA
Alumni)

5. • 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

6. 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 teacher
B. 1st-3rd year teacher & UTA graduate
C. 1st-3rd year teacher & non-UTA graduate
D. 4th year+ teacher
E. Faculty or none of the above

7. 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.)

8. Hello! I am
Dr. 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
• Seven years as professor at UT
Arlington
• Associate Professor of Literacy
Studies in the Department of
Curriculum and Instruction

9. Key ideas:
Do not “teach to the test”!
Involve students in the process
• Collecting
Data
• Analyzing
Data
• Action!

10. 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

11. 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.

12. Example from Hello Literacy
(Used with Creative Commons License CC-BY)
http://www.helloliteracy.com/2012/06/progress-monitoring-vs-progress.html

13. 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

14. Obtaining Baseline Data
By: Nely Tinajero Santoyo
Masters in Curriculum and Instruction
with Literacy Studies Emphasis

15. 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.

16. 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

17. 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.

18. 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.

19. In conclusion, baseline data is……..
Baseline
data
common
assessments
Review
the data
Tier
students
Progress
monitor
Keep
record of
progress

20. Thanks for watching!
If you have more questions feel free to e-
mail me at nely.tinajero@mavs.uta.edu

21. “This is OUR classroom.”
How to involve students in data analysis and
instructional planning.
Alison Capasso,
1st and 2nd grade teacher

22. 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.

23. 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.

24. 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.

25. Give them the
opportunity to
make an
individual
growth plan.

26. Remember-
Rome wasn’t
built in a day!
The more you
discuss
performance, the
deeper you can
go.

27. 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 mpant@uta.edu.

28. Demo of Data Types
• See the link to see a spreadsheet with ways to display and visualize
data: https://uta.box.com/s/nhib5rcynhofrfyiamjc9shnyue033ic

29. 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….”

30. Graduate Program in Literacy Studies
• http://www.utcoursesonline.org/programs/programinfo/med/
curriculumandinstruction/index.html
• Email Dr. Kathleen Tice about Literacy Studies: ktice@uta.edu
• Our other Master’s programs in Curriculum and Instruction:
https://www.uta.edu/coed/gradadvising/programs/curricandin
struct/index.php

31. 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

32. For more information on the Mind,
Brain, and Education Master’s degree,
please contact Dr. Marc Schwartz
schwarma@uta.edu