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QUANTITATIVE LITERACY PROGRAM
RAJ B OPPANAMARY DIXSONKIM MASSAROGAIL PIZZOLA
KIMBERLY WARD
2015 QLP Workshop
Agenda
9:30 – 9:50am Introduction & Purpose of QLP Rajendra Boppana
9:50 – 10:10 am QLCDS – Data Submission Process Kim Massaro
10:10 – 10:20 am Q-course Logistics Kimberly Ward
10:20 – 10:40 am Incorporating QL into MAT 1043 Jonathan Brucks
10:40 – 11:00 am Incorporating QL into WRC 1013/1023 Gail Pizzola
11:00 – 11:30 am Analysis of Q-course Results Rajendra Boppana
11:30 – 12:45 am Lunch Break
12:45 – 1:05pm Incorporating QL into CRJ 3013 Rob Tillyer
1:05 – 1:25 pm Incorporating QL into KIN 3323 Sakiko Oyama
1:30 – 2:30 pm Discussion with the Provost Dr. Frederick
2:30 – 3:00 pm Wrap Up and Questions QLP Team
Location: JPL Faculty Center, Assembly Room
QLP Team
Dr. Mary DixsonQLP Implementation/ Training Coordinator
Kimberly WardQLP Program Coordinator
Dr. Gail PizzolaQLP Implementation/ Training Coordinator
Dr. Rajendra BoppanaProject Director of QLP
Kim MassaroQLP Program Coordinator
Prajan PradhanQLP Data Specialist
Robin Schulze QLP Coordinator & Analyst
Quantitative Literacy Program (QLP) Program Goals
Develop quantitative skills in undergraduate students
Implement effective teaching pedagogies and assessments to support the development of an exemplary quantitative scholarship program at the undergraduate level
Provide the organizational framework and resources for an institutional transformation to graduate a quantitatively informed citizenry
Q-Course
Current Course QLP Q-Course
• Learn Quantitative Skills
• Think critically• Interpret and use
data that naturally exist in the subject area
• Make informed decisions
• Makes the course more engaging
Data + Q. Methods + Redesign
Student Participation in QLP
Completion of one or more Q-courses is a graduation requirement
Started with core courses, expanded to major-required upper division courses
QLAT Advising Q-courses QLATGraduatio
nStudent
s
Faculty Participation in QLP
167 faculty members, 150+ TAs/graders participated in QLP since Fall ‘11
QLP Timeline
Year 1 First cohort of Q-faculty and students; 10 Q-courses Program website is created Every incoming freshman takes the entrance QLAT Course data is collected
Year 2 Developed online QLAT entrance exam Workshop (QLW) is created to address core complete
transfer students Individual faculty and overall Q-course reports are
developed Surveys of students begin
QLP Timeline (contd.)
Year 3 QLP maximized its enhancement of core courses QLP invites upper division courses for redesign Surveys to faculty, department chairs, and advisors begin Exit QLAT is administered to compare to the baseline Develops online version of QLW Workshop for transfer
students QLP awards first Faculty Excellence Award
Year 4 Data Collection process is streamlined 8 upper division courses are enhanced with QL Surveys to employers and alumni begin QLP awards second Faculty Excellence Award
QLP Growth
Year One (2011-12)
Year Four (2014-15)
20 faculty 100 faculty
10 Q-Courses 27 Q-Courses
113 Sections 556 Sections
6,845 enrollments 26,599 enrollments
2015-16 Q-course List
ANT 2033ANT 2043ARC 4183ARC 4283BIO 1233BIO 1404COM 3073CRJ 3013ECO 2003ECO 2013ECO 2023ENG 2413ES 2013
HIS 2123 HIS 2133 KIN 3323MAT 1043MDS 4983PHI 1043POL 1013POL 1113
SOC 1013SOC 3323SPE 3603STA 1053WRC 1013WRC 1023
27 Q-courses 19 core, 8 upper division
Student Coverage: First-time, Full-time Students
Completion of a Q-course by year by freshmen cohortsEach colored segment in a bar represents one year
Student Coverage: Transfer Students
Completion of a Q-course by transfer student cohortsEach colored segment in a bar represents one year
Student Coverage: Graduating Students
QLC: Completed at least one Q-courseQLW: Completed a 3-hour workshop instead of a Q-courseQLE: Exempted based on major; Q-course not completed
Student Enrollments in Q-Courses
Fall 2015 enrollments are based on Aug 13 2015 data
Q-Course Performance Analysis (Fall 2014)
12 out of 19 (63%) of core level Q-Courses showed significant increase from pre to post-test Out of those 12, 11 courses reported an average score
greater than 70 on post-test.
5 out of 8 (63%) of upper-division Q-Courses showed significant increase from pre to post-test Out of those 5, 4 courses reported an average score
greater than 70 on post-test.
Q-Course Performance Analysis (Spring 2015)
13 out of 18 (72%) of core level Q-Courses showed significant increase from pre to post-test Out of those 13, 10 courses reported an average score
greater than 70 on post-test.
6 out of 8 (75%) of upper-division Q-Courses showed significant increase from pre to post-test Out of those 6, all 6 courses reported an average
score greater than 70 on post-test.
Presenter: Kim Massaro
QLP Data Submission Process
Pre/Post Test
Give the Pre-test before any Q-material is taught
Grade the Express QuestionBubble in the score for the Express question
on the Parscore for each studentTake Parscores to Testing ServicesMake sure to include a note: give permission
to upload data to the QLP drive
How to Bubble Express
Large Form: SUBJ Score
5
5
Small Form: Exam #
QLCDS Website
qlcds.it.utsa.edu (open using Mozilla Firefox) Log on with your abc123 and password
Download your courses’ template Course coordinators will create the templates at the
beginning of the semester
Upload the item level data
Course Coordinators
• Create pre-test template– Upload Pre-test document with SLO’s and taxonomy– Upload rubric for the Express Question with Answer
Key– Upload a dummy file to generate SLO’s
• Create Homework template– Upload Q-assignment with SLO’s, taxonomy, and
answers
• Create post-test template
Best Practices
For Course Coordinators: Create a course specific “primer” for faculty teaching
Ex: Materials handbook, Blackboard Learn shell, packet, etc…
Meet with new faculty prior to beginning of semester
Create QLCDS templates during first month of semester
When uploading materials, include SLOs, taxonomies, and correct answers clearly marked on document/rubric.
Best Practices (contd)
For New Faculty teaching Q for first time: Attend recommended trainings with the QLP team
Meet with course coordinator and other “Q” team members
Contact Testing Services for ParScore training (optional)
Visit QLP program website (http://qlp.utsa.edu) for more information on the program, workshop materials, video presentations, tutorials, and technical reports.
Best Practices (contd)
For All Q-Faculty Contact the Course Coordinator at beginning of each
semester Bookmark the following websites:
http://qlp.utsa.edu QLP program website http://qlcds.it.utsa.edu Data Collection Website (very
important!) http://qlp.utsa.edu/faculty (Resources include workshop
materials, technical reports, and data collection tutorials) https://medialibrary.utsa.edu/Brwose/Category/57 (video
presentations from QLP staff and other Q-faculty) Make sure TA/Grader attends QLP Training Workshop
(8/28) or schedules one-on-one training Verify that TA/Grader has all documents, rubrics, and
understands the needs of the course.
Presenter: Kimberly Ward
Q-Course Logistics
Q-Course Logistics
Beginning of the semester1. Course Coordinator creates course template for pre-test,
assignment, and post-test on QLCDS website
2. Faculty register pre/post-test with Parscore at Testing Services
3. Faculty meet with TA/Grader and provide documents and rubrics for grading Q materials.
4. Faculty give Blackboard gradebook access to TA/Grader (optional)
5. All Q-Faculty submit pre-test data by Wednesday Sept. 9th Must state “Give permission for Testing Services to send results to
QLP”
Q-Course Logistics (contd.)
Middle/End of the semester1. Faculty or TA/Grader downloads “Homework” Excel
template from QLCDS website
2. Faculty or TA/Grader enters student roster information and itemized student scores in Excel template columns.
3. Faculty or TA/Grader uploads the completed Excel template and indicates scoring criterion for each question.
4. “Q” Homework data is submitted to QLCDS by Friday Dec. 4th
5. Post-test ParScore forms are dropped off at Testing Services by Friday Dec. 4th
1. Unless on Final Exam, then due Tuesday Dec. 15th (Grades Due Deadline)
Q-Course Logistics (contd)
Should there be problems/missing data after submission, the QLP will email the faculty member to resolve the issue.
1st Email – Individual Faculty
2nd Email –Individual Faculty and Course Coordinator
3rd Email—Individual Faculty, Course Coordinator, and Department Chair
Murphy’s Law and QLP
When things go wrong/errors happen:TA/Grader Assigned FacultyFaculty Course CoordinatorCourse Coordinator QLP Team
Email the QLP team at qlp@utsa.edu NOT individual team member emails
QLP Data Submission Process & Due Dates
Pre-test Data (via Parscore) Due September 9th (one week after census)
Homework Data (for Upper-division courses only) Due December 4th (study days)
Post-test Data (via Parscore) Due December 4th (study days)
Introduction to MathematicsPresenter: Jonathan Brucks
Incorporating Quantitative Literacy into MAT 1043
Freshman Composition I and IIPresenter: Gail Pizzola
Incorporating Quantitative Literacy into WRC 1013/1023
Presenter: Rajendra Boppana
Analysis of Q-course Results
Will resume at 12:45pm
Break for Lunch
Agenda
12:45 – 1:05pm Incorporating QL intoCRJ 3013
Rob Tillyer
1:05 – 1:25 pm Incorporating QL intoKIN 3323
Sakiko Oyama
1:30 – 2:30 pm Provost’s Discussion Dr. Frederick
2:30 – 3:00 pm Wrap Up and Questions QLP Team
Research Design and AnalysisPresenter: Rob Tillyer
Incorporating Quantitative Literacy into CRJ 3013
BiomechanicsPresenter: Sakiko Oyama
Incorporating Quantitative Literacy into KIN 3323
With Dr. John Frederick UTSA Provost and Vice President for Academic
Affairs
Round Table Discussion
Thank you for your participation.
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