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Everyday Data

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Collecting and Using Data in The Learning Commons. Everyday Data. Bernard Grindel & Tracy Hallstead, Quinnipiac University. Hello. Berny Grindel: Assistant Director of The Learning Commons, supervisor of CRLA certified peer tutoring program - PowerPoint PPT Presentation
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Everyday Data Collecting and Using Data in The Learning Commons Bernard Grindel & Tracy Hallstead, Quinnipiac University
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Page 1: Everyday Data

Everyday DataCollecting and Using Data in The Learning

Commons

Bernard Grindel & Tracy Hallstead, Quinnipiac University

Page 2: Everyday Data

HelloBerny Grindel: Assistant Director of The Learning Commons, supervisor of CRLA certified peer tutoring programTracy Hallstead: Academic Specialist, supervisor of Supplemental Instruction program (aka Peer Fellow program)Quinnipiac University

Hamden, Connecticutprivate, ~ 6500 undergrads, non-sectarian

(both NYY and BOS)Learning Commons – a nexus of academic support

Page 3: Everyday Data

Rationale and Agenda

Data shouldn’t be difficultWe don’t favor qualitative or quantitative…but qualitative, aggregated and crunched, becomes quantitativeWe will cover collection, day-to-day use, and strategic useOur three programs are peer tutoring, peer fellow (Supplemental Instruction), retention

Page 4: Everyday Data

Data Collection – Discussion

Question 1) What data do you currently collect? – or – How do you currently collect data?Question 2) What do you do with this data?

Page 5: Everyday Data

Data Collection – peer tutoring

Professor Report “system”electronic (web-based) data collectiondata aggregated in a database (Access)

Can replicate effects with paper-based approachAppointment sign-up sheetsEnd of semester evaluations by users

Page 6: Everyday Data

Peer Tutoring – Web-based Professor Report System

Page 7: Everyday Data

Peer Tutoring – Session Notes (prelude to Professor Report)

Page 8: Everyday Data

Peer Tutoring and Peer Fellow

Program – End of Semester

Evaluation of Tutor and Center

Page 9: Everyday Data

Data Collection – peer fellow program

Planning SheetsAttendance Rosters Learning Commons Reports on Student AttendanceTimesheetsGrade Reports

Page 10: Everyday Data

Peer Fellow Program – Attendance Roster

Page 11: Everyday Data

Peer Fellow

Program – Planning Sheets

Page 12: Everyday Data

Data Collection – Retention Requires tight cooperation between Learning Commons and Information SystemsDeficiency Rosters (information from Datatel)

SAT scores, math/verbalWithdrawals and leaves of absenceActive students not registeredOutstanding incompletesGPA, term and cumulativeCredits earnedAdvisor contact information

Improvement Plan (for Probation or Credit Deficient students)

Page 13: Everyday Data

Data Collection – Improvement Plan

Page 14: Everyday Data

Data Collection – Retention Alert

Faculty/Staff contribution to student’s “case”Record of “automatic” e-mails triggered by

faculty/staff contributionsmidterm gradesprobation/credit deficiency/etc.

Record of Learning Commons interactions(meeting information also copied into LC

database)

Page 15: Everyday Data

Retention Alert – faculty contribution

Page 16: Everyday Data

Data Collection – Discussion and Planning

Question 3) How would you like to change/add to your current data collection practices?Question 4) What are the obstacles to making those changes?

Page 17: Everyday Data

Daily Data Use – all servicesCommon database collects

professor reports (peer tutoring)students’ meetings with full-time staffpeer fellow study group attendanceno-shows for appointments

Retention Alertfaculty contributionsmidterm gradesstatus warnings (credit deficiency, probation, etc.)

Page 18: Everyday Data

All Services Report in database

Page 19: Everyday Data

Daily Data Use – peer tutors and fellowsTutorials produce Professor Report e-mails:

routed through shared e-mail fileGrad Assistants vet, edit, and send to faculty

Peer Fellows submit weekly prep sheets and time sheetsSupervisors’ use:

keeping tabs on tutors/fellowsprofessor reports and prep sheets indicate pedagogy/procedure allotting space/time to meet students’ demandanswering faculty/administration queries (sometimes parents’ too)

Page 20: Everyday Data

Peer Tutoring – a professor report e-mailed to course instructor

Page 21: Everyday Data

Peer Tutoring – professor report summaries drawn from database

Page 22: Everyday Data

Daily Data Use – retention Retention Alert faculty contributions and Datatel reports

generate automatic e-mails to studentsdaily cross-check against deficiency rosters

triage! first outreach to students with multiple absences,

multiple early warning reports, and failures at midterm

academic advisors, LC staff (504 Coordinator, Learning Specialists) track each others’ work in Retention AlertMeeting information (w/LC full-time staff or peer educators) cross-checked against deficiency rosters – outreach aligned with degree of disengagement

Page 23: Everyday Data

Retention Alert – “working the case”

Page 24: Everyday Data

Daily Data Use – Discussion and Planning

Question5) Which of your programs is working, which is not?Question 6) How do you use information generated by the programs to manage them?Question 7) What kind of organization of or access to information would inform better program management?

Page 25: Everyday Data

Strategic Data Use – peer tutoring

Hiring/recruiting – top 10 trendingGraphs to visualize service useEnd of Semester peer tutor evaluations

measure of busynessProfessor Report review

Metacog Projectfeedback to facultypotential for in-depth description and data for

assessment

Page 26: Everyday Data

Peer Tutoring – tutoring histogram by school of enrollment

Page 27: Everyday Data

Peer tutoring – Metacog project

Page 28: Everyday Data

Strategic Data Use – Peer Fellow Program

Grade/outcome comparisonOfferings for next semester and longer term futureFaculty and Student buy-in

End of semester evaluationsTraining objectivesMetacognitive objectives for students

Page 29: Everyday Data

Peer Fellow Program – grade/outcome comparison

2.653.17 2.82

0.000.501.001.502.002.503.003.504.00

Not Attending (32) Attending (16) Total (48)

12FA BIO 211-055 or More Study Sessions

• About 33% (16/48) of the class attended five or more study sessions

• The average GPA of the students who attended five or more sessions was higher (3.17) than the GPA of the students who did not attend (2.65)

• In comparison to the previous graph, as students attended more study sessions their grades improved significantly

Page 30: Everyday Data

Peer Fellow Program – user survey data

Page 31: Everyday Data

Strategic Data Use – Retention

Academic Specialist ReportsStaff evaluation and trainingStaff hiring

Trending of withdrawn, suspended, dismissed students

Monitoring of percentage points for retention and graduation

Page 32: Everyday Data

Retention – End-of-Semester Academic Specialist Report

Page 33: Everyday Data

Strategic Data Use – Institutional LevelInstitutional Support

facilitiesstaff (professional and student)

Institutional Engagementsupport for faculty/staff initiativesdata for faculty to incorporate in course/curriculum

design

Page 34: Everyday Data

Strategic Data Use – Discussion and Planning

Question 8) What role do you aspire to playing at your school?Question 9) Which student behaviors and outcomes are associated with that role?Question 10) To whom do you need to make your case?Question 1) What data will you need to collect? And how will you collect it?


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