Everyday DataCollecting and Using Data in The Learning
Commons
Bernard Grindel & Tracy Hallstead, Quinnipiac University
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
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
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?
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
Peer Tutoring – Web-based Professor Report System
Peer Tutoring – Session Notes (prelude to Professor Report)
Peer Tutoring and Peer Fellow
Program – End of Semester
Evaluation of Tutor and Center
Data Collection – peer fellow program
Planning SheetsAttendance Rosters Learning Commons Reports on Student AttendanceTimesheetsGrade Reports
Peer Fellow Program – Attendance Roster
Peer Fellow
Program – Planning Sheets
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)
Data Collection – Improvement Plan
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)
Retention Alert – faculty contribution
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?
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.)
All Services Report in database
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)
Peer Tutoring – a professor report e-mailed to course instructor
Peer Tutoring – professor report summaries drawn from database
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
Retention Alert – “working the case”
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?
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
Peer Tutoring – tutoring histogram by school of enrollment
Peer tutoring – Metacog project
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
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
Peer Fellow Program – user survey 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
Retention – End-of-Semester Academic Specialist Report
Strategic Data Use – Institutional LevelInstitutional Support
facilitiesstaff (professional and student)
Institutional Engagementsupport for faculty/staff initiativesdata for faculty to incorporate in course/curriculum
design
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?