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Everyday Data

Everyday Data

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Everyday Data

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  1. Collecting and Using Data in The Learning Commons Everyday Data Bernard Grindel & Tracy Hallstead, Quinnipiac University

  2. Hello Berny Grindel: Assistant Director of The Learning Commons, supervisor of CRLA certified peer tutoring program Tracy Hallstead: Academic Specialist, supervisor of Supplemental Instruction program (aka Peer Fellow program) Quinnipiac University Hamden, Connecticut private, ~ 6500 undergrads, non-sectarian (both NYY and BOS) Learning Commons – a nexus of academic support

  3. Rationale and Agenda Data shouldn’t be difficult We don’t favor qualitative or quantitative… but qualitative, aggregated and crunched, becomes quantitative We will cover collection, day-to-day use, and strategic use Our three programs are peer tutoring, peer fellow (Supplemental Instruction), retention

  4. 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?

  5. Data Collection – peer tutoring Professor Report “system” electronic (web-based) data collection data aggregated in a database (Access) Can replicate effects with paper-based approach Appointment sign-up sheets End of semester evaluations by users

  6. Peer Tutoring – Web-based Professor Report System

  7. Peer Tutoring – Session Notes (prelude to Professor Report)

  8. Peer Tutoring and Peer Fellow Program – End of Semester Evaluation of Tutor and Center

  9. Data Collection – peer fellow program Planning Sheets Attendance Rosters Learning Commons Reports on Student Attendance Timesheets Grade Reports

  10. Peer Fellow Program – Attendance Roster

  11. Peer Fellow Program – Planning Sheets

  12. Data Collection – Retention • Requires tight cooperation between Learning Commons and Information Systems • Deficiency Rosters (information from Datatel) • SAT scores, math/verbal • Withdrawals and leaves of absence • Active students not registered • Outstanding incompletes • GPA, term and cumulative • Credits earned • Advisor contact information • Improvement Plan (for Probation or Credit Deficient students)

  13. Data Collection – Improvement Plan

  14. Data Collection – Retention Alert Faculty/Staff contribution to student’s “case” Record of “automatic” e-mails triggered by faculty/staff contributions midterm grades probation/credit deficiency/etc. Record of Learning Commons interactions (meeting information also copied into LC database)

  15. Retention Alert – faculty contribution

  16. 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?

  17. Daily Data Use – all services Common database collects professor reports (peer tutoring) students’ meetings with full-time staff peer fellow study group attendance no-shows for appointments Retention Alert faculty contributions midterm grades status warnings (credit deficiency, probation, etc.)

  18. All Services Report in database

  19. Daily Data Use – peer tutors and fellows Tutorials produce Professor Report e-mails: routed through shared e-mail file Grad Assistants vet, edit, and send to faculty Peer Fellows submit weekly prep sheets and time sheets Supervisors’ use: keeping tabs on tutors/fellows professor reports and prep sheets indicate pedagogy/procedure allotting space/time to meet students’ demand answering faculty/administration queries (sometimes parents’ too)

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

  21. Peer Tutoring – professor report summaries drawn from database

  22. Daily Data Use – retention Retention Alert faculty contributions and Datatel reports generate automatic e-mails to students daily 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 Alert Meeting information (w/LC full-time staff or peer educators) cross-checked against deficiency rosters – outreach aligned with degree of disengagement

  23. Retention Alert – “working the case”

  24. 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?

  25. Strategic Data Use – peer tutoring Hiring/recruiting – top 10 trending Graphs to visualize service use End of Semester peer tutor evaluations measure of busyness Professor Report review Metacog Project feedback to faculty potential for in-depth description and data for assessment

  26. Peer Tutoring – tutoring histogram by school of enrollment

  27. Peer tutoring – Metacog project

  28. Strategic Data Use – Peer Fellow Program • Grade/outcome comparison • Offerings for next semester and longer term future • Faculty and Student buy-in • End of semester evaluations • Training objectives • Metacognitive objectives for students

  29. Peer Fellow Program – grade/outcome comparison • 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

  30. Peer Fellow Program – user survey data

  31. Strategic Data Use – Retention Academic Specialist Reports Staff evaluation and training Staff hiring Trending of withdrawn, suspended, dismissed students Monitoring of percentage points for retention and graduation

  32. Retention – End-of-Semester Academic Specialist Report

  33. Strategic Data Use – Institutional Level Institutional Support facilities staff (professional and student) Institutional Engagement support for faculty/staff initiatives data for faculty to incorporate in course/curriculum design

  34. 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?