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Using Data to Change Student (and Faculty) Behavior About Academic Integrity John Fritz & Teresa Viancour UMBC Educause MARC 2008.

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Presentation on theme: "Using Data to Change Student (and Faculty) Behavior About Academic Integrity John Fritz & Teresa Viancour UMBC Educause MARC 2008."— Presentation transcript:

1 Using Data to Change Student (and Faculty) Behavior About Academic Integrity John Fritz & Teresa Viancour UMBC Educause MARC 2008

2 More Information "Copyright John Fritz, Teresa Viancour, This work is the intellectual property of the author. Permission is granted for this material to be shared for non-commercial, educational purposes, provided that this copyright statement appears on the reproduced materials and notice is given that the copying is by permission of the author. To disseminate otherwise or to republish requires written permission from the author."

3 Overview Problem Solution Lessons Learned Next Steps Q & A

4 Problem Nationally, 70 percent of college students admit to cheating at least once, according to the Center for Academic Integrity. UMBC AI Survey Results (2003)  Nearly half of undergrad respondents self-reported cheating on tests (46%) or plagiarizing (43%).  Faculty report observing higher percentages (52% & 80%)  Source: Executive Summary | ReportExecutive SummaryReport UMBC Enrollment = 12,041 (9,464 undergrad, 2,577 grad) Academic Conduct Committee  Less than 150 misconduct reports filed by faculty annually.  Why don’t we get 5,000 faculty reports?

5 Solution: An Online Database Facilitate and standardize the process for reporting misconduct incidents. Provide baseline data to understand university AI problems/successes over time. Biology Department proposed (threatened?) building their own online database. Use technology to change faculty and student behavior regarding AI reporting and compliance.

6 Key Functions

7 Disclaimer

8 1st Incident Faculty use student's userid to file an online report AFTER meeting with the student. confirmation sent to instructor, student and chairs of the Faculty Senate Academic Conduct and UMBC Academic Integrity Committees Details of report only visible by the parties above. Student has fifteen (15) days to confirm or contest the report; failure implies confirmation by the student.

9 Subsequent Incident AIDB notifies ACC and AI chairs and student in question. Student may be subject to a more severe penalty. ACC/AI chairs may consult faculty who submitted report to determine penalty. Summary data by infraction, penalty, department/discipline, semester/year, etc.

10 Prior Report? Disclaimer Faculty Enters Student Userid Student ACC/AI Chairs Instructor Yes Add Record AI Reporting Database Workflow Meet with student first? No Exit Program Confirm/Alert No 1st Incident Yes Subsequent Incident

11 Alert to Students Sent through myUMBC Portal

12 1st Incident Notification “A UMBC instructor has filed an academic misconduct report about you. In addition, both chairs of the Faculty Senate Academic Conduct and UMBC Academic Integrity committees have been notified of this report. To confirm or contest the details of your report (required 15 days from now) please log in to myUMBC or click here. Failure to do so will imply your confirmation of the report. Note: If another UMBC instructor subsequently files a report about you, the ACC and AI chairs will be notified, and you may be subject to a more severe penalty including, but not limited to, a permanent notation on your transcript, suspension or expulsion from the university.”

13 Summary Reporting Number of violations by...  Type of infraction  Penalties imposed  Students Involved  Reported by Faculty  Departments and/or Disciplines  Semester/Year Should summary data (no links to records) be visible by all users? “Drill down” access to individual records granted to...  ACC Chair, Faculty, Deans, Chairs, etc.

14 Misconduct Type

15 Penalty Type

16 Department Reports

17 People Reports

18 Results? 104 reports on 101 students since 1/1/07. Student behavior change:  No subsequent incident report of these students. Faculty behavior change:  Number of reports seem to be growing as more faculty become aware of AIDB.

19 More Information Background Screen Shots Narrated Demo Initial Specs

20 Lessons Learned Where shall we begin?

21 How Was Campus Involved? 2003 to 2007  AIDB proposed by AI Tech Committee,  Reviewed by the Student Government Association  Funded by Office of the Provost  Developed by the Office of Information Technology  Approved by Office of Legal Affairs views the database reports as protected educational records under the Family Educational Rights and Privacy Act (FERPA).  Tested and reviewed by faculty and student volunteers  QA by chairs of AC & AI committees  Piloted Fall 2006, launched Spring 2007

22 Campus Review Academic Conduct Committee  Review with Chair Joan Korenman (Fall 2004) AI Integrity Committee  Technology Subcommittee (Fall 2004)  Executive Committee (2/8/05)  Full Committee (3/10/05) Faculty Senate  Computer Policy Committee (4/18/05)  Pilot (Fall 06) SGA  Review with President Dominic Cirincione (Spring 2005)  Senate (4/11/05)

23 Faculty Concerns AI is not a university issue; it is a classroom issue. Faculty alone should deal with it. Faculty don’t have time to report. My take: some faculty don’t want to confront students, so don’t report incidents. AI conversations are ultimate “teaching moment.”

24 Student Concerns What recourse, if any, do students have to identify cheating that goes unreported and unpunished by faculty? Beyond faculty, who will have access to a misconduct report (e.g., adjunct instructors, advisors, staff, GAs who teach their own classes)?

25 Technology Concerns Don’t “get ahead” of campus. Didn’t want rogue effort by one dept. Privacy & security. Sustainability. Scope creep. Integration with other SIS & portal.

26 Consistency Should faculty be allowed to penalize a student for academic misconduct without at least filing an incident report used for broader institutional tracking and analysis? If yes... What will prevent inconsistent interpretations and applications of academic misconduct across the university? How will we know the scope of UMBC’s AI problem? UMBC’s Academic Conduct Policy says “No”

27 Fairness What provisions, if any, should be implemented to prevent faculty from “browsing” their class list to identify students who have prior violations?  Disclaimer on first screen  “Hiding” (not expunging) a first offense if there are no others three semesters later.  Tracking a set number of queries without filing of a report?  Note: Faculty CAN view any student’s transcript now What prevents a faculty member from being unduly biased in how he or she treats students who have “learned their lesson”?

28 Prior Report? Disclaimer Faculty Enters Student Userid Student ACC/AI Chairs Instructor Yes Add Record AI Reporting Database Workflow Meet with student first? No Exit Program Confirm/Alert No 1st Incident Yes Subsequent Incident

29 Initially Proposed Schedule Campus Review (Spring ’05) Development (Late Spring ‘05) Testing (Summer ‘05) Launch (Fall ’05) Evaluation (AY )

30 Next Steps? Better reporting for ACC & AI chairs. Implement “alert” system requiring students to acknowledge report. Implement AI tutorial and Blackboard “quiz” students are expected to pass before turning in class work  Summer pilot in “Orientation” Bb Community.

31 Q & A


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