Introduction

1. Introduction Some nuts and bolts of performance indicators, business intelligence, and operational planning.

2. Undergraduate admissions An interesting example of : • Business intelligence • And its pitfalls: you need to really understand the data definitions • Operational intelligence • Operational planning • Enterprise planning

3. Undergraduate admissions Current process: • “Faculty Compacts” fix admission targets as part of the budget process. • e.g. Faculty of Science = 1120 • Admissions Offices work to hit the targets • The “control knobs” are the grade thresholds for each applicant type

4. Undergraduate admissions:Business Intelligence • McGill Fact Book • prepared by PIA • Drill down capability • Output to Excel for further analytics • Final Admissions (szraads) reports • prepared by Enrolment Services (ES) + IT Services/ISR • Output to print and Excel

5. Undergraduate admissions:Business Intelligence

6. Undergraduate admissions:Business Intelligence You need to understand the data definitions !!!

7. Undergraduate admissions: Operational intelligence • SQL queries to the Student Data Warehouse • Open, flexible, effective • Requires experienced users • “Minerva reports” (SZRA…) • accessed on the web; scores of weekly reports, from canned queries, with the data sliced and diced to suit faculties, departments, admission offices, etc. ; • output formats: .pdf and .xls ; • old fashioned, useful and heavily used.

8. Undergraduate admissionsOperational planning • The objective: target in the “faculty compact” • The challenge: • Different decision dates for different applicant categories • Each applicant can apply to 1 or 2 or more faculties • The applicant is not asked to prioritize applications • Admission decisions for each application are independent

9. Undergraduate admissionsOperational planning

10. Undergraduate admissionsOperational planning Build a model at the application level: • a selectivity matrix • a yield matrix • adjust the “threshold knobs” to generate offers leading to the target registrations

11. Undergraduate admissionsPerformance indicators The usual performance indicators are • Selectivity = offers / applications • low is good • Yield = registrations / offers • High is good

12. Undergraduate admissionsPerformance indicators

13. Business Intelligence • The concept is old, the name is new • Lots of vendor hype … • The new tools are more powerful and the code is easier to maintain • End users need little expertise to slice & dice • Warning: you still need to understand the data definitions

14. Business Intelligence Foundation of BI is rock solid data All McGill fine grained data • is stored in Banner (FIS, SIS, HRIS) • uses standard data definitions • is centrally validated Operational and enterprise data warehouses are fed from Banner

15. Business Intelligence Business analytics can be grouped into two categories: • Analysis of the current data to understand where we are; • Modelling to predict where we are going;

16. Data warehouses • Operational Data Warehouses • Enrolment Services, HR, ….. • Data bases and analytic tools developed by PIA (Planning and Institutional analysis) • Enterprise Data Warehouse Project (IT Services + PIA )

17. Enterprise Data Warehouse Project • system: Microsoft BI Solution • Implemented: • student data: McGill + external • grad. student funding data • Analytics : graduate student capacity planning • Next steps : ???

18. Student data warehouse – class lists

19. Student data warehouse – class lists

20. Graduate Capacity Indicator • Evidence-based strategic enrolment planning • Easy interactive form = dashboard tool • Combines data from different sources: • McGill and G13 universities • Enrolment, funding, … • Timeline • Delivered in December 2009 • In use since January 2010 • Complete dashboard version for the Fall 2010 for planning

21. Graduate Capacity Indicator

22. Business Intelligence • Issues with planning • Lots of “where we are” analytics • Not much modelling and predictive analytics • Trivial example: faculty enrolment model starting from admissions data

23. Business Intelligence • Issues with access – how open ? • Partially a question of user-friendly tools • Partially a question of policy • Partially a question of mindset.

24. Performance indicators A performance indicator is a measure linked to an activity • How fast can you run ? Benchmarking compares your performance indicator to other’s • Where did you place in the race ?

25. Performance indicators Why benchmark? • Just to really know ? • Accountability to others ? • Plan how to improve ?

26. Student learning A student takes a program of courses in order to learn. The learning is assessed grades CGPA The CGPA is a measure of the student’s overall success at learning The student’s CGPA, relative to others’, is a performance indicator

27. Student learning Student’s planning: • Optimize the learning? • Take as many and as challenging courses in your program as you can handle • Optimize the CGPA? • Take as few and as easy courses as you can while still meeting program requirements

28. Performance indicators Benchmarking • Need to select the performance indicators • Need to select who you will use as reference • Need to ensure that your measures and the reference measures are comparable

29. Performance indicators Benchmarking is serious business at McGill • Canada: G13 universities’ data exchange • USA: AAUDE (Association of American Universities Data Exchange) • NSSE (National Survey of Student Engagement)

30. Performance indicators This year, Departments were given guidelines but were given wide scope to benchmark their performance. Reports go to the Dean.

31. Performance indicators Two things to keep in mind: • Performance indicators relate only to what is quantifiable and ignore some of the most important things • The real performance and the bperformance indicator should not be confused.

32. Enrolment planning Admission stage: admit to the “admission program” • for Arts, this is to the faculty • for Engineering, it is to the department • Registration stage: • Program selection • Course selection

33. Civil Engineering (factbook) 2003 2004 2005 2006 2007 2008 2009 APP 675 636 612 757 866 1043 1098 ACC 309 318 323 404 403 377 378 REG 97 85 65 99 106 93 90 Sel 46% 50% 53% 53% 47% 36% 34% Yield 31% 27% 20% 24% 26% 25% 24%

34. Enrolment planning - courses One conceptual model : • Allow students almost complete freedom to chose • Decide course offerings in consequence • Do the scheduling (timetable and room allocation) in consequence. Is this practical? Or even possible?

35. Enrolment planning - courses Another conceptual model : • Decide the course offerings based on academic considerations • Decide the course enrolments based on academic considerations ( and past experience, and …. ) • Do the scheduling in consequence

36. Enrolment planning - courses • Courses are “capped” at room capacity • Schedule is available in March • Academic advising • Registration of returning students • Students have flexibility within the constraints This is the model used at McGill

37. BA degree Requirements : • Freshman program (30 cr) • only for admissions from high school • Major concentration (36 cr) • Minor concentration (18 cr) • Electives (36 cr) Total credits: 90 or 120

38. Major concentration in History 36 credits selected from 4 areas: • The Americas, • Europe, • Asia/Africa/Middle East, • Global/Thematic. ( Each area has a long course list )

39. Major in History Restrictions: • No more that 12 cr. at the 200-level • No more than 24 credits from any one area. • One 3 credits in history of the pre-1800 period • One 3 credits in history of the post-1800 period

40. Department of History(2009-10, Fall+Winter) • Professors: 32 • Total undergraduate FTE taught = 683 • 237 History students • 446 out-of-department students • Courses: about 120 • 4 courses/professor • 57 students /course

41. Department of HistoryFall 2010 • 63 undergraduate courses • Using 32 different classrooms • Room capacity: 6 seats to 220 seats • Total scheduled capacity: 3992 bodies = 399 FTE • Taught FTE in the Fall of 2009: 342 FTE

42. Teaching and learning spaces All classrooms are “university property” and centrally funded • Classroom booking is handled centrally • Instructors can specify their needs in terms of classroom equipment Laboratories are still “departmental property” but centrally funded

43. Teaching and learning spaces Deputy Provost >>Teaching and Learning Services >> >> Teaching and Learning Space Work group TLSWG allocates the equipment budget, and is responsible for teaching space design, equipment, etc

44. Student mobility: CREPUQ • Allows students registered as regular students in one Québec university (the home university) • to follow, within the framework of their program of study, one or more courses of their choice, for valid reasons, • at another Québec university (the host university).

45. Student mobility: CREPUQ Process –web based, developed and hosted by CREPUQ • Student submits a request • Program advisor and Registrar at home university approve • Registrar of the host university ( and academic adviser) approves.

46. Student mobility: CREPUQ Budget implications • Home university keeps the student fees • Host university receives the government funding • Automatically calculated by the government based on a flag in the university regular submission

47. Student mobility: CREPUQ Exchanges The numbers are not large. For the Fall 2008 McGill • Incoming = 294 • Outgoing = 283 All Quebec universities = 5064

48. http://mymcgill.mcgill.ca • http://www.mcgill.ca/students/courses/calendars/ • http://www.mcgill.ca/pia/mcgillfactbook • http://www.mcgill.ca/pmo/projects/edw/glossary/https://dbs.crepuq.qc.ca/mobilite-cours/4DSTATIC/ENAccueil.html