Canada by gjjur4356

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									               Introduction


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




                                          1
       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

                                          2
       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

                                           3
       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
                                            4
     Undergraduate admissions:
       Business Intelligence

Admissions , Faculty of
Science, Fall 2009
                  ES       PIA
Applicants      7557      6615
Offers          3509      2933
Registration    1194       894

                                 5
        Undergraduate admissions:
          Business Intelligence



You need to understand the
  data definitions !!!



                                    6
        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.
                                               7
               Undergraduate admissions
                 Operational 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
                                                        8
               Undergraduate admissions
                 Operational planning
Faculty of
Science -
Applications

        CEGE Ontario ROC
        P    HS      HS     US HS OS HS Transf. Total

all     1482 1945    1457   1583   1177   600   8250

Only    473    840   628    544    554    292   3332
Sc+othe
r       1009 1105    829    1039   623    308   4918
                                                    9
       Undergraduate admissions
         Operational 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

                                          10
             Undergraduate admissions
              Performance indicators

The usual performance indicators are
• Selectivity = offers / applications
   – low is good
• Yield = registrations / offers
   – High is good




                                        11
         Undergraduate admissions
          Performance indicators

                            Religious
              Arts   Law    studies

Selectivity   49 %   18 %    8%
Yield         37 %   72 % 68 %

                                        12
            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
                                               13
            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
          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;

                                     15
                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 )

                                            16
       Enterprise Data Warehouse
                 Project
• system: Microsoft BI Solution
• Implemented:
   –student data: McGill + external
   –grad. student funding data
   –Analytics : graduate student capacity
    planning
• Next steps : ???
                                            17
Student data warehouse – class lists




                                       18
Student data warehouse – class lists




                                       19
      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                     20
Graduate Capacity Indicator




                              21
           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


                                           22
            Business Intelligence
• Issues with access – how open ?
   – Partially a question of user-friendly
     tools
   – Partially a question of policy
   – Partially a question of mindset.



                                             23
        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 ?


                                     24
          Performance indicators
Why benchmark?
• Just to really know ?
• Accountability to others ?
• Plan how to improve ?




                                   25
               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
                                            26
               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

                                                 27
         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
                                          28
         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)


                                         29
         Performance indicators
This year, Departments were given
  guidelines but were given wide scope to
  benchmark their performance. Reports
  go to the Dean.




                                            30
         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.

                                            31
             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

                                               32
       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%



                                           33
      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?
                                       34
      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
                                          35
      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
                                          36
                   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
                                           37
      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 )

                                       38
              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

                                           39
          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
                                         40
          Department of History
                Fall 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
                                        41
      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
                                           42
      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
                                          43
       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).
                                         44
       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.
                                           45
            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
                                            46
       Student mobility: CREPUQ Exchanges


The numbers are not large.
For the Fall 2008
McGill
• Incoming =        294
• Outgoing      =   283
All Quebec universities = 5064

                                            47
•   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

•




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