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
628 544 554 292 3332 829
1039 623 308 4918 840 Sc+othe r
1009 1105 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 studies Arts Law Selectivity
49 % 18 % Yield 37 % 72 % 68 % 8%
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 others 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 students overall success at learning The students CGPA, relative to others, is a performance indicator 26 Student learning Students 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
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 Qubec 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 Qubec 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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