SAGA Schedule Academic Groundwork Assistance Outline Organizational Chart of Team Societal Problem SAGA Solution Case Study Process Flow for ODUs System Issues with Current System What is Needed to Solve it ID: 763428
Download Presentation The PPT/PDF document "SAGA Schedule Academic Groundwork Assis..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
SAGA Schedule Academic Groundwork Assistance
OutlineOrganizational Chart of Team Societal ProblemSAGA Solution Case Study Process Flow for ODU’s SystemIssues with Current SystemWhat is Needed to Solve it?Software Diagrams Improved Process FlowMilestonesSchemasCompetitionRisksWhat’s in the Box ?4/14/2011 SAGA 2
Project Leads 4/14/2011 SAGA 3 George Miller Daniel Longest
Database Experts 4/14/2011 SAGA 4 GT Weeks Aaron Evans Michael Olson
Software Developers 4/14/2011 SAGA 5 Byron Thulin Ian Byrnes Ronald Thorne
Risk Management and Security 4/14/2011 SAGA 6 Matthew Ellis Roosevelt Cooper Jeremy Dart
Domain Experts I rwin Levinstein Department Scheduler and Database Expert Janet Brunelle Computer Science Advisor Elizabeth Batu Registrar Scheduler Terri Mathews Assistant Dean for the College of Science 4/14/2011 SAGA 7
Higher Education in the U.S.4/14/2011 SAGA 8 5,758 higher education institutions in the United States, the second largest number in the world 19,764,000 students are currently enrolled in these institutions Roughly 100,000,000 courses or more being taken nation wide per semester
Resources Involved in the Scheduling Process4/14/2011 SAGA 9
Resources Involved in the Scheduling Process 4/14/2011 SAGA 10 Limited resources Poor capacity management
Resources Involved in the Scheduling Process 4/14/2011 SAGA 11 Inconsistent and limited faculty input
Resources Involved in the Scheduling Process 4/14/2011 SAGA 12 Not meeting needs of students Low enrolled courses I neffective prediction of demand
Resources Involved in the Scheduling Process 4/14/2011 SAGA 13 No current input Forced to take courses they don’t want Delayed graduation
The Root of the Problem The lack of an effective scheduling software results in universities: Requiring faculty and staff spending undue amounts of time on the scheduling process Developing a negative image among potential studentsCreates an unhappy student body Having less than optimal graduation rates (Average of five years for graduation) 4/14/2011 SAGA 14
Solution: Build SAGASchedule Academic Groundwork Assistance S oftware suite which: Includes a predictive scheduling engine Allows for efficient management of room ownership Considers student & faculty preference and needs Allows for customization to the current system Digitally automates the collection of faculty and student preferences 4/14/2011 SAGA 15
Case Study: ODU – Computer Science 4/14/2011 SAGA 16Old Dominion University’s Computer Science Department represents a stable case that demonstrates problems that similar universities have with scheduling and utilization of resources
Utilization of Large Rooms* by Department at ODU4/14/2011 SAGA 17 *rooms with 50 or more seats Optimum range
Improving Freshman Retention4/14/2011 SAGA 18 Schools Rate Eastern Michigan University 71% Old Dominion University 80% Florida International University 81% Georgia State University 82% Oakland University 72% Universities similar to ODU will benefit the most from SAGA.
Current Registrar Process4/14/2011 SAGA 19
Current Department Process4/14/2011 SAGA 20
Issues With the Current ODU Scheduling System Labor intensive Error proneInefficientLack of student contributions Inconsistent use of tools4/14/2011 SAGA21
The SAGA SolutionSAGA is a customizable, intuitive, scheduling software suite that can be integrated into any existing university infrastructure. SAGA will support student input, faculty preferences, prediction, report generation, and efficient classroom utilization on a semester by semester basis. 4/14/2011 SAGA 22
SAGA ObjectivesCapture Faculty & Student wants and needs Provide a schedule management tool for the department Offer customized interface for use with current tools Provide a predictive engine that utilizes historical data4/14/2011 SAGA23
What’s in the Box?4/14/2011 SAGA 24
SAGA’s Improved Utilization4/14/2011 SAGA 25
SAGA’s Improved Utilization 4/14/2011 SAGA 26 Poor room allocation A system that will check for common mistakes and auto correct
SAGA’s Improved Utilization 4/14/2011 SAGA 27 Faculty preferences
SAGA’s Improved Utilization 4/14/2011 SAGA 28 Efficient use of classroom space
SAGA’s Improved Utilization 4/14/2011 SAGA 29 Students interest
Major Functional Component Diagram 4/14/2011 SAGA 30
Current Registrar Process4/14/2011 SAGA 31
Improved Registrar Process4/14/2011 SAGA 32
Current Department Process4/14/2011 SAGA 33
Improved Department Process4/14/2011 SAGA 34
Software Milestones4/14/2011 SAGA 35 Testing
Database4/14/2011 SAGA 36 Testing Student Profile Faculty Profile Extracted Historical Data
Data Miner4/14/2011 SAGA 37 Testing Extracts the University DB Processes into SAGA Builds a report
Interfaces4/14/2011 SAGA 38 Testing Registrar & Department Student & Faculty Root Level
Prediction Engine4/14/2011 SAGA 39 Testing Schedule Assistance Algorithm Evolves over time Evaluates data trends
Database Schema 4/14/2011 SAGA 40
Historical Database ERD4/14/2011 SAGA 41
Student Database ERD4/14/2011 SAGA 42
Faculty Database ERD4/14/2011 SAGA 43
Data Miner and Report4/14/2011 SAGA 44
Report OutputThis report will contain :Receive data based reflecting student and faculty preferences The predicted cap and actual attendance per course Rooms based on scheduling and availability 4/14/2011SAGA 45
Prediction Engine4/14/2011 SAGA 46
GUI Site Map4/14/2011 SAGA 47
GUI Prototype4/14/2011 SAGA 48
GUI Student Profile4/14/2011 SAGA 49
SAGA Software4/14/2011 SAGA 50
Competition Preferences4/14/2011 SAGA 51 Prefences SAGA Schedule Whiz Schedule 25 IQ.Session Scheduling Studio 7 Faculty Student Room Room Ownership Preference Priority Course Dependencies Student Curriculum Track
Competition Scheduling Features4/14/2011 SAGA 52 SAGA Schedule Whiz Schedule 25 IQ.Session Scheduling Studio 7 Prediction Engine Dept to University Optimizer Multi-Term Forecaster 3 rd Party Integration
Return on Investment4/14/2011 SAGA 53
Security4/14/2011 SAGA 54
Risks4/14/2011 SAGA 55 I 5 M 4 F2 F1, F3 S2C1 C2, C3 P 3 T5, T4 S1T6 A 2 T3, T2, T1 C 1 T 1 2 3 4 5 PROBABILITY Legend: Financial Risks: Technical Risks: F1: Development Cost T1: Getting enough student data F2: Product Cost T2: Getting enough faculty data F3: Lack of Funding T3: Getting the right student data T4: Getting the right faculty data T5: Various ODU systems don't implement well T6: Historical data difficult to collect & manage Customer Risks Schedule Risks C1: University Politics (Institutional Inertia) S1,T6: Historical data difficult to collect & manage C2: Unable to force students to make profile S2, C1: University Politics C3: Unable to force faculty to create profile
Financial RisksF1 : Development Cost:Impact – 4, Probability – 2 Mitigation: Secure stable funding, efficient use of funding.F2: Product Cost High Impact 4, Probability - 1Mitigation: Set product cost to be competitive with the existing market. F3: Unable to fund project Impact – 4, Probability -2Mitigation: Secure development grants/investors to move project forward. 4/14/2011 SAGA 56
Technical RisksT1: Getting enough student dataImpact – 2, Probability – 3 Mitigation: Emphasis on having students fill out profiles.T2: Getting enough faculty data Impact – 2, Probability - 3Mitigation: Emphasis on having faculty fill out profiles.T3: Getting the right student dataImpact – 2, Probability - 3 Mitigation: Student profiles will provide the data we need. 4/14/2011SAGA 57
Technical RisksT4: Getting the right faculty data Impact – 3, Probability -3Mitigation: Faculty profiles will provide the data we need. T5: The various ODU systems do not integrate wellImpact – 3, Probability – 3Mitigation: Beta TestingT6: Historical data exists in a difficult form to collect and manage Impact – 3, Probability – 4Mitigation: Format the data to suit our needs.4/14/2011 SAGA 58
Customer RisksC1 : University politics (Institutional Inertia)Impact – 4, Probability – 3 Mitigation: Work with the university to resolve any issues.C2: Unable to force students to create a profileImpact – 4, Probability -4Mitigation: Emphasize the importance for the student to make a profile.C3: Unable to force faculty to create a profile Impact – 4, Probability -4Mitigation: Emphasize the importance for faculty to make a profile. 4/14/2011 SAGA 59
Schedule RisksS1,T6 : Historical data difficult to collect and manageImpact – 3, Probability – 4 Mitigation: Format data to suit our needs.S2,C1: University PoliticsImpact – 4, Probability – 3Mitigation: Work with the university to resolve any issues. 4/14/2011SAGA 60
Work Breakdown Structure4/14/2011 SAGA 61
Work Breakdown StructureHardware 4/14/2011 SAGA 62
Work Breakdown StructureSoftware Development 4/14/2011 SAGA 63
Work Breakdown StructureSoftware Development 4/14/2011 SAGA 64
Staffing4/14/2011 SAGA 65 Position Number of Employees Salary Hourly Rate Cost Project Manager 1 $84,000 $ 42.00 $74,928 Software Engineer 3 $ 68,000 $ 34.00 $181,968 Financial Analyst 1 $ 50,000 $ 25.00 $44,600 Marketing Director 1 $ 68,000 $ 34.00 $60,656 Documentation Specialist 1 $ 38,000 $ 19.00 $33,896 HR Manager 1 $ 58,000 $ 30.00 $53,520 Software Tester 1 $ 60,000 $ 30.00 $53,520 Web Developer 1 $ 50,000 $ 25.00 $44,600 Database Administrator 2 $ 80,000 $ 40.00 $142,720 Domain Expert 1 $ 100,000 $ 52.00 $92,768 Salary Cost $783,176 40% Overhead $313,270 Total Cost $1,096,896
What’s not in the Box?Will not replace current tools Will not change university politics Will not automate the schedule 4/14/2011SAGA 66
Schedule Academic Groundwork Assistance The ultimate purpose of SAGA is to ease the burden of those involved with the scheduling process while simultaneously catering to student and faculty needs and interests. SAGA improves the course making process by providing collaborative input from students, faculty, and historical trends to assist in predicting demand for the future. 4/14/2011SAGA 67
ResourcesMathews, Terri (Fall 2010) – Mathews, Terri. (2011). Fall 2010 Room Utilization By Priority Rooms from BANNER Cost of Students - http://www.collegemeasures.org/reporting/Institution/Scorecard/232982.aspxRetention Rate - The Carnegie Foundation for the Advancement of Teaching. (2011, March 30). Carnegie classifications. Retrieved from http://classifications.carnegiefoundation.orgCompetition Data – www.thoughtitmus.com , corp.collegenet.com , www.comquip.com , www.collegescheduler.com , www.lantiv.com Fiver Year Graduation - http://www.collegeparents.org/members/resources/articles/reasons-why-your-college-student-might-not-graduate-four-years 4/14/2011 SAGA 68