Elif Kongar*, Mahesh Baral and Tarek Sobh

1. Are We Accepting the Right Students to Graduate Engineering Programs: Measuring the Success of Accepted Students via Data Envelopment Analysis Elif Kongar*, Mahesh Baral and Tarek Sobh *Departments of Technology Management and Mechanical Engineering University of Bridgeport, Bridgeport, CT, U.S.A 2008 ASEE Annual Conference & Exposition Pittsburgh, PA June 22-25, 2008

2. Motivation – I : Difficulties in admission procedure due to increasing number of students in the SOE at UB. UB SOE Enrollment 2002 - 2008 # of Available Dual Degree Programs: 16 # of Available Concentration Areas / Graduate Certificate Programs: 34 Priceless  Being able to admit students in less than 5 minutes:

3. Motivation – II Lack of literature to suggest a solution for customized curriculum. Moore (1998) - an operational two-stage expert system to examine the admission decision process for applicants to an MBA program, and predict the degree completion potential for those actually admitted. Nilsson (1995) - differences in the predictive relationships between the scores of the Graduate Record Examination (GRE) and the graduate grade point average, and the scores of the Graduate Management Admission Test (GMAT) and the graduate grade point average. Landrim et al. (1994) - a value tree diagram for fifty-five graduate institutions offering the Ph.D. degree in psychology. The authors used this diagram to indicate the relative weight of admission factors used in the decision making process.

4. x2 = funding allocation ($) y3 = compatibility of research (IN) Introduction – Data Envelopment Analysis Efficiency = Output/Input (year) (year) (number)

5. A (0,800) B (2,500) C (12,450) A simple numerical DEA example Efficiency of Candidate B OB/OV = app. 70%

6. Two DEA Models • DEA Model I To rank the applicants according to: • e1 = number of below-B grades in math-related/technical courses in the BS transcript of the applicant, • e2 = number of semesters to complete the BS degree, • e3 = BS GPA of the applicant, • e4 = TOEFL score of the applicant, • e5 = GRE-Q score of the applicant, • e6 = number of years of work experience of the applicant.

7. Two DEA Models DEA Model I To rank the applicants according to: • e1 = number of below-B grades in math-related/technical courses in the BS transcript of the applicant, • e2 = number of semesters to complete the BS degree, • e3 = BS GPA of the applicant, • e4 = TOEFL score of the applicant, • e5 = GRE-Q score of the applicant, • e6 = number of years of work experience of the applicant.

8. MS Computer Science Application Data (Fall 2004) 37 Students Source: Office of Admissions, University of Bridgeport, 2008

9. Relative Efficiency Scores and Ranks of Each Candidate

10. DEA I - Technical Efficiencies, Min, Mean, Max. B.S. degree completion in identical number of semesters (6). Average technical efficiency = 77.4% High GPAs, GRE-Q scores, years of work experience, significantly low numbers of below-B grades in math-related/technical courses. Driven by the number of below-B grades.

11. Two DEA Models DEA Model II To rank the applicants according to: • t1 = number of below-C grades in the M.S. transcript of the M.S. candidate, • t2 = GPA of the M.S. candidate, • t3 = application status for the Curricular Practical Training (CPT) or Optional Practical Training (OPT).

12. MS Computer Science Application Data (Fall 2004) t1 = number of below-C grades in the M.S. transcript of the M.S. candidate, t2 = GPA of the M.S. candidate, t3 = application status for the Curricular Practical Training (CPT) or Optional Practical Training (OPT). 37 Students Source: Office of Admissions, University of Bridgeport, 2008

13. DEA II - Technical Efficiencies, Min, Mean, Max. High GPA & graduation. Average technical efficiency = 82.2% Driven by the lack of OPT or CPT applications and failure to graduate.

14. Comparing DEA I & II – Establishing a Pattern Proposed DEA application detects the efficient DMU more successfully compared to the ones that are below the average.

16. Conclusions DEA allows introduction of intangible and out-of-system indicators. • Can accommodate multiple inputs and multiple outputs. • Allows these inputs and outputs to be expressed in different units of measurement. Does not require an assumption of a functional form relating inputs to outputs. • TE is affected by the performance indicators. • Quality of data is important.

17. Future Research Additional criteria University ranking Problem statement Financial statement # publications/projects Quality of publications/projects and others Weight Automated model (DEA Solver Pro v.5.0) Database I/O Statistics collection Predict and compare the degree completion for those actually admitted

18. Are We Accepting the Right Students to Graduate Engineering Programs: Measuring the Success of Accepted Students via Data Envelopment Analysis Thank you ! Elif Kongar*, Mahesh Baral and Tarek Sobh *Departments of Technology Management and Mechanical Engineering University of Bridgeport, Bridgeport, CT, U.S.A We would like to acknowledge the following individuals that contributed their time and, more importantly, their innovative ideas to this project. Audrey Ashton-Savage, Vice President of Enrollment Management; Bryan Gross and Isabella Varga, Office of Admissions. 2008 ASEE Annual Conference & Exposition Pittsburgh, PA June 22-25, 2007

19. Regression Analysis RA: A statistical technique used to find relationships between variables for the purpose of predicting future values. x1 = 19.04651 – 0.02465x2

20. DEA “orientation” • Input-oriented DEA models define efficiency as “the least input for the same amount of output” • Output-oriented DEA models define it as “the most output for the same amount of input”. • Other considerations: • # of DMUs = App. 2 to 5 times of the sum of Input and Output variables • Input and output selection

21. Justification of Method Selection • Data envelopment analysis (DEA) is a widely applied linear programming-based technique. • Low divergence low complexity • Aim is to evaluate the efficiency of a set of decision-making units. • DEA has mostly been used for benchmarking and for performance evaluation purposes. • A DEA approach to measure the relative efficiency of end-of-life management for iron in different countries.

22. Advantages of DEA • Can accommodate multiple inputs and multiple outputs • Allows these inputs and outputs to be expressed in different units of measurement. • It doesn't require an assumption of a functional form relating inputs to outputs. • DMUs are directly compared against a peer or combination of peers. • Efficient units form the “efficient frontier” and inefficient units are enveloped by this frontier providing information on their improvement potential.

23. Data Envelopment Analysis Model where, k = 1 to s, j = 1 to m, i = 1 to n, yki= amount of output k produced by DMU i, xji = amount of input j produced by DMU i, vk = weight given to output k, uj = weight given to input j.

24. Dual Output-oriented CRS Model

25. Simplified schematic diagram of the application evaluation and decision making process

26. OCEAN

27. OCEAN – Admin Part