عملکرد نسبی گروه های دانشگاهی با استفاده از تحلیل پوششی داده ها همراه با تجزیه و تحلیل حساسیت

The process of liberalization and globalization of Indian economy has brought new opportunities and challenges in all areas of human endeavor including education. Educational institutions have to adopt new strategies to make best use of the opportunities and counter the challenges. One of these challenges is how to assess the performance of academic programs based on multiple criteria. Keeping this in view, this paper attempts to evaluate the performance efficiencies of 19 academic departments of IIT Roorkee (India) through data envelopment analysis (DEA) technique. The technique has been used to assess the performance of academic institutions in a number of countries like USA, UK, Australia, etc. But we are using it first time in Indian context to the best of our knowledge. Applying DEA models, we calculate technical, pure technical and scale efficiencies and identify the reference sets for inefficient departments. Input and output projections are also suggested for inefficient departments to reach the frontier. Overall performance, research performance and teaching performance are assessed separately using sensitivity analysis.

India's development through education and research in the scientific and technical disciplines has achieved global reach and stature. In the list of the best technical institutes in India, the first name comes into insight is a group of institutions called Indian Institute of Technology. From Kharagpur in 1950 to Roorkee in 2001, there are now seven potentially world-class institutions in this family. These institutions have international recognition. The one member of this group is the Indian Institute of Technology Roorkee, which is the successor institute of the University of Roorkee, charted in 1949 and the Roorkee College founded in 1847, which was later renamed as the Thomason College of Civil Engineering. It is worth mentioning that the Roorkee College was the first engineering college established in the British Empire. This institute has the glorious past over 160 years and has been acclaimed for its excellence in education, research and training. The institute has three types of academic programs, namely, Undergraduate (UG) Programs (Bachelor of Technology degree in different disciplines), Postgraduate (PG) Programs (Master of Technology degree in different disciplines and Master in Business Administration degree) and Doctoral Programs (Ph.D.). Nineteen academic departments offer these programs. The purpose of this paper is to assess the relative performance of these departments based on the multiple criteria. Second objective is to measure how efficiently the departments work in the institute and to identify efficient and inefficient performers. Thirdly, we apply sensitivity analysis to test the robustness of results and assess the performance of departments for different activities like research and teaching. In the literature, several approaches are applied for measuring efficiency like performance indicators, parametric methods (such as ordinary least square method, stochastic frontier method) and non-parametric methods (such as DEA and Free Disposal Hull). Each method has its strengths and weaknesses. The ratio style performance indicators can work well only when a single input and single output are involved. But in multi-input and multi-output context, it is unable to draw right inferences. Parametric methods require explicit functional form for technology as well as distribution of inefficiency. But non-parametric methods do not require any functional form and work well with multiple inputs and outputs. The paper applies data envelopment analysis (DEA) methodology as it is the most suited methodology for measuring the performance of non-profit organization such as academic departments. It is particularly appropriate when the researcher is interested in investigating the efficiency of entities that convert multiple inputs into multiple outputs. Here such an entity is academic department. The paper is organized as follows: Section 2 comprises methodology and brief survey of literature of DEA in education sector. Section 3 describes the application procedure of DEA. Description of inputs and outputs and models used in the paper is also given. Section 4 gives information about data collection and computation. The overall performance of the departments is discussed in Section 5. Section 6 explains the teaching and research performance of the departments and robustness of the estimated efficiency scores using sensitivity analysis. Efficiency measurement of engineering departments estimated separately is also given in this section. In the last section, conclusions are given.

This paper has evaluated the performance of academic departments of IIT Roorkee through DEA models using different combinations of input and output variables. The principle objective is to analyze activity-wise performance assessment of the departments. This means that we want to evaluate which department is good for which specific activity (like teaching, placements and research). For this purpose, we did four assessments, namely, overall performance assessment, research performance assessment, teaching performance assessment and assessment for engineering departments by using 10 models. We have done sensitivity analysis in these 10 models by changing inputs and outputs. Among all models, the highest mean (0.9157) and the lowest standard deviation (0.1644) in technical efficiency are reported for Model-1. Therefore, overall performance is satisfactory for all the departments. The lowest mean (0.3684) and the highest standard deviation (0.3452) are calculated for Model-7 of teaching performance assessment. This confirms that improvements are needed in the field of placements. Models related to research assessment (Models 1–4) show that Model-2 has lower mean score (0.7311) in comparison to other models. Therefore, it is advised to focus on number of Ph.D. degrees awarded and enrolled to improve the performance of the inefficient departments. For overall performance assessments, four departments, namely, Chemistry, HSS, Management Studies and Mathematics are good example to follow by the inefficient departments to monitor and improve their performance. Among research performance assessment, the highest mean (0.8500) and the lowest standard deviation are calculated for Model-1. This indicates that the research performance is satisfactory when all activities related to research are combined into one output “Research Index”. Four departments, viz. Biotechnology, Chemistry, Civil Engineering and Hydrology are the good performers for the doctoral programs as well as all other activities like publications and research projects. The Mathematics department has scored 0.8667 that is above the average efficiency scores for Models (1–4). Therefore, Mathematics department, though below efficient frontier, performs consistently for all activities of research. ECE and Management Studies are the efficient departments in the field of placement as well as teaching activities while Mathematics is performing well only for enrolled students and students taught for other departments. ECE and Chemical Engineering are the best performers for both PG and UG programs among all engineering departments. The Civil engineering department is performing well only for PG programs. Thus our study provides information about every activity of the departments and policy makers can use suggested improvements and reductions to improve the performance in different areas. Finally, we can give some concluding remarks for the departments. • Overall performance assessment is good for all science departments. Other departments need improvements in their activities. • Only Biotechnology, Chemistry, Civil Engineering and Hydrology departments are efficient in every area of research. All other departments should pay attention for research works. • For every department, it is essential to focus on number of Ph.D. degrees awarded and enrolled students to improve their performance. • Only ECE and Management studies are doing well for placements. So all other departments should take care for placement activity. • The ECE and Chemical engineering departments are the best for both UG and PG programs among all engineering departments. Other engineering departments are advised to change their inputs and outputs to become efficient. • Some departments are not utilizing effectively their staff (both academic and non-academic) for some specific activities related to research and teaching.