DIGITAL LIBRARY
EDUCATIONAL PROCESS EFFICIENCY ANALYSIS BY DATA ENVELOPMENT ANALYSIS METHOD
1 Academy of Economic Studies (ROMANIA)
2 University Al. I. Cuza from Iasi, (ROMANIA)
About this paper:
Appears in: EDULEARN09 Proceedings
Publication year: 2009
Pages: 4751-4762
ISBN: 978-84-612-9801-3
ISSN: 2340-1117
Conference name: 1st International Conference on Education and New Learning Technologies
Dates: 6-8 July, 2009
Location: Barcelona ,Spain
Abstract:
In the last few decades were developed specific methods to analyze the economic efficiency, especially at microeconomic level. These methods can be successfully used to analyze other fields of activity; such are macroeconomics, sociology or education. Farrel hypothesis is that efficiency consists in two components: technical efficiency and allocative efficiency. Technical efficiency represent the ability to obtain maximum level of output given that a set of inputs. Allocative efficiency represents the ability to optimally use the inputs for given technologies and prices. The combinations of these two components are total economic efficiency. In our paper we apply this method of analysis to develop a method to classify scholar units from efficiency point of view and to obtain a hierarchy of Bucharest high schools.
We define the performance of school units as ability of school management to use the financial, human and material resources to obtain a better education of students. This “better education” is not easy to define. For this we must establish a set of indicators to measure the education level obtains in school years. As outputs we can use different indicators like: Ratio of graduated students in total for each school unit; Overall mean Ratio of graduated students that continue in university; Number of students in national contests; Number of national grants obtained by unit school students; Number of prizes obtained in national and international competitions; Number of published papers and books, etc. As inputs we can use: Total staff members; Total professors numbers; High school size (surface, number of rooms and laboratories); Total costs of maintenance; Equipments quality (qualitative variable); Total number of students; Mean number of students in each classroom; High quality professors ratio in total staff; Mean age of professors; High school reputation (defined by intermediary of minimum level of admission mark); Investments; Sponsors, etc. Other variables (environmental) are: High school location; Number of high school in nationhood; Economical level of students (defined by numbers of social grants); Existence of special programs.
In our study we use only a part of these variables due on available data from Romanian Ministry of Education.
So, like output variable we consider:
• Ratio of graduated students/ total students for each school unit;
• Overall mean of graduation marks;
• Number of national grants obtained by unit school students;
• Number of prizes obtained in national and international competitions;
Our inputs are:
• Total professors numbers;
• High school reputation (defined by intermediary of minimum level of admission mark);
• Equipments quality (qualitative variable);
• Economical level of students (defined by numbers of social grants);
• Total number of students;
• Mean number of students in each classroom;
Using DEA methodology we can obtain a hierarchy of school units, a number of efficient school units and the inefficient ones, but also the distance between efficient and inefficient units. Some results of our analysis indicate: A classification of high school from efficiency point of view; Inefficiency depends especially on managerial staff abilities; Variables with a week influence on output levels are: high school reputation and economical level of students; A “frontier of efficiency” for Bucharest high schools and also the distance between inefficient high schools and efficient ones.
Keywords:
efficiency, education, inputs, outputs, school classification.