APPLICATION OF CLUSTER ANALYSIS AS A TOOL TO ANALYSE DISTANCE EDUCATION STUDENTS

The educational databases often have hidden knowledge about the students, their academic behavior, their study skills and their performance in an academic program. This explicit knowledge (Koulopoulos and Frappaolo, 1999) can be used to facilitate learning more effectively and efficiently by the educational institutions. There are many studies that tried to analyse students’ characteristics and then draw conclusions about the students. These studies were mainly based on either nominal and interval data and the characteristic were judged by the percentage of students possessing these characteristics. Almost negligible number of studies tried to analyse students globally with respect to all their characteristics. Question thus arises that can students be classified on the basis of knowledge delivered by the student database. One of the data mining techniques of structuring data is “cluster analysis”. Clustering literally means, “to gather” or “draw together”. In terms of data, clustering means dividing the data in such a way that similar data points comes together. The study has collected data on various student characteristics and attempted to apply cluster analysis technique on the science graduate students of Indira Gandhi National Open University, India from Delhi region. The dataset has taken a sample of 75 students from the data repository of 1,307 students of which 693 students were active students (Khare, et.al., 2003). The study tried to obtain various clusters of students that are “heterogeneous among” them but “homogenous within” themselves. From the results obtained study has tried to identify the reasons that though students are different in their performance in the TEE, there is some similarity between them, which binds them together in a particular cluster. The study has also tried to build up a measure to indicate the dissimilarity among the students. Introduction Literal meaning of clustering is to gather, to congregate or draw together. In terms of data management, clustering means dividing the data in such a way that similar data points comes together. The objective of clustering is form groups that are heterogeneous but homogeneous within. Clustering is thus a method to divide a database into clusters that can be used for classification purpose. However, classification segments the data into groups that are already defined. Clustering facilitates segmentation of the data into groups that are not previously defined. The clientele of distance education is normally heterogeneous in nature (Chisthty, 1992, Giglotti, 1995). They come from diverse environments, different socio-economic streams, and varied age groups and sometimes without even having formal education (Pathni, 1985). The learning styles, learning environment and learning culture can also be a function of the heterogeneity and may affect the performance of students in an academic programme. The academic institutions should address the issues that mushroom-up from the heterogeneity and should try to build bridges to create ‘balancing parameters’ among identified group(s) of students. For the purpose, educational institutions are to be equipped with a tool to find out the dissimilarities among the learners. Then a question comes, is there a method of defining the “dissimilarity“ between learners? A student-centric attitude forces to have a re-look into the “learners” from learning environment, learning culture and learning style point of view. The basic premises of question-set that may arise are: 1 Reader, School of Management Studies, Indira Gandhi National Open University, New Delhi. 2 Programme Officer, Commonwealth Educational media Centre for Asia, New Delhi. 3 Pro-Vice-chancellor & Professor of Physics, Indira Gandhi National Open University, New Delhi.