Analysis of Dropouts in Primary Education

We use Fuzzy Cognitive Maps to study the dropouts in primary education. We construct a teacher-student-model in primary education using some basic nature of teachers-like devoted teacher, trained teacher, untrained teacher, friendly approach of the teacher towards the student etc. In primary education, teachers play a major role, in not only creating interest in small children but also vitally affect the dropouts in primary education. By using the Fuzzy Cognitive Maps we obtain the hidden pattern of the cause for the dropouts in primary education. The hidden pattern cannot be found out using any other method.

Secondly we also construct a parent-children model to study dropouts in primary education using Fuzzy Cognitive Map. We have constructed this model using the following concepts: namely, the educational status of the parents, economical status of the parents, fee structure and inability of parents to teach their children in a language alien to them and analyze how they affect dropouts in primary education. We present the opinion of two experts. The main aim of the study is to find the cause of dropouts in primary classes, which seems to be a major problem, as it largely affects the literary percentage of our nation. We analyse the cause of dropouts and on the parents role in denying a child even a primary education. To achieve this we construct a FRM model; as the data under analysis is an unsupervised one we felt only FRM can give us the hidden pattern of the situation. The main causes for the dropouts in primary schools are:

1. Poor economic condition of the parents.
2. No school in the neighbourhood with less fees.
3. Language problems
4. Uneducated parents who are unaware of the value of education.
5. No proper counselling given to the poor and uneducated parents by the teachers or social workers about the need of the basic primary education and the unknown harm they are doing to their children by denying them education.

We suggest that education should be made free at least for the very poor in the schools in their locality.

The motivation for the application of fuzzy set theory to the design of databases and information storage lies in the need to handle imprecise information. The database that can accommodate imprecise information can store and manipulate not only precise facts, but also subjective expert opinions, judgements and values that can be specified in linguistic terms. This type of information which is quite useful is for the first time applied by us to study the child labour problem, as this problem is one of the vital problems of the nation as one is not able to precisely say why children are used to do work when they should be given basic education and should be free to enjoy life. The various reasons spoken for child labour are very vague like poverty, lack of motivation in parents, absence of school, hereditary job, uneducated parents, drop outs in schools and so on and so forth, but one is not able to precisely pinpoint the cause of child labour.

This fuzzy database model and its associated fuzzy relation algebra obtained by us contain opinions of a group of experts about child labour. We use Computer coding (SQL statements) to program the tables using fuzzy relational equations. As our approach to study the child labour problem is using fuzzy database and since our results are derived not only using the statistical data but also equal weightage is given to the ‘feelings’ as to why child labour is practiced we think that our conclusions would be better than the ones found out by analyzing only the statistical data.