Extraction and analysis of faculty performance of management discipline from student feedback using clustering and association rule mining techniques

This paper deals with the extraction and analysis of faculty performance of management discipline from student feedback using clustering and association rule mining techniques. The performance of a faculty of any school or an Institute has been found to be dependent on a number of parameters broadly ranging from the individual´s qualifications, experience, level of commitment, research activities undertaken to institutional support, financial feasibility, top management´s support etc. The parameters which are crucial for assessment of faculty performance range across various verticals, but the paper discusses and covers the performance of faculties based strictly on students feedback only. The other assessors of faculty performance being the Management Body which could be a private body or a Government unit, self and peer faculties of the organization or the University. The parameters act as performance indicators for an individual and group and subsequently can impact on the decision making of the individual and also the stakeholders. The idea proposed in this paper is to perform extraction and analysis of faculty performance using techniques of Data Mining. The rationale behind using Data Mining is to cluster faculty performances on various criteria subject to the certain constraints and also extracting the dependencies amongst the parameters which will help finding meaningful associations between them. These associations in turn help to identify new patterns for decision making. The paper restricts to students feedback from Computer Applications Department across accredited institutes. The analysis depends on many attributes and the paper justifies the usage of mining methodologies rather than following the conventional approach.