S Anupama Kumar

Work place: Department of MCA, R V College of Engineering, Bangalore, India

E-mail: kumaranu.0506@gmail.com


Research Interests: Parallel Computing, Data Mining


S Anupama Kumar, presently working as an Associate Professor, completed her Doctoral in Educational Data Mining and is currently guiding two Ph D students under Visvesvaraya Technological University, India. She has 15 years of teaching experience and has 18 publications to her credit. Her research interest includes Data Mining and Parallel Computing.

Author Articles
Edifice an Educational Framework using Educational Data Mining and Visual Analytics

By S Anupama Kumar

DOI: https://doi.org/10.5815/ijeme.2016.02.03, Pub. Date: 8 Mar. 2016

Educational Data Mining and Visual analytics are two emerging trends in the industry that plays a major role in bringing out changes in the educational institutions. This paper discusses about building an educational framework that suits the higher education in India using the above mentioned technologies. Educational data mining comprises of various technologies and tasks which can applied on educational data to bring out useful information. In this research work, a data ware house is built to store the student data, two data mining tasks classification and association rule mining are applied over the student data set to analyse their performance in the examination. Decision tree algorithm is used to predict the course and program outcome. Association mining is used to analyze the outcome and understand technical capability of the students. The algorithms were found very accurate in predicting and analyzing the performance. Visual analytics is used in the framework to depict the analysis of the student's performance.

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A Naïve Based approach of Model Pruned trees on Learner’s Response

By S Anupama Kumar Vijayalakshmi M.N.

DOI: https://doi.org/10.5815/ijmecs.2012.09.07, Pub. Date: 8 Sep. 2012

Appraisal and feedback have a strong positive influence on teachers and their work. Teachers report that it increases their job satisfaction and, to some degree, their job security, and it significantly increases their development as teachers. Student’s appraisal towards a teacher plays a vital role in building a very good teaching-learning environment in an educational institution. The evaluation report of the student helps the stakeholders to retain qualified teachers for the course. It will also help the teacher to understand the need of the student and the course. Therefore it becomes necessary to evaluate the teacher using appropriate tool to improve the quality of the education. Teacher evaluation can be measured based on the technical knowledge, communication skills, clarity, attitude towards the student etc. Regression trees can be considered as a tool to analyze the teacher appraisal scores. Two regression trees namely the REP tree and M5P algorithms are applied on the data set to bring out new knowledge from it. The algorithms have identified Parameter A as an important factor in teacher’s appraisal. Pruning has been taken as parameter to find the accuracy of the algorithm. The performance of the algorithm is measured using the mean absolute error and the time taken by the algorithms to derive the regression tree. The REP tree algorithm performs better than the M5P algorithm in terms of accuracy as well as the performance.

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