TRAINEE PERFORMANCE PREDICTION MODEL FOR HAWASSA POLYTECHNIC COLLEGE USING KDP

Abstract

Data mining is the key tool for discovery of knowledge from large data set. It is this technology in most of the educational organization of the world currently helping to know the organization data explicitly and pave the way to produce quality citizens. Unlike other sectors the power of data mining is not much exploited in educational sector. Although there are studies regarding academic performance of students using data mining techniques, they are all about university students. we cant find academic performance of trainee research in Technical and vocational Institute. Thus, the purpose of this study is to develop Trainee performance prediction model for Hawassa polytechnic college. A total of 8200 records with 13 attributes were collected from Hawassa Polytechnic College registrar data set of the past 5 years ranging from 2009 to 2013 E.C. An experiment has been conducted using the Knowledge Discovery Process (KDP) Model using WEKA software version 3.8.4. Four data mining algorithms namely J48 Decision Trees, JRip rules induction, Naïve Bayes and PART with seven experiments (J48 Pruned and Un-pruned decision tree algorithm, Naive Bayes classifier, JRIP Pruned and Un-pruned and PART Pruned and Un pruned) were used to develop trainees performance predictive model. All the experiments were carried out with the same dataset and evaluated with 10-fold cross validation, 80% and 66% split test parameters. The study shows PART Un-pruned 10-fold cross validation test has the highest accuracy with 95.4268% and attributes such as trade/occupation, EGSECE, transcript, level, sex, English, and sector can be used at a time of decision making as they have shown strong prediction power which can help to predict trainees performance. Finally the researcher develop a prototype based on the rules generated from the selected algorithm.