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Developing Knowledge-Based Systems Using Data Mining Techniques for Advising Secondary School Students in Field of Interest Selection

2018; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-91455-8_16

1611-3349

Sofonias Yitagesu, Zhiyong Feng, Million Meshesha, Getachew Mekuria, Muhammad Qasim Yasin,

Imbalanced Data Classification Techniques

Ethiopia gives a highly emphasis on a secondary school and reform program of impressive expansion. In doing this, students interest towards the fields they are assigned to needs to be taken into consideration. This has been put into practice when the Ministry of Education and preparatory schools have assigned students in fields of studies based on their performance at secondary schools. However, they used only the students grade 10 Ethiopian General School Leaving Certificate Examination result to assign them. The objective of this study is to develop a knowledge-based systems using machine learning (data mining) techniques that consults the students in their field of study selection process. In this study, the hybrid model that was developed for academic research is used. To build the predictive model, 9364 sample students data from selected secondary schools are used. The sample data is preprocessed for missing values, outliers, noisy and errors. Then the model is experimented using decision tree (j48) and rule induction (PART) algorithms. In this study as compared to j48, the PART unpruned decision list algorithm has 98.003% predictive performance. Thus, the knowledge discovered with this algorithm is further used to build the knowledge-based systems. Hence, the Java program is used to integrate data mining results to knowledge-based systems. As a result, the developed knowledge based-systems is used to predict students field of study based on their performance at secondary school. The study concludes that, to build the accurate knowledge-based systems discovering knowledge using data mining techniques is significant.