Students’ Placement Prediction Model: A Data Mining Approach
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Date
2017-06-09
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Addis Ababa University
Abstract
The main objective of Higher Education institutions is to provide quality education to students. To achieve highest level of quality of education institutions may apply discovery of knowledge for prediction placement of the students’, consider resource capability and performance of the students. Thus researcher initiated to undertake study on students’ placement into different available departments using data mining technique and to propose a predictive model.
The study attempted to build a predictive model for student placement prediction and identify interesting rules from the generated model by applying data mining techniques. The study been carried out using hybrid data mining methodology processing. The targeted data set used for the study was the students’ placement and high school score of students’, who joined Addis Ababa University during 2015/16 and 2016/17 Academics years. The data is acquired from AAU registrar office and NEAEA. The original dataset consist 34 attributes and very 11320 instances. Thus, to make the initial data appropriate and manageable for the data mining exercise, data preparation task was undertaken. Decision tree and rule induction classification techniques using J48, REPTree and PART algorithms were applied for the experimentation.
The experiments were conducted using six scenarios for each algorithm and the outputs of the experiments were used for comparison of the models based on the set evaluation criteria. After the test design was defined and the dataset separated into training and test dataset, the model was built the training set and its quality was estimated on the separate test set. As a result, the test run using PERTree algorithm has registered best accuracy which is 82.045%. The generated rules were interpreted, analyzed and the discovered knowledge was evaluated against the existing knowledge base and domain expert’s validation. Based on the findings of this research work, we can conclude that improved students’ placement in to various departments can be done using data driven predictive model with acceptable.
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Data Mining