Predicting and analyzing secondary education placement-test scores: A data mining approach

Authors:
Baha Şen;Emine Uçar;Dursun Delen
Affiliations:
Yıldırım Beyazıt University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Ulus, 06030 Ankara, Turkey;Karabük University, Faculty of Engineering, Department of Computer Engineering, Baliklarkayasi, 78050 Karabük, Turkey;Spears School of Business, Department of Management Science and Information Systems, Oklahoma State University, Stillwater, OK, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 7
Cited 2

Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

Neural Networks
Support Vector Machines for 3D Object Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Induction of Decision Trees

Machine Learning
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Advanced Data Mining Techniques

Advanced Data Mining Techniques
Decision Support and Business Intelligence Systems

Decision Support and Business Intelligence Systems

Review: Educational data mining: A survey and a data mining-based analysis of recent works

Expert Systems with Applications: An International Journal
Smart meter monitoring and data mining techniques for predicting refrigeration system performance

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Understanding the factors that lead to success (or failure) of students at placement tests is an interesting and challenging problem. Since the centralized placement tests and future academic achievements are considered to be related concepts, analysis of the success factors behind placement tests may help understand and potentially improve academic achievement. In this study using a large and feature rich dataset from Secondary Education Transition System in Turkey we developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models we identified the most important predictors. The results showed that C5 decision tree algorithm is the best predictor with 95% accuracy on hold-out sample, followed by support vector machines (with an accuracy of 91%) and artificial neural networks (with an accuracy of 89%). Logistic regression models came out to be the least accurate of the four with and overall accuracy of 82%. The sensitivity analysis revealed that previous test experience, whether a student has a scholarship, student's number of siblings, previous years' grade point average are among the most important predictors of the placement test scores.