Development of the Academic Model to Predict Student Success at VUT-FSASEC Using Decision Trees

Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

Abstract:

The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: Academic environment model, decision trees, FSASEC, K-nearest neighbor, machine learning, popularity index, support vector machine.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132643

References:

[1] T. Daniel Larose, D. Chantal Larose, “Data Mining and Predictive Analytics” Second Edition, Wiley. 2015.
[2] R. Paul and L Cowe Falls. “Mapping Career Success Competencies to Engineering Leadership Capabilities, 2015 IEEE.
[3] M. Agaoglu. “Predicting Instructor Performance Using Data Mining Techniques in Higher Education” 2016 IEEE.
[4] J. Han,M. Kamber and J. Pei, “Data Mining” Third Edition, Morgan Kaufmann Publications. 2012.
[5] Ministry of Education. “A New Funding Framework: How Government grants are allocated to Higher Education Public Institutions” February 2004.
[6] A. Giri, M. V. V. Bhagavath, B. Pruthvi and N. Dubey. “A Placement Prediction system Using K-Nearest Neighbor Classifier” 2016 IEEE.
[7] B. Twala. “Robot Execution Failure Prediction Using Incomplete Data” Proceedings of the 2009 IEEE International Conference on Robotics and Biometrics, December 19-23, 2009, China.
[8] P. K. Imbrie and J. Lin. “Work in Progress Engineering Students Change in Profile over the Freshman Year across Male and Female Samples: A Neural Network Approach” 36th ASEE / IEEE Frontiers in Education Conference.
[9] S. Fong, Y. Si and R. P. Biuk-Aghai. “Applying a Hybrid Model of Neural Network and Decision Tree Classifier for Predicting University Admission” 2009 IEEE.
[10] B. Twala and T. Nkonyana. “Extracting Supervised Learning Classifiers from Possibly Incomplete Remorely Sensed Data” 2013 BRICS Congress on Computational Intelligence. 478 – 479 Brazil, 2013.
[11] W. Shang and H. Zhu. “The Improved ontology kNN Algorithm and its Application” 2006 IEEE.