A Classification-Based Graduates Employability Model for Tracer Study by MOHE

This study is to construct the Graduates Employability Model using data mining approach, in specific the classification task. To achieve it, we use data sourced from the Tracer Study, a web-based survey system from the Ministry of Higher Education, Malaysia (MOHE) since 2009. The classification experiment is performed using various Bayes algorithms to determine whether a graduate has been employed, remains unemployed or in an undetermined situation. The performance of Bayes algorithms are also compared against a number of tree-based algorithms. In conjunction with tree-based algorithm, Information Gain is used to rank the attributes and the results showed that top three attributes that have direct impact on employability are the job sector, job status and reason for not working. Results showed that J48, a variant of decision-tree algorithm performed with highest accuracy, which is 92.3% as compared to the average of 90.8% from other Bayes algorithms. This leads to the conclusion that a tree-based classifier is more suitable for the tracer data due to the information gain strategy.

[1]  Abdul Razak Hamdan,et al.  Employability and service science: Facing the challenges via curriculum design and restructuring , 2009, 2009 International Conference on Electrical Engineering and Informatics.

[2]  Hüseyin Gürüler,et al.  A new student performance analysing system using knowledge discovery in higher educational databases , 2010, Comput. Educ..

[3]  Lilly Suriani Affendey,et al.  Ranking of influencing factors in predicting students' academic performance. , 2010 .

[4]  Latisha Asmaak Shafie,et al.  Employability Awareness among Malaysian Undergraduates , 2010 .

[5]  E.N. Ogor.  Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques , 2007, Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007).

[6]  Norshahriah Wahab,et al.  Predicting NDUM Student's Academic Performance Using Data Mining Techniques , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[7]  Deependra Sharma,et al.  Store Choice and Store Loyalty: An Investigation on Shopper's Behaviour Towards Organized versus Unorganized Retailer Stores , 2012 .

[8]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[9]  E. Jaynes.  Probability theory : the logic of science , 2003 .

[10]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[11]  Azami Zaharim,et al.  Practical framework of employability skills for engineering graduate in Malaysia , 2010, IEEE EDUCON 2010 Conference.

[12]  W. F. Punch,et al.  Predicting student performance: an application of data mining methods with an educational Web-based system , 2003, 33rd Annual Frontiers in Education, 2003. FIE 2003..