Utilization of a Self Organizing Map as a Tool to Study and Predict the Success of Engineering Students at Walailak University



Many factors have an influence on the success of undergraduate students particularly in engineering programs. Some students have to drop out as a result of obtaining very poor GPA (grade point average) and/or GPAX (accumulated grade point average) after only their first year of studying. It would be helpful for students if they know how their current GPA/GPAX could be improved in order to successfully graduate. In addition, what would be the expected outcome of their study, if their current GPAs of compulsory subjects are not fairly good? In this paper, the Self Organizing Map (SOM) neural network is utilized as a tool to cluster engineering student data into different groups by means of their study results. The results are then used to produce the weight maps. The maps reflect the correlation between GPA/GPAX of the compulsory subjects and the educational status of students. The result from the SOM with some adaptations to its matching phase is also used to create a predictor which is capable of producing a fairly high degree of correctness. The meaningful results are intended to be used as a guideline for students to prepare and improve themselves. In addition, it might be useful for student advisors and counselors to give appropriate advice to students whose GPAX are critically low. This can be accomplished by advising students to register less or withdraw some subjects in order to leverage their GPAX. In addition, some students should be advised to change their field of study if they perform fairly poorly in all compulsory subjects. The approach utilized in this paper is a novel one with respect to this application domain.


Self organizing map, engineering education, education prediction

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