Sam Nii Tackie

Work place: Near East University/Electrical & Electronic Department, Lefkosa, via Mersin-10, North Cyprus



Research Interests:


Samuel Nii Tackie is currently pursuing a masters degree program in Electrical/Electronic Engineering at Near East University, Lefkosa, via Mersin-10, Turkey. Research interests include power electronics, artificial neural networks, pattern recognition, machine learning, image processing, fuzzy systems and robotics.

PH-+905428892591. E-mail:

Author Articles
Data Mining of Students’ Performance: Turkish Students as a Case Study

By Oyebade Kayode Oyedotun Sam Nii Tackie Ebenezer Obaloluwa Olaniyi Khashman Adnan

DOI:, Pub. Date: 8 Aug. 2015

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task. The performances obtained from these networks were evaluated in consideration of achieved recognition rates and training time.

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