Ruqayyah S. Ibrahim

Work place: Department of Computer Science, Federal University of Technology, PMB 65, Minna, Nigeria



Research Interests: Computational Learning Theory, Data Mining, Data Structures and Algorithms


Ruqayyah S. Ibrahim obtained a BSc in Computer Science from the Ahmadu Bello University, Zaria – Nigeria. She is currently undergoing her Master‟s degree program in computer science with the Federal University of Technology, Minna – Nigeria.
Her research interests include machine learning application, data mining and knowledge discovery.

Author Articles
Detecting Anomalies in Students‟ Results Using Decision Trees

By Hamza O. Salami Ruqayyah S. Ibrahim Mohammed O. Yahaya

DOI:, Pub. Date: 8 Jul. 2016

Examinations are one of the most important activities that take place in institutions of learning. In many Nigerian universities, series of meetings are held to manually examine and approve computed student examination results. During such meetings, students‟ results are scrutinized. Reasonable explanations must be provided for any anomaly that is discovered in a result before the result is approved. This result approval process is prone to some challenges such as fatigue arising from the long duration of the meetings and wastage of man-hours that could have been used for other productive tasks. The aim of this work is to build decision tree models for automatically detecting anomalies in students‟ examination results. The Waikato Environment for Knowledge Analysis (WEKA) data mining workbench was used to build decision tree models, which generated interesting rules for each anomaly. Results of the study yielded high performances when evaluated using accuracy, sensitivity and specificity. Moreover, a Windows-based anomaly detection tool was built which incorporated the decision tree rules.

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