Detecting Anomalies in Students‟ Results Using Decision Trees

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Hamza O. Salami 1,* Ruqayyah S. Ibrahim 1 Mohammed O. Yahaya 2

1. Department of Computer Science, Federal University of Technology, PMB 65, Minna, Nigeria

2. Computer Science and Engineering Department, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia

* Corresponding author.


Received: 22 Mar. 2016 / Revised: 12 May 2016 / Accepted: 10 Jun. 2016 / Published: 8 Jul. 2016

Index Terms

Decision trees, examination results, anomaly detection, educational data mining, result anomaly


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.

Cite This Paper

Hamza O. Salami, Ruqayyah S. Ibrahim, Mohammed O. Yahaya, "Detecting Anomalies in Students' Results Using Decision Trees", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.7, pp.31-40, 2016. DOI:10.5815/ijmecs.2016.07.04


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