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Examinations are means of assessing the knowledge or skills that students have acquired, after having been taught over a period of time. Anomalies in student results are noteworthy observations that require additional clarifications. Manual detection of anomalies in results leads to human errors and wastage of manpower. This paper describes how extreme learning machines can be used to automatically detect anomalies in student results. The results show that using extreme learning machines almost always produces better or equal results compared to decision trees.