Abstract-Research projects are graduation requirements for many university students. If students are arbitrarily assigned project supervisors without factoring in the students' preferences, they may be allocated supervisors whose research interests differ fro m theirs or who m they just do not enjoy working with. In this paper we present a genetic algorith m (GA ) for assigning project supervisors to students taking into account the students' preferences for lecturers as well as lecturers' capacities. Ou r work differs fro m several existing ones which tackle the student project allocation (SPA) problem. SPA is concerned with assigning research projects to students (and sometimes lecturers), wh ile our wo rk focuses on assigning supervisors to students. The advantage of the latter over the former is that it does not require pro jects to be available at the time of assignment, thus allowing the students to discuss their own pro ject ideas/topics with supervisors after the allocation. Experimental results show that our approach outperforms GAs that utilize standard selection and crossover operations. Our GA also compares favorably to an optimal integer programming approach and has the added advantage of producing mu ltip le good allocations, which can be d iscussed in order to adopt a final allocation.

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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 manhours 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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