Attendance management in the classroom is important because in many educational institutions, sufficient number of class attendance is a requirement for earning a regular grade in a course. Automatic signature verification is an active research area from both scientific and commercial points of view as signatures are the most legally and socially acceptable means of identification and authorization of an individual. Different approaches have been developed to achieve accurate verification of signatures. This paper proposes a novel automatic lecture attendance verification system based on unsupervised learning. Here, lecture attendance verification is addressed as an offline signature verification problem since signatures are recorded offline on lecture attendance sheets. The system involved three major phases: preprocessing, feature extraction and verification phases. In the feature extraction phase, a novel set of features based on distribution of black pixels along columns of signatures images is also proposed. A mean square error of 0.96 was achieved when the system was used to predict the number of times students attended lectures for a given course.

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 from theirs or whom they just do not enjoy working with. In this paper we present a genetic algorithm (GA) for assigning project supervisors to students taking into account the students’ preferences for lecturers as well as lecturers’ capacities. Our work differs from several existing ones which tackle the student project allocation (SPA) problem. SPA is concerned with assigning research projects to students (and sometimes lecturers), while our work focuses on assigning supervisors to students. The advantage of the latter over the former is that it does not require projects to be available at the time of assignment, thus allowing the students to discuss their own project 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 multiple good allocations, which can be discussed in order to adopt a final allocation.

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.