Project allocation is an annual challenge for lecturers and students. The process of allocating project involves matching preferences of students over project and with of staff over the student, and is thus an instance of stable marriage problem from theoretical computer science aspect. The aim is to find a stable allocation of project to students, such that it is impossible to find a project swap that would make all involved parties (both students, both staff) happier. This paper investigated efficacy of stable marriage algorithm and deployed basic Gale Sharply Algorithm into the process of allocating student project. A system was developed using ruby and MySQL to handle the task. The result showed that the algorithm was able to improve the process by enhancing the stability involved.   

Open-source software has been widely developed and adopted for different purposes, including in the educational sector. Many institutions of learning extensively utilize open-source e-learning software to complement their in-class programs, especially during the COVID-19 pandemic. But many institutions of learning in Nigeria are still unable to utilize the opportunity due to a lack of adequate awareness and guidance on how to adopt the software. This study is aimed at educating the Nigerian public on open-source e-learning software and its benefits in education, especially in tertiary institutions amid the COVID-19 pandemic. To better have a wide coverage of respondents in Nigerian tertiary institutions, we conducted an online survey and a paper-based questionnaire. A sample of 500 responses was collected from Nigerians with different high educational levels regarding Open-Source e-learning adoption and awareness. However, only 349 responses were returned. The results of the survey were analyzed using python-based analysis libraries. The findings indicated that a greater percentage of the respondents were aware of what constitutes open-source e-learning but that most institutions in Nigeria have not fully utilized the platform for learning activities. It is implied that a reasonable number of respondents have literacy knowledge of open-source learning and agree that COVID-19 has greatly influenced the use of e-learning software in Nigerian institutions. It is therefore recommended that more work be done to improve the awareness level of various institutions to fully utilize the technology and the benefits it represents. This led to the development of a web-based solution for creating more awareness among the public. This would help close the gap found in this study and affect how open-source e-learning software is used in Nigerian schools.

Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.