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Nowadays, people find it easier to express opinions via social media-formally known as Web 2.0. Sentiment analysis is an essential field under natural language processing in Computer Science that deals with analyzing people's opinions on the subject matter and discovering the polarity they contain. These opinions could be processed in collective form (as a document) or segments or units as sentences or phrases. Sentiment analysis can be applied in education, research optimization, politics, business, education, health, science and so on, thus forming massive data that requires efficient tools and techniques for analysis. Furthermore, the standard tools currently used for data collection, such as online surveys, interviews, and student evaluation of teachers, limit respondents in expressing opinions to the researcher's surveys and could not generate huge data as Web 2.0 becomes bigger. Sentiment analysis techniques are classified into three (3): Machine learning algorithms, lexicon and hybrid. This study explores sentiment analysis of Web 2.0 for novice researchers to promote collaboration and suggest the best tools for sentiment data analysis and result efficiency. Studies show that machine learning approaches result in large data sets on document-level sentiment classification. In some studies, hybrid techniques that combine machine learning and lexicon-based performance are better than lexicon. Python and R programming are commonly used tools for sentiment analysis implementation, but SentimentAnalyzer and SentiWordnet are recommended for the novice. Keywords:   Sentiment Analysis; Web 2.0; Applications; Tools; Novice

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.   

Unified modelling language (UML) is the accepted standard and modelling language for modeling in software development process. UML is widely used by most course tutors in teaching modules of software engineering and system analysis and design. Students taking such courses do submit assignments with UML diagrams such as use case, class, sequence, activity and so on. Different versions of such diagrams produced by the students for a given problem have to be assessed by the course tutor which is a challenging and time-consuming task. This paper presents a java-based tool which is developed based on a simple yet effective algorithm developed by the authors that will read student and tutors solution diagrams as inputs and evaluate and grade the diagrams automatically. The output of the tool is the score of the student diagram in respect of lecturer’s final solution. The output is presented in two feedback files, one containing students’ score for the lecturers and the other to be send to the student to note the areas that were incorrect. The tool has been tested and evaluated using a simple and assumed UML class diagram. The result shows that the tool functions effectively and can produce detail feedbacks for both students and tutors. The outcome of this paper contributes towards automating UML diagram evaluations.

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.