Utilizando comitês de classificadores para predição de rendimento escolar

Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigated. Among the activities to be treated in this context exists the prediction of school performance of the students, which can be accomplished through the use of machine learning techniques. Such techniques may be used for student’s classification in predefined labels. One of the strategies to apply these techniques consists in their combination to design multi-classifier systems, which efficiency can be proven by results achieved in other studies conducted in several areas, such as medicine, commerce and biometrics. The data used in the experiments were obtained from the interactions between students in one of the most used virtual learning environments called Moodle. In this context, this paper presents the results of several experiments that include the use of specific multi-classifier systems systems, called ensembles, aiming to reach better results in school performance prediction that is, searching for highest accuracy percentage in the student’s classification. Therefore, this paper presents a significant exploration of educational data and it shows analyzes of relevant results about these experiments.

Uma proposta de indicadores para o acompanhamento de alunos em projetos de desenvolvimento colaborativo de software com foco no desenvolvimento de habilidades transversais

Soft skills and teamwork practices were identi ed as the main de ciencies of recent graduates in computer courses. This issue led to a realization of a qualitative research aimed at investigating the challenges faced by professors of those courses in conducting, monitoring and assessing collaborative software development projects. Di erent challenges were reported by teachers, including di culties in the assessment of students both in the collective and individual levels. In this context, a quantitative research was conducted with the aim to map soft skill of students to a set of indicators that can be extracted from software repositories using data mining techniques. These indicators are aimed at measuring soft skills, such as teamwork, leadership, problem solving and the pace of communication. Then, a peer assessment approach was applied in a collaborative software development course of the software engineering major at the Federal University of Rio Grande do Norte (UFRN). This research presents a correlation study between the students' soft skills scores and indicators based on mining software repositories. This study contributes: (i) in the presentation of professors' perception of the di culties and opportunities for improving management and monitoring practices in collaborative software development projects; (ii) in investigating relationships between soft skills and activities performed by students using software repositories; (iii) in encouraging the development of soft skills and the use of software repositories among software engineering students; (iv) in contributing to the state of the art of three important areas of software engineering, namely software engineering education, educational data mining and human aspects of software engineering.

Uma proposta de indicadores para o acompanhamento de alunos em projetos de desenvolvimento colaborativo de software com foco no desenvolvimento de habilidades transversais

Soft skills and teamwork practices were identi ed as the main de ciencies of recent graduates in computer courses. This issue led to a realization of a qualitative research aimed at investigating the challenges faced by professors of those courses in conducting, monitoring and assessing collaborative software development projects. Di erent challenges were reported by teachers, including di culties in the assessment of students both in the collective and individual levels. In this context, a quantitative research was conducted with the aim to map soft skill of students to a set of indicators that can be extracted from software repositories using data mining techniques. These indicators are aimed at measuring soft skills, such as teamwork, leadership, problem solving and the pace of communication. Then, a peer assessment approach was applied in a collaborative software development course of the software engineering major at the Federal University of Rio Grande do Norte (UFRN). This research presents a correlation study between the students' soft skills scores and indicators based on mining software repositories. This study contributes: (i) in the presentation of professors' perception of the di culties and opportunities for improving management and monitoring practices in collaborative software development projects; (ii) in investigating relationships between soft skills and activities performed by students using software repositories; (iii) in encouraging the development of soft skills and the use of software repositories among software engineering students; (iv) in contributing to the state of the art of three important areas of software engineering, namely software engineering education, educational data mining and human aspects of software engineering.