Learning analytics to assess students’ behavior with scratch through clickstream

The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. This suposes a huge problem of time and no adecuate intime feedback to students while practicing programming activities. The field of learning analytics has been a common practice in research since last years due their great possibilities in terms of learning improvement. Such possibilities can be a strong positive contribution in the field of computational practice such as programming. In this work we attempt to use learning analytics to ensure intime and quality feedback through the analysis of students behavior in programming practice. Hence, in order to help teachers in their assessments we propose a solution to categorize and understand students’ behavior in programming activities using business technics such as web clickstream. Clickstream is a technique that consists in the collection and analysis of data generated by users. We applied it in learning programming environments to study students behavior to enhance students learning and programming skills. The results of the work supports this business technique as useful and adequate in programming practice. The main finding showns a first taxonomy of programming behaviors that can easily be used in a classroom. This will help teachers to understand how students behave in their practice and consequently enhance assessment and students’ following-up to avoid examination failures.Peer ReviewedPostprint (published version

Uso de Learning Analytics sobre dados de simulados para apoio à avaliação da aprendizagem por professores e gestores

A avaliação da aprendizagem a partir de ambientes virtuais de aprendizagem (AVA) traz novas oportunidades, porém produz um grande volume de dados, o que dificulta uma análise manual dos resultados dos alunos. Por esse motivo, este trabalho busca fornecer informações relevantes para professores e gestores a partir da utilização de técnicas de Learning Analytics (LA) sobre dados de simulados aplicados em um AVA. Seguindo o processo de LA e suas técnicas, foi possível visualizar em quais turmas os alunos apresentaram menor compreensão em relação à disciplina, agrupar as disciplinas conforme os valores das métricas de avaliação da aprendizagem utilizadas, assim como verificar que o tempo de resposta das questões, estipulado pelo professor, pode ter sido insuficiente

A learning analytics tool with hybrid graphical and textual interpretation generation

The introduction and use of on-line learning resources has improved students learning process in several aspects. But at the same time, it also introduced more complexity and made more difficult to teachers how to analyze learning evolution and improvement of students. In this paper, we propose a first approach how to visualize and analyze student interaction with on-line learning systems and Virtual Learning Environments (VLE). We present a piece of software that collects information on the interaction of the students with the Moodle VLE and that it displays to teachers in a more analytical way. The interaction data is displayed to support teacher interpretation from a learning point of view, with the inclusion of automatically generated textual explanations about the analysis of such data.Peer Reviewe

A learning analytics tool with hybrid graphical and textual interpretation generation

The introduction and use of on-line learning resources has improved students learning process in several aspects. But at the same time, it also introduced more complexity and made more difficult to teachers how to analyze learning evolution and improvement of students. In this paper, we propose a first approach how to visualize and analyze student interaction with on-line learning systems and Virtual Learning Environments (VLE). We present a piece of software that collects information on the interaction of the students with the Moodle VLE and that it displays to teachers in a more analytical way. The interaction data is displayed to support teacher interpretation from a learning point of view, with the inclusion of automatically generated textual explanations about the analysis of such data.Peer Reviewe