One way to improve the education of our students is to promote the creation of mass collaborative projects. These projects, also seen as learning products, would also help to better scale our learning experiences (massive open online courses) and generate collective value from the hours and cognitive efforts invested doing academic work. However, the complexities to assess those projects are challenging. This requires developing new monitoring and feedback systems for this kind of projects. This paper presents an exploratory analysis for applying learning analytics methodologies based on social networks analysis, factorial analysis, k-means clustering, and “naïve” Bayes algorithms.
© 2001-2024 Fundación Dialnet · Todos los derechos reservados