How to keep track of the learning process of a community of learners is a problem whose resolution requires accurate assessment tools and appropriate teaching and learning strategies. Peer Assessment is a standard didactic strategy which requires students in a course to correct their peers’ assignments. Since the representation of a community, even a large one, of students, is based on directed graphs, it is difficult to follow its whole dynamics. In this paper, we investigate the possibility of using two machine learning techniques: Graph Embeddings, and Principal Component Analysis, to represent a students’ communities by points in a 2D space, in order to have valuable and understandable information on the dynamics of the group. For this purpose we present a case study based on three real Peer Assessment sessions. The first results are encouraging.
Dettaglio pubblicazione
2021, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pages 350-356 (volume: 12677)
Using Graph Embedding to Monitor Communities of Learners (04b Atto di convegno in volume)
Gasparetti F., Sciarrone F., Temperini M.
ISBN: 978-3-030-80420-6; 978-3-030-80421-3
Gruppo di ricerca: Human-Computer Interaction