Communication and Resource Usage Analysis in Online Environments An Integrated Social Network Analysis and Data Mining Perspective

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Figueira, A (Autor) FCUP

Ata de Conferência Internacional

Título: 2017 IEEE Global Engineering Education Conference, EDUCON 2017, Athens, Greece, April 25-28, 2017

Páginas: 1027-1032

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00M-V9C

DOI: 10.1109/educon.2017.7942974

Abstract (EN): Predicting whether a student will pass or fail is one of the most important actions to take while giving lectures. Usually, the experienced teacher is able to detect problematic situations at early stages. However, this is only true for classes up to a hundred students. For bigger ones, automatic methods are needed. In this paper, we present a predictive system based on three criteria retrieved and computed from the logs of the learning management system. We built fast frugal decision trees to help predict and prevent student failures, using data retrieved from their resource usage patterns. Evaluation of the decision system shows that the system's accuracy is very high both in train and test phases, surpassing logistic regression and CART. © 2017 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6