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

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Figueira, A (Author) FCUP

Conference proceedings International

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

Pages: 1027-1032

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6