Educational Big Data Mining: how to Enhance Virtual Learning Environments

• Pietro Ducange ^[1] ; Riccardo Pecori ^[1] ; Luigi Sarti ^{[1] [2]} ; Massimo Vecchio ^[1]
  1. [1] Università degli Studi eCampus

     Università degli Studi eCampus

     Novedrate, Italia

     
  2. [2] Consiglio Nazionale delle Ricerche

     Consiglio Nazionale delle Ricerche

     Roma Capitale, Italia

     
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 681-690
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • The growing development of virtual learning platforms is boosting a new type of Big Data and of Big Data Stream, those ones that can be labeled as e-learning Big Data. These data, coming from different sources of Virtual Learning Environments, such as communications between students and instructors as well as pupils tests, require accurate analysis and mining techniques in order to retrieve from them fruitful insights. This paper analyzes the main features of current e-learning systems, pointing out their sources of data and the huge amount of information that may be retrieved from them. Moreover, we assess the concept of educational Big Data, suggesting a logical and functional layered model that can turn to be very useful in real life.