Big Data and Collaborative Learning: a system for real-time and in-progress monitoring of learners’ satisfaction in online courses

Abstract

Continuous monitoring of learners’ satisfaction (LS) is a key activity for designing effective and successful collaborative learning experiences. Grounded on constructivism and connectivism learning theories, modern ICT platforms allow students performing collaboratively many online tasks, generating large data sets on their interactions. This creates the opportunity to leverage the emerging Big Data paradigm to setup a “non-intrusive” evaluation strategy of online courses that integrates explicit and implicit knowledge. Indeed, the application of Big Data in the collaborative learning domain is a recent explored research area with limited applications, and may have a significant role in the future of higher education. By adopting the design science methodology, this paper presents and discusses the application of an innovative system that relies on Big Data techniques to measure in real-time, both in progress and at the end, the level of LS of online courses. The research contributes to investigate new methods and approaches to measure LS in online collaborative systems by using the Big Data paradigm. The result presented can provide mentors and learning managers with the knowledge and tool for monitoring in progress and at the end the individual learning experience, thus allowing them to intervene effectively along the entire learning process