A massive data processing approach for effective trustworthiness in online learning groups

摘要

This paper proposes a trustworthiness-based approach for the design of secure learning activities in onlinernlearning groups. Although computer-supported collaborative learning has been widely adopted in manyrneducational institutions over the last decade, there exist still drawbacks that limit its potential. Among thesernlimitations, we investigate on information security vulnerabilities in learning activities, which may be developedrnin online collaborative learning contexts. Although security advanced methodologies and technologiesrnare deployed in learning management systems, many security vulnerabilities are still not satisfactorilyrnsolved. To overcome these deficiencies, we first propose the guidelines of a holistic security model in onlinerncollaborative learning through an effective trustworthiness approach. However, as learners’ trustworthinessrnanalysis involves large amount of data generated along learning activities, processing this information isrncomputationally costly, especially if required in real time. As the main contribution of this paper, we eventuallyrnpropose a parallel processing approach, which can considerably decrease the time of data processing,rnthus allowing for building relevant trustworthiness models to support learning activities even in real time.