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

Abstract

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