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The worldwide shift to distance learning at Higher Education Institutions (HEIs) during the COVID-19 global pandemic has raised several concerns about the credibility of online academic activities, especially regarding student identity management. Traditional online frameworks cannot guarantee the authenticity of the enrolled student, which requires instructors to manually verify their identities, a time-consuming task that compromises academic quality. This article presents a comprehensive review of existing efforts around continuous user identification, focusing on intelligent proctoring systems and automatic identification methods, as well as their applicability in this domain. We conclude that there is a clear need for continuous user identification technology by HEIs, but existing systems lack agile system integration models that combine many inputs, such as face, voice and behavioural data in a practical manner, and encounter numerous barriers related to data protection during implementation.

One of the main challenges of massive open online courses (MOOCs) is the effective facilitation of learners in the course forum. The more learners participating in the forum, the more difficult it is for instructors to provide timely support. The effective intervention of teaching assistants (TAs) can play a crucial role in mitigating this issue; however, questions arise regarding the instructional approaches that TAs follow and whether they can effectively promote learning in a MOOC environment. In this article, we study the instructional approaches that TAs followed in two MOOCs of different subject matters, using mixed-methods. The goal was to evaluate the pedagogies that TAs adopted in facilitating learners, following a broadly accepted framework on MOOC instructional quality. Content analysis and interviews with the TAs provided insights into their intervention strategies, while linguistic and social network analysis enhanced the findings. The study revealed that the TAs most often were rushing to provide direct answers rather than guiding learners in finding themselves an answer. Problem-solving approaches and collaboration were not adequately promoted. The findings of this article provide design implications for the development of supportive tools that could automatically assess facilitators' instructional approaches in the forum. Thus, timely feedback could be provided to instructors so as to refine the instructional design of their online courses and to provide guided support to their learner facilitators while courses are running.

The credibility of online examinations in Higher Education is hardened by numerous factors and use-case scenarios. This paper reports on a longitudinal study, that spanned over eighteen months, in which various stakeholders from three European Higher Education Institutions (HEIs) participated, aiming to identify core threat scenarios experienced during online examinations, and to, accordingly, propose threat models, data metrics and countermeasure features that HEI learning management systems can embrace to address the identified threat scenarios. We also report on a feasibility study of an open-source intelligent and continuous student identity management system, namely TRUSTID, which implements the identified data metrics and countermeasures. A user evaluation with HEI students (n = 133) revealed that the TRUSTID system is resilient and effective against impersonation attacks, based on intelligent face and voice identification mechanisms, and scored well in usability and user experience. Aspects concerning the preservation of privacy in storing, retrieving and processing sensitive personal data are also discussed. [ABSTRACT FROM AUTHOR]