The outbreak of the COVID-19 pandemic forced Universities to adopt new approaches in both deliver their learning activities and carry out the examinations. The remote examination approach was selected in order to overcome the limitation of students' physical presence in examination centers. This paper presents the evaluation of the remote examinations conducted by the Hellenic Open University. A sample of 9.276 students participated in order to fill out anonymously a short questionnaire that consists of 11 questions (8 closed-ended Likert-like and 3 open-ended). Both qualitative and quantitative analysis was employed. Analysis revealed that students were satisfied in general from the remote examination process and they expressed their desire to be examined remotely in the future. To the best of our knowledge, this is the first large-scale study of a remote examination process in our country.

HCI researchers and practitioners are increasingly using physiological data to measure User eXperience (UX) parameters. The dynamic nature of physiological data offers a continuous window for an in-depth understanding of users’ interaction experience. However, in order to be truly informative, physiological signals need to be linked to users’ interaction experience aspects, such as their emotional states, in a systematic and efficient way. Studies have shown that skin conductance is a physiological signal highly associated with stress. The main purpose of this paper is to present the validation study of our proposed stress detection mechanism which is integrated into a software named PhysiOBS. PhysiOBS is an observation analysis tool that can be used in the post-study analysis phase. PhysiOBS uses nonspecific skin conductance responses (NS-SCRs) in order to auto-report time periods that are probably associated with a problematic interaction. PhysiOBS can also combine multiple data sources. Hence, UX evaluators are able to further investigate a recorded session in order to reveal additional interaction flaws. The integrated stress assessment mechanism, which uses four trained classifiers, can be applied in the reported periods (auto/expert-reported) in order to classify them as stress or non-stress. For the purpose of the validation study, 24 users were recruited in order to participate in a lab experiment. Results showed that our stress assessment mechanism supports UX evaluators by accurately identifying stressful regions within an interaction scenario.