In March 2020, due to the Covid19 pandemic, higher education had to switch from face-to-face to exclusively virtual mode overnight. In this unexpected scenario, supervisors also had to adapt the assessment procedures, including the exams. This caused a significant controversy, as, according to many students, supervisors were more concerned about how to prevent students from cheating, than actually measuring their learning. This paper introduces an experience that implemented several of the students' requests in an online exam and conducts a comprehensive analysis of students’ behavior according to the virtual learning environment records. Different existing software tools are used for the analysis, complemented with a Python application ad-hoc developed. The objective indicators gathered provide evidence that some students cheated and invite focusing on evidence-based assessment.

Chatbots have arrived in higher education, and professors are trying to make the most of them. Typically, chatbots are used to help students learn academic subjects. In times of crisis, such as the COVID-19 pandemic, students who were not living with their families during the course, especially international students, were isolated and in critical situations. The student services offices were in constant contact with these students to solve problems, advise them and support them during their stay, within the constraints of confinement and the guidelines dictated by the country at the time. The student services offices were overwhelmed trying to help these students because, although the students' problems were very recurrent, the government guidelines changed from one day to the next. This article proposes the use of a chatbot to provide initial support to students during crisis situations, and facilitate communication between them and the university. The chatbot was tested by more than 160 students and student services staff. The findings support the use of chatbots as a potential tool to facilitate communication with students in emerging emergency situations, and encourage universities to adopt these types of smart tools to be prepared to respond quickly and efficiently to students in times of crisis.

Students’ acquisition of generic skills is key to their incorporation into the job world. However, teachers encounter several difficulties when measuring their students’ performance in generic skills. These difficulties increase in online courses based on learning management systems where there is no direct contact between teachers and their students. To address this problem, this work adopts a solution based on the use of activity records from learning management systems as indicators of students’ performance in generic skills. After conducting a survey among teachers of online courses on the suitability of the solution, an artefact was developed and applied within four online courses. Interviews conducted with teachers and the results of a survey for mapping skills and activities provide positive evidence of this solution’s potential.

Wikis are appropriate tools for deploying authentic assessment experiences for learning and work scenarios in which a group of users are asked to develop a shared task. However, when the number of wiki users increases, the number of contributions can grow at a pace whereby accurately assessing them becomes a complex and non-scalable task. While different quantitative approaches have been shown to be scalable, they are usually coarse-grained and provide limited feedback about the assessment. This work proposes a scalable assessment methodology for wiki-based tasks, based on qualitative self- and peer assessment of wiki contributions. The methodology is implemented using a software tool and is applied as part of an undergraduate course, complementing a quantitative assessment approach. Positive evidence on the scalability of the method and how it implements a more fine-grained qualitative assessment than the regular quantitative approach is found, providing indicators for assessing both individual and group generic skills.