The COVID-19 pandemic has changed the way we do education in recent years. In fact, thanks in part to the progress of the Internet, there has been an exponential growth in courses delivered in distance mode. Among these, Massive Open Online Courses are undoubtedly those courses where the growth in enrolments has been strongest: in fact, even in universities there are distance courses with thousands of enrolments. In this scenario, it is really difficult, if not impossible, for a teacher to monitor the learning process of her/his class, unless he or she is equipped with one or more tools enabling him or her to follow the students, in their learning process, in a more analytical manner. In this paper we propose a web tool, the AI4T system, a dashboard usable as a web application, which allows the teacher, once an assignment has been assigned to her/his students, to monitor their outcomes through a representation in a two-dimensional space. We present an initial experiment with encouraging results.

Most Software Engineering (SE) human-based controlled experiments rely on students as participants, raising concerns about their external validity. Specifically, the realism of results obtained from students and their applicability to the software industry remains in question. In this short paper, we bring further evidence on this controversial point. To do so, we compare 62 students and 42 software professionals on a bug-fixing task on the same Java program. The students were enrolled in a Bachelor's program in Computer Science, while the professionals were employed by two multinational companies (for one of them, the professionals were from two offices). Some variations in the experimental settings of the two groups (students and professionals) were present. For instance, the experimental environment of the experiment with professionals was more realistic; i.e., they faced some stress factors such as interruptions during the bug-fixing task. Considering the differences between the two groups of participants, the gathered data show that the students outperformed the professionals in fixing bugs. This diverges to some extent from past empirical evidence. Rather than presenting definitive conclusions, our results aim to catalyze the discussion on the use of students in experiments and pave the way for future investigations. Specifically, our results encourage us to examine the complex factors influencing SE tasks, making experiments as more realistic as possible.