## 1 Introduction  
The diffusion of remote learning in the scenario of schools and university, boosted by the recent COVID-19 pandemic, offers new challenges to educators and web designers. The e-learning scenario requires the use of new methods to develop and deliver content [1], as well as new strategies to collect and analyse students' direct and indirect feedback. Students feedback is valuable for evaluation and analysis of students engagement and teachers' evaluation of learning objects and resources [2] based on their usage in a precise learning design (LD) process. The importance of designing the learning process stands in a user-centred representation of the pedagogical intention. Regrettably, these strategies do not take into account the patterns of students' engagement, paramount for the learning process. To analyse students' engagement, we can take advantage of the opportunities that virtual learning environment (VLE) systems can offer to the traditional teaching and learning scenario. Using VLEs, we can monitor how students download, read or watch online learning resources, which is considerably complicated in the traditional approach. In the face-to-face scenario of a traditional frontal lecture, teachers can understand the students' engagement looking at their face, and monitor their activities by the evaluation survey often submitted at the end of the course, together with assessments and progression scores [3]. The traditional face-to-face setting, in which educators glean immediate feedback by verbal and facial cues, as well as the ability to adjust teaching strategies immediately in response to that feedback, is a crucial advantage of in-person teaching. The immediate feedback formed a significant rationale for our study, the goal of which is to bring such decisive capability to the e-learning world. Concerning the relevant feedback of the in-person environment, that we can add to VLEs, we can for sure consider the students engagement in each single learning object. In VLEs, all the interactions and learning activities are recorded automatically in the platform logs, resulting in digital footprints for a large number of users [3]. This continuous data gathering can provide relevant information to instructors to analyse the students' engagement information in real-time. Using such a system, teachers can take immediate actions to align the course enactment to the underlying LD, adapting and modifying the course structure or content, e.g., providing additional learning material to the learning audience, promoting students' interaction with the teacher or within study groups, and learning activities. In general, educators use tools to support and inform the LD process, which does not rely only on the experience of the designer and the best practice of their colleagues. Appropriate analytics tools can support constant monitoring of the impact of the learning material, can help to revise and find flaws in the course learning objects, and test the attainment of pedagogical objectives. Analytics should be based on pre-existing aggregated data on students' engagement, progression and achievement [3]. Most learning management systems (LMS) provide instructors with some form of information to track students' accesses and online activities through system logs and basic visualisations. Usually, such information is provided in the form of numbers, tables, and, less often, graphs, but most of the log information is not visible at all in the user interface. The monitoring interfaces often looks like directed to the system manager, more than to the teacher, to whom is scarcely usable. From the instructor point of view, logs provide a large and useless amount of raw data, where only a few graphical outputs are provided in the form of bar charts and simple filters. Functionalities for exporting data in different formats are usually available.