## 1 Introduction
However, only instructors that possess advanced notions in the field of database query languages can fruitfully use them, while it remains difficult for those who do not have extensive previous Information Technology (IT) knowledge. As the population of instructors using e-learning systems is growing, especially in the actual boosting of remote learning due to the COVID-19 pandemic (using, e.g., e-learning, blended learning, and mixed lectures at the same time offered in-person and in e-learning to ensure social distancing), the number of educators with little or no IT background suddenly involved in e-learning is huge. Thus, there is a growing and urgent need of tools for continuous monitoring of learners which fulfills requirements of immediacy, effectiveness, and usability. Desirable properties of such tools are to be:  
* _integrated_ in the standard LMS interfaces;
* _usable_ by every instructor, disregarding their knowledge of data analysis, data mining, or statistics;
* _informative_ and _immediate_, and to be continuously used as part of everyday instructor's activity similarly to in-class feedback. Learning analytics (LA) can help instructors to understand the realisation of their pedagogical intent by analysing the actions and behaviours of students, alone or in learning communities [4]. Instructors should put particular care and caution in the analysis of the students' actions because the access to activities and material does not automatically lead to students' knowledge acquisition [5]. All the tools and techniques that LA provides are useful to evaluate the effectiveness of the pedagogical process in the online context and to provide an in-depth insight into the development of the courses in the LD environment. Most of the current LA tools remain scarcely usable and unpractical [2], while the data is presented in ways that provide difficult interpretation by non-experts [3; 5], e.g., numbers, lists, and tables which require skills and time to be read and analysed. Thus, the current trend is to move the analytics information to a separate learning dashboard, leading to a separation between the daily instructors' activities and the data analysis phase [6]. Instructors have to learn a different interface and use a non-integrated tool to inform their LD process [7; 8] (see [9] for a deeper analysis of alternative solutions using logs). Another drawback of many LA approaches is that they only allow post-course analysis, as they are developed to be used after the course conclusion (i.e., to have a comparison on the same course on different academic years [10]), without a real-time visualisation of the results. Furthermore, such visualisation often requires batch executions on the gathered data, only feasible with appropriate hardware. The current tools, lacking real-time features, fail to provide \"as in-class\" feedback to the instructor. In the e-learning and blended learning environment typical of most of LMSs, most of the interactions with the learners take place offline. Interaction is the core of most of the activities, e.g., discussion forums, self-assessment tests, wiki, flipped lessons, and cooperative learning, while the necessary consumption of course material (e.g., slides, videos, documents) does not require deep interaction. The interaction through an online platform is unquestionably scarce of feedback, in contrast to the face-to-face interaction, where instructors can receive direct signals and in-class feedback from students [3]. What lacks in the current LA scenario, and what we are willing to provide, is a tool that enables the educators to have continuous real-time monitoring of course development and help them to check if the students will correctly receive the LD they pursue [11].