Activities: Again, based on the previous analytics measures (Obj.2 and Obj.3), provide the learning content to each specific student according to his/her personal needs, preferences and performance, but with the teacher acting as the curator of the delivered content. For instance, adjust the difficulty of a sequence of questions based on the performance of the student monitored through shallow analytics. Similarly, add or remove parts of learning content based on the predicted future performance obtained through deep analytics. Moreover, deploy personalization techniques to stimulate the engagement of students to the learning process and avoid churn out by providing students with responsive assessment in the form of constructive feedback (e.g., praise, correction, comments, etc.). This can be used for the learners as an extrinsic motivator to stay in lab, e.g., by observing their progress or comparing it with that of other learners.

The produced system must integrate the results of the analytics tools from Obj.2 and Obj.3 in order to offer personalized feedback as well as to assess the responses of the users in order to streamline the process of designing and implementing virtual labs. Moreover, feedback should definitely not discourage students but at the same moment should be challenging enough to keep the engagement level of students high. Since learning in formative years of children is a very sensitive social activity, which determines key features of their character and mindset, it is important that analytics are restricted to a supportive role so as the tutor has the main responsibility for the provided content. For this purpose it is crucial to find the golden ratio between the contribution of analytics and teacher in the learning process.