Online virtual labs have been important to educational practice by providing students with distance courses that otherwise would be difficult to be offered. However, the majority of them cannot be easily applied to different courses or pedagogical approaches. In order to overcome this, we propose a high-level, easy-to-use authoring tool that will allow building course-independent high-standard virtual labs. This solution is based on learning and gaming analytics. Ιn the gaming industry, there have been developed strong game analytics methods and tools, which could be easily transferred into the learning domain. Game analytics monitor the users’ activity; model their current behavior through the use of shallow analytics and predict the future behavior of the users through the use of deep analytics. We propose that both of these approaches combined with visuali-zation methodologies will offer insights on what features are important and what functionalities users expect to find in a virtual lab.