Activities: Predict the future trajectories of the several key performance indices (KPI’s) measured from the users by exploiting machine learning algorithms (e.g., classification and regression) that have been used in games. This will enable the appropriate adjustment of the learning styles and techniques and facilitate proactive decision-making in a personalized or a segment-based manner. For instance, predictions about the future behavior of learners could be communicated to the teacher when designing new labs so as to avoid labs leading to undesired situations (e.g., frustration of students).

The learning scenarios defined in Obj.1 should clearly demonstrate the predictive power of the developed algorithms and the added value of adopting deep analytics methods from the gaming industry. In contrast to shallow analytics, which come with very high accuracy, a success measure for deep analytics will be the robustness to rapid and unpredictable behavior changes that take place during the formative years of students.