Adaptation and personalization in an e-learning process has a major effect on the relation between tutorial content selection and user learning level. Implicit feedbacks, which are easy to collect and have no effect on learning process, are introduced as valuable resources in many different adaptation methods. Common adaptive learning systems are short of an integrated and standard method for incorporating parameters like time spent for reading (TSR), page clicks, and page scrolling time. Recent methods and reports in this area are topic specific and usually not related to learning paradigms. In order to address this problem, we introduce a general framework for defining a relation between a combination of user implicit feedback and its learning level. This relation is trained as a component by some real data in a way that it could be reused in a learning management system.