The ability for learners to self-regulate their learning, is considered as a key factor to achieve academic success. With the increasing popularity of digital learning environments, it has become critical to develop effective ways of supporting self-regulated learning in these contexts to ensure that learners are able to take advantage of the benefits of these platforms, and therefore, measuring self-regulated learning. This paper aims to describe a new approach for analyzing self-regulated learning strategies, while assessing learning skills being acquired by the learner. We propose a two-layer approach that combines an analysis of learners skill levels with the analysis of self-regulation strategies through data traces. This analysis of skills mastery and behaviours leads to qualify the relevance of self-regulated learning strategies. These assessments could serve as a basis to recommend behavioral strategies. This article mainly focus on the presentation of this two-layer system and its first implementation on the Quick-Pi platform dedicated to the learning of the python programming language.