Modern language learning applications have become 'smarter' and 'intelligent' by including Artificial Intelligence (AI) and Machine Learning (ML) technologies to collect different kinds of data. This data can be used for analysis on a microscopic and/or macroscopic level to provide granulation of knowledge. We analyzed 1,213 French language learner data over a 30-day period, publicly available from Duolingo, to compare the progression of individual learners (microscopic granularity) and large groups of learners (macroscopic granularity). Using network modeling, we compared patterns of individual learners against one another and that of a learning community and determined what groups of learners typically practice across communities. Preliminary results suggest how applications for L2 learning can be designed to create an optimal path for learning. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]