Language education has a rich history of research and scholarship focusing on the effectiveness of learning activities and the impact these have on student behaviour and outcomes. One of the basic assumptions in foreign language pedagogy and CALL in particular is that learners want to be able to communicate effectively with native speakers of their chosen language. Combining principles of learning analytics and Big Data with learning design, this study used a student activity based taxonomy adopted by the Open University UK to inform module design. The learning designs of four introductory and intermediary language education modules and online engagement of 2111 learners were contrasted using weekly learning design data. In this study, we aimed to explore how learning design decisions made by language teachers influenced students' engagement in the VLE. Using fixed effect models, our findings indicated that 55% of variance of weekly online engagement in these four modules was explained by the way language teachers designed weekly learning design activities. Our learning analytics study highlights the potential affordances for CALL researchers to use the power of learning design and big data to explore and understand the complexities and dynamics of language learning for students and teachers.