Performance-Differences in Groups based on Preferences in a Language Learning Online Course

Online courses can be adapted to suit learner needs. Although it is known that learners are diverse, courses are often optimized using split tests to find an optimum that results in the best performance for the majority of participants. This is the best-practice approach, which is cost-efficient using well-defined statistical fundaments. However, learners are considered as one cohort, independently of subgroups, and their existence is seldom further analyzed. In this paper, we examine two versions of a 45min language learning online course, which cover the same learning content, but one version is enriched by simulations to create different settings of being observed, collaborating with a peer, or taking part in a competition. T-tests over all 157 users identify some tasks, which are optimum for the majority of learners. Nevertheless, such tasks must not be the best for everyone. If learners are split by preference levels, learner performances differ, but the results are in line with the literature, without statistically significance.