Go with the Flow: Personalized Task Sequencing Improves Online Language Learning

Machine learning (ML) based adaptive learning promises great improvements in personalized learning for various learning contexts. However, it is necessary to look into the effectiveness of different interventions in specific learning areas. We conducted an online-controlled experiment to compare an online learning environment for spelling to an ML based implementation of the same learning platform. The learning platform is used in schools from all types in Germany. Our study focuses on the role of different machine learning-based adaptive task sequencing interventions that are compared to the control group. We evaluated nearly 500,000 tasks using different metrics. In total almost 6,000 students from class levels 5 to 13 (ages from 11–19) participated in the experiment. Our results show that the relative number of incorrect answers significantly decreased in both intervention groups. Other factors such as dropouts or competencies reveal mixed results. Our experiment showed that personalized task sequencing can be implemented as ML based interventions and improves error rates and dropout rates in language learning for students. However, the impact depends on the specific type of task sequencing.