Content Analytics Support Student Learning in Wall Street English

Digital learning tools provide a plethora of data about how a product is used. At Pearson our analytics teams leverage such data to determine how a product can be improved to optimize learning.

Wall Street English, (add description) one of the most popular english language learning tools for professionals and adults seeking career advancement…

Over the past two years, we’ve leveraged content and learner analytics to 1) guide and inform the design of new course content; and 2) implement an early-warning system to identify learners who may be at risk of not completing the course, in time for effective intervention.

Analytics are Used to Guide & Inform New Course Content

Engaging content alone isn’t sufficient to support student learning, it’s also important that content aligns with what we know about how learners are using a product. To learn more about learners’ study rhythm, Pearson analysts considered platform data from 200K Wall Street English users. By studying usage patterns, our teams were able to determine the effect of the design of interactive video lessons on learners’ lesson completion and performance. Such aspects as as the duration of videos used in lessons, the type of script on which the videos were based, and the accents of actors starring in the videos were analysed, and found to be important elements of the learners experience. These findings helped inform the design of new content, ensuring that the WSE method is supported with information on how their learners study.

Learning Optimized Through the Development of an Early Warning System

In a large scale study of learner analytics (over 59K learners), our analysts identified that certain learner study patterns led to course completion, while other study patterns led to course drop out, even after a learner had already paid for the course. To increase completion rates, our team developed an early-warning system that leverages learner study patterns and alerts Wall Street English tutors to those learners who may be at risk of not completing the course. With the help of the early warning system tutors will effectively be able to intervene with the learners who need the most support, which will, in turn, help more learners to reach their learning goal.