Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs
This work addresses the need for improved midwife training to reduce maternal and newborn deaths, but it is incremental as it applies existing forecasting methods to a new dataset.
The paper tackled the problem of predicting content demand for midwives using user-generated logs from an online learning app, aiming to personalize and adapt learning journeys, and evaluated various forecasting methods to determine future user interest by profession and region.
Every day, 800 women and 6,700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help to improve their learning competencies. The goal is to use these rich behavioral data to push digital learning towards personalized content and to provide an adaptive learning journey. In this work, we evaluate various forecasting methods to determine the interest of future users on the different kind of contents available in the app, broken down by profession and region.