Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs

TL;DR

This paper explores how user-generated logs from online midwifery learning platforms can be used with forecasting models to personalize content and enhance training for midwives, ultimately aiming to reduce maternal and newborn mortality.

Contribution

It evaluates multiple forecasting methods to predict content demand based on behavioral data, enabling personalized and adaptive digital learning for midwives.

Findings

Forecasting models can accurately predict user interest in different content types.

Personalized content recommendations improve learning engagement.

Adaptive learning pathways can be developed using behavioral data.

Abstract

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.