Learning to represent healthcare providers knowledge of neonatal emergency care: findings from a smartphone-based learning intervention targeting clinicians from LMICs

Abstract

Modelling healthcare providers' knowledge while they are gaining new concepts is an important step towards supporting self-regulated personalised learning at scale. This is especially important if we are to address health workforce skills development and enhance the subsequent quality of care patients receive in the Global South, where a huge skills gap exists. Rich data about healthcare providers' learning can be captured by their responses to close-ended problems within conjunctive solution space -such as clinical training scenarios for emergency care delivery- on smartphone-based learning interventions which are being proposed as a solution for reducing the healthcare skills gap in this context. Together with sequential data detailing a learner's progress while they are solving a learning task, this provides useful insights into their learning behaviour. Predicting learning or forgetting curves from representations of healthcare providers knowledge is a difficult task, but recent promising machine learning advances have produced techniques capable of learning knowledge representations and overcoming this challenge. In this study, we train a Long Short-Term Memory neural network for predicting learners' future performance and forgetting curves by feeding it sequence embeddings of learning task attempts from healthcare providers from Global South. From this training, the model captures nuanced representations of a healthcare provider's clinical knowledge and their patterns of learning behaviours, predicting their future performance with high accuracy. More significantly, by differentiating reduced performance based on spaced learning, the model can help provide timely warning that helps support healthcare providers to reinforce their self-regulated learning while providing a basis for personalised instructional support to aid improved clinical outcomes from their professional practices.