Building a learner model for a smartphone-based clinical training intervention in a low-income context: a pilot study

Research is lacking on developing adaptive learning applications for training health workers in low-resource settings making student modelling approaches supporting individualised learning to remain largely unexplored. This study targeted a clinical training intervention using smartphones in a low-resource context to explore if clinicians’ performance patterns can be differentiated into distinctive groups based on an inferred proficiency level using cluster analysis. We also explored the applicability of Knowledge-Component (KC) cognitive learning models-Additive and Performance Factor Models (AFMs, PFMs) - in describing these patterns and their accuracy in predicting performance. The intervention provides simulation training on contextualised management of new-born resuscitation through a series of learning interactions that elicit responses through multiple-choice answers and interactive tasks. AFMs and PFMs were used to explore the impact of previous exposure to KCs within the learning intervention on learner performance. We demonstrate that effectiveness of low-dose-high-frequency training might be linked to successful attempts in previous learning sessions. Additionally, there exists intermediate and expert cadres of health workers who would benefit more from cascading-challenge scenarios. From these results, we propose a preliminary cognitive learning model as a basis for adaptive instructional support on smartphones for clinical training in low-resource settings.