Modelling children's inhibitory skills using learning data from an educational app

Abstract

AbstractBackgroundEarly literacy and numeracy skills are developed during early childhood. Among the many factors that influence the development of such skills, the literature shows that the executive functions, especially the response inhibition (RI)—that is the capability to block out or to tune out what can be considered irrelevant information or action to the learning task—is one of the most essential functions. There are specific tests used to appraise these children's inhibition skills, but these tests are generally time‐consuming, and demand specialized human resources.ObjectivesWe present a computational approach to model children's RI behaviour through the analysis of educational traces left in an educational app. This modelling allows the automatic and instant identification of the RI level of children without the need of a human‐conducted test.MethodsOur modelling is based on two definitions of RI found in the literature, from which we derived a mathematical formalism of three variables we used to query the traces dataset and isolate the RI behaviour of each student from the learning traces generated in the app. The sample population is composed of children from diverse socioeconomic backgrounds. The model is then assessed by comparing it to a traditional human‐conducted RI test suitable for kindergarten children, the Head‐Toes‐Knees‐Shoulder (HTKS) task.Results and conclusionsThe results show that our RI model can explain an important part of the HTKS variance (up to 0.45 according to the adjusted R2) when taking the HTKS results as a dependent variable for a multiple regression model. In practice, our model can be integrated in a learning app and become a powerful tool for instant preliminary identification of dysfunctional RI behaviour, especially in the early stages of children's education. Once students are identified by our model as having a dysfunctional RI behaviour, teachers can rapidly act to help them. Besides, the proposed model requires only very simple data to work, which means it can be easily integrated into different learning apps.