A Dynamic Bayesian Network Approach for Predicting Multitasking Performance

Abstract

This study examines the utilities of a dynamic Bayesian network (DBN) to predict multitasking performance. Multitasking is the practice of conducting more than one task simultaneously. Compared with BN (Bayesian network), the DBN has the advantage of encoding both spatial and temporal relationships of the multiple variables under uncertain information. We established the DBN model based on contextual and observable variables from 19 participants to predict multitasking performance over time. The proposed DBN outperformed the BN model with smaller prediction errors and showed great potential to be used as a tool in error management and operator screening in diverse work environments.