Toward adaptive training based on bio-behavioral monitoring

Abstract

The present work investigates a cumulative part-task training method that builds up task complexity adaptively based on individual learner states. A research-oriented game entitled “Space Fortress” was used to evaluate two training conditions in a between-group design prior to a third condition involving an adaptive cumulative part task training method. The latter detects when the learner is ready to progress and dynamically adjusts training progression. Here we report the results of the first two conditions. First was the full task condition, where the learner was exposed to the entire task throughout the training session. The second condition followed a cumulative part-task training approach, where sub-tasks were added at fixed progression points. Results showed no statistically significant gain nor loss in terms of learning outcomes between the full task and the non-adaptive cumulative part task condition, adding evidence to previous mixed findings. A trigger rule needed for the adaptive cumulative part task training condition was developed based on short-term patterns of change in performance and mental workload to be used as a dynamic criterion for adaptation. Furthermore, bio-behavioral measures were evaluated as potential proxies for performance and workload with the aim of applying this adaptive method in contexts where performance and workload cannot be directly measured at regular intervals.