# Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning

## TL;DR

This paper presents a deep multitask learning framework that classifies cognitive load and expertise levels in healthcare simulations to enable adaptive training tailored to individual learner needs.

## Contribution

It introduces an end-to-end system combining trauma simulations and neural networks to dynamically adapt simulations based on real-time cognitive and skill assessments.

## Key findings

- Achieved 89.4% accuracy in classifying cognitive load.
- Achieved 96.6% accuracy in classifying expertise.
- Developed a novel dataset with ECG signals from healthcare practitioners.

## Abstract

Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should provide an optimum amount of cognitive load to the learner and be tailored to their levels of expertise. However, most current simulations are a one-type-fits-all tool used to train different learners regardless of their existing skills, expertise, and ability to handle cognitive load. To address this problem, we propose an end-to-end framework for a trauma simulation that actively classifies a participant's level of cognitive load and expertise for the development of a dynamically adaptive simulation. To facilitate this solution, trauma simulations were developed for the collection of electrocardiogram (ECG) signals of both novice and expert practitioners. A multitask deep neural network was developed to utilize this data and classify high and low cognitive load, as well as expert and novice participants. A leave-one-subject-out (LOSO) validation was used to evaluate the effectiveness of our model, achieving an accuracy of 89.4% and 96.6% for classification of cognitive load and expertise, respectively.