基于深度多任务学习的自适应模拟认知负荷和专业知识分类
Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning
摘要：
模拟是一种教学手段，使医疗从业者能够以无风险的方式学习，保持或提高他们的知识和技能。这种模拟应该为学习者提供最佳的认知负荷，并根据他们的专业知识水平进行调整。然而，大多数当前的模拟是一种通用的工具，用于训练不同的学习者，而不管他们现有的技能，专业知识和处理认知负荷的能力。为了解决这个问题，我们提出了一个端到端的创伤模拟框架，积极分类参与者的认知负荷和专业知识的发展动态自适应模拟的水平。为了促进这一解决方案，创伤模拟被开发用于收集新手和专家从业者的心电图（ECG）信号。开发了一个多任务深度神经网络来利用这些数据，并对高认知负荷和低认知负荷以及专家和新手参与者进行分类。一个留一主题（LOSO）验证被用来评估我们的模型的有效性，实现了89.4%和96.6%的认知负荷和专业知识的分类，分别为。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
