Analysis of Multitasking in Divided Attention using Machine Learning

Bhanu Pratap Singh Bankoti, C. Gupta, O. Bandyopadhyay, Mallika Banerjee
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Abstract

Evaluation of cognitive functionality plays an important role in the career choice of students as well as for the selection of employee for the employer. Divided attention is one such cognitive ability that deals with allocation of attention to multiple tasks simultaneously. An accurate analysis of divided attention would help us to identify cognitive decline, as well as provides a quantifiable indicator of a salient feature viz., vigilance which is highly relevant for defence personnel as well as pilots in air, water and road. The close observation of divided attention in home or classroom environment is an essential component for early detection of cognitive problems. It also helps in assessing the effectiveness of learning patterns. This work proposes a new method to determine the ability of relative divided attention through unobtrusive monitoring of use of a software game. The process measures the performance of a user (college student) on a multi-task cognitive software by computing the scores as part of the test for divided attention. This metric indicates the user's ability of multitasking in divided attention, i.e whether user is efficiently paying attention to all the tasks at once, or is primarily attending to one task at a time (sacrificing optimal performance). The data set is labelled based on statistical analysis. After classifying the data using machine learning model (random forest), the academic performance of the user is analysed against the divided attention levels to establish a correlation among them.
用机器学习分析分散注意下的多任务处理
认知功能评价在学生的职业选择和雇主对雇员的选择中起着重要的作用。分散注意力就是这样一种认知能力,它处理注意力同时分配到多个任务上。对注意力分散的准确分析将有助于我们确定认知能力的下降,并提供一个显著特征的可量化指标,即对国防人员以及空中、水上和公路上的飞行员高度相关的警惕性。在家庭或课堂环境中密切观察注意力分散是早期发现认知问题的重要组成部分。它还有助于评估学习模式的有效性。这项工作提出了一种新的方法来确定相对分散注意力的能力,通过不引人注目的监测使用的软件游戏。这个过程通过计算分数来衡量用户(大学生)在多任务认知软件中的表现,作为分散注意力测试的一部分。这个指标表明用户在分散注意力的情况下进行多任务处理的能力,即用户是一次有效地关注所有任务,还是一次主要关注一个任务(牺牲最佳性能)。根据统计分析对数据集进行标记。在使用机器学习模型(随机森林)对数据进行分类后,根据划分的注意力水平分析用户的学习成绩,以建立它们之间的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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