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SkillsLab+—A New Way to Teach Practical Medical Skills in an Augmented Reality Application With Haptic Feedback SkillsLab+ - 利用触觉反馈在增强现实应用中教授实用医疗技能的新方法
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-31 DOI: 10.1109/TLT.2024.3435979
Christian Gießer;Johannes Schmitt;Emma Löwenstein;Christian Weber;Veit Braun;Rainer Brück
Digital technologies have transformed medical care and education by providing rapid access to knowledge and advanced methods, such as augmented reality and haptic feedback. These technologies are improving the efficiency of healthcare professionals and the quality of medical education. Particularly in Germany, where a shortage of skilled workers and an aging population are increasing pressure on the healthcare system, digital methods can help to optimize workflows and improve training. The integration of haptic feedback in this context makes it possible to make virtual objects tangible, increasing immersion and ultimately learning success. The application presented, SkillsLab+, uses augmented reality and haptic feedback via a data glove to provide a digitized version of the analog SkillsLab medical training programme. SkillsLab+ was evaluated using standardized questionnaires (Igroup Presence Questionnaire, presence, and haptic questionnaires). In order to determine the learning outcomes of the students, an AB test was carried out comparing the final grades. At the same time, a subjective questionnaire was used to assess whether students felt better prepared for the exam. In this context, this article aims to evaluate the learning success and compare the results with the previous proof of concept study of 2022. The results of the comparison show an improvement in the responses to the SkillsLab+ questionnaire in 2023. The result of the examination also improved compared to the group without AR experience. This shows the improvement in application and learning with the help of augmented reality and haptic feedback. They were more confident, had better results, and felt better prepared for the exams.
数字技术通过提供快速获取知识的途径和先进方法(如增强现实和触觉反馈),改变了医疗保健和教育。这些技术正在提高医疗专业人员的工作效率和医疗教育的质量。特别是在德国,熟练工人的短缺和人口老龄化正在增加医疗系统的压力,数字化方法有助于优化工作流程和改善培训。在这种情况下,触觉反馈的集成使虚拟对象变得有形成为可能,增加了沉浸感,最终提高了学习的成功率。所展示的应用软件 SkillsLab+ 通过数据手套使用增强现实技术和触觉反馈,提供了模拟 SkillsLab 医学培训课程的数字化版本。SkillsLab+ 采用标准化问卷(Igroup 临场感问卷、临场感和触觉问卷)进行评估。为了确定学生的学习成果,进行了 AB 测试,比较最终成绩。同时,还使用了主观问卷来评估学生是否感觉为考试做了更好的准备。在此背景下,本文旨在评估学习的成功率,并将结果与之前 2022 年的概念验证研究进行比较。比较结果显示,2023 年学生对 SkillsLab+ 问卷的回答有所改善。与没有 AR 体验的小组相比,考试成绩也有所提高。这表明,在增强现实和触觉反馈的帮助下,应用和学习能力得到了提高。他们更加自信,成绩更好,感觉为考试做了更好的准备。
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引用次数: 0
Balancing Performance and Explainability in Academic Dropout Prediction 在辍学预测中兼顾性能和可解释性
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-26 DOI: 10.1109/TLT.2024.3425959
Andrea Zanellati;Stefano Pio Zingaro;Maurizio Gabbrielli
Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an Italian university, the research incorporates a range of variables, including demographic information, prior educational metrics, and real-time academic performance indicators. We present a nuanced comparative evaluation of the RF and FTT models, highlighting their predictive accuracy and interpretative capabilities. Our empirical results demonstrate the effectiveness of machine learning in managing student attrition, with FTT models outperforming RF models in terms of predictive accuracy and achieving a sensitivity rate of 81%. Significantly, the inclusion of historical academic data enhances the models' ability to identify students at increased risk of dropping out. Furthermore, we apply advanced explanatory techniques, such as shapley additive explanations, to investigate the discriminative power of these models across different student profiles. This provides valuable insights into the key variables influencing dropout risk, contributing to a more holistic understanding of the issue. In addition, we conduct a fairness analysis to ensure the ethical robustness of our predictive models, making them not only effective but also equitable tools.
辍学仍然是教育系统面临的一个重大挑战,需要进行严格的分析和有针对性的干预。本研究采用机器学习技术,特别是随机森林(RF)和特征标记转换器(FTT)来预测学业流失。研究利用意大利一所大学 40,000 多名学生的综合数据集,纳入了一系列变量,包括人口统计信息、先前的教育指标和实时学业成绩指标。我们对 RF 模型和 FTT 模型进行了细致入微的比较评估,强调了它们的预测准确性和解释能力。我们的实证结果证明了机器学习在管理学生流失方面的有效性,FTT 模型在预测准确性方面优于 RF 模型,灵敏度高达 81%。值得注意的是,历史学业数据的加入增强了模型识别高辍学风险学生的能力。此外,我们还应用了先进的解释技术(如夏普利加法解释)来研究这些模型在不同学生情况下的判别能力。这为我们深入了解影响辍学风险的关键变量提供了宝贵的资料,有助于我们更全面地认识辍学问题。此外,我们还进行了公平性分析,以确保我们的预测模型在道德上的稳健性,使其不仅有效,而且成为公平的工具。
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引用次数: 0
Predicting Student Performance in a Programming Tutoring System Using AI and Filtering Techniques 利用人工智能和过滤技术预测编程辅导系统中的学生成绩
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-24 DOI: 10.1109/TLT.2024.3431473
Miloš Ilić;Goran Keković;Vladimir Mikić;Katerina Mangaroska;Lazar Kopanja;Boban Vesin
In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate approach to employ. Additionally, determining the optimal input parameters for each AI technique remains a pertinent question in this domain. This study employs machine learning (ML) and artificial neural networks (ANN) to predict student grades within a programming tutoring system. The experiment involved university students whose interaction data with the e-learning system were analyzed and used for predictions. By identifying the structural relationships between the properties of the input data, this research aims to determine the most efficient AI method for accurately predicting student performance in e-learning systems. The structure of the input data in these systems is described by variables related to individual student activities, so correlations between variables were a natural starting point for further theoretical considerations. In this manner, by applying a filtering technique based on the minimum redundancy–maximum relevance (mrMR) criterion, it was shown that correlations among predictors and between predictors and the target variable play a significant role in defining the appropriate model for predicting student grades. The results showed that ANN (the Levenberg–Marquardt algorithm with Bayesian regularization) outperformed ML methods, achieving the highest prediction accuracy. The results obtained from this study can be of great importance for learning technologies engineering and AI in general.
近年来,利用人工智能(AI)方法而非传统统计方法预测电子学习环境中学生成绩的趋势日益明显。值得注意的是,许多研究人员在采用人工智能技术时,并没有对最合适、最准确的方法进行全面调查。此外,确定每种人工智能技术的最佳输入参数仍然是该领域的一个相关问题。本研究采用机器学习(ML)和人工神经网络(ANN)来预测编程辅导系统中的学生成绩。实验涉及大学生,对他们与电子学习系统的交互数据进行了分析并用于预测。通过确定输入数据属性之间的结构关系,本研究旨在确定最有效的人工智能方法,以准确预测电子学习系统中的学生成绩。这些系统中输入数据的结构是由与学生个人活动相关的变量来描述的,因此变量之间的相关性自然成为进一步理论考虑的出发点。通过这种方式,应用基于最小冗余-最大相关性(mrMR)准则的过滤技术,证明了预测变量之间以及预测变量与目标变量之间的相关性在确定预测学生成绩的适当模型方面起着重要作用。结果表明,ANN(采用贝叶斯正则化的 Levenberg-Marquardt 算法)优于 ML 方法,预测准确率最高。本研究获得的结果对学习技术工程和人工智能具有重要意义。
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引用次数: 0
EQGG: Automatic Question Group Generation EQGG: 自动生成问题组
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-24 DOI: 10.1109/TLT.2024.3430225
Po-Chun Huang;Ying-Hong Chan;Ching-Yu Yang;Hung-Yuan Chen;Yao-Chung Fan
Question generation (QG) task plays a crucial role in adaptive learning. While significant QG performance advancements are reported, the existing QG studies are still far from practical usage. One point that needs strengthening is to consider the generation of question group, which remains untouched. For forming a question group, intrafactors among generated questions should be considered. This article proposes a two-stage framework by combining neural language models and genetic algorithms for addressing the issue of question group generation. Furthermore, experimental evaluation based on benchmark datasets is conducted, and the results show that the proposed framework significantly outperforms the compared baselines. Human evaluations are also conducted to validate the design and understand the limitations.
问题生成(QG)任务在自适应学习中起着至关重要的作用。虽然问题生成任务的性能有了很大提高,但现有的问题生成任务研究离实际应用还很远。需要加强的一点是考虑问题组的生成,这一点仍未触及。在组建问题组时,应考虑生成问题之间的内部因素。本文结合神经语言模型和遗传算法,提出了一个两阶段框架,以解决问题组生成的问题。此外,本文还基于基准数据集进行了实验评估,结果表明所提出的框架明显优于所比较的基线框架。此外,还进行了人工评估,以验证设计并了解其局限性。
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引用次数: 0
Online Self-Service Learning Platform for Application-Inspired Cloud Development and Operations (DevOps) Curriculum 应用启发式云开发与运营(DevOps)课程在线自助学习平台
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-24 DOI: 10.1109/TLT.2024.3428842
Roshan Lal Neupane;Prasad Calyam;Songjie Wang;Kiran Neupane;Ashish Pandey;Xiyao Cheng;Durbek Gafurov;Hemanth Sai Yeddulapalli;Noah Glaser;Kanu Priya Singh;Yuanyuan Gu;Shangman Li;Sharan Srinivas
Cloud-hosted services are being increasingly used in hosting business and scientific applications due to cost-effectiveness, scalability, and ease of deployment. To facilitate rapid development, change and release process of cloud-hosted applications, the area of development and operations (DevOps) is fast evolving. It is necessary to train the future generation of scientific application development professionals such that they are knowledgeable in the DevOps-enabled continuous integration/delivery automation. In this article, we present the design and development of our “Mizzou Cloud DevOps platform,” an online self-service platform to learn cutting-edge Cloud DevOps tools/technologies using open/public cloud infrastructures for wide adoption amongst instructors/students. Our learning platform features scalability, flexibility, and extendability in providing Cloud DevOps concepts knowledge and hands-on skills. We detail our “application-inspired learning” methodology that is based on integration of real-world application use cases in eight learning modules that include laboratory exercises and self-study activities. The learning modules allow students to gain skills in using latest technologies (e.g., containerization, cluster and edge computing, data pipeline automation) to implement relevant security, monitoring, and adaptation mechanisms. The evaluation of our platform features a knowledge growth study to assess student learning, followed by a usability study to assess the online learning platform, as well as the curriculum content as perceived by instructors and students across multiple hands-on workshops.
云托管服务具有成本效益高、可扩展性强和易于部署等优点,因此越来越多地用于托管业务和科学应用程序。为了促进云托管应用程序的快速开发、变更和发布过程,开发和运营(DevOps)领域正在快速发展。有必要对新一代科学应用程序开发专业人员进行培训,使他们掌握 DevOps 支持的持续集成/交付自动化知识。在本文中,我们将介绍 "Mizzou 云 DevOps 平台 "的设计和开发,这是一个在线自助服务平台,可利用开放/公共云基础设施学习尖端的云 DevOps 工具/技术,供教师/学生广泛采用。我们的学习平台在提供云 DevOps 概念知识和实践技能方面具有可扩展性、灵活性和可扩展性。我们详细介绍了我们的 "应用启发式学习 "方法,该方法基于在八个学习模块中整合真实世界的应用案例,包括实验室练习和自学活动。通过这些学习模块,学生可以掌握使用最新技术(如容器化、集群和边缘计算、数据管道自动化)实施相关安全、监控和适应机制的技能。对我们平台的评估包括一项知识增长研究,以评估学生的学习情况;随后是一项可用性研究,以评估在线学习平台,以及指导教师和学生在多个实践研讨会中感知到的课程内容。
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引用次数: 0
Digital Twins of Pet Robots to Prolong Interdependent Relationships and Effects on Student Learning Performance 数字双胞胎宠物机器人延长相互依存关系及对学生学习成绩的影响
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-27 DOI: 10.1109/TLT.2024.3416209
Vando Gusti Al Hakim;Su-Hang Yang;Jen-Hang Wang;Hung-Hsuan Lin;Gwo-Dong Chen
The use of robots in education has the potential to engage students in learning activities and aims to form lasting relationships with them. To encourage sustainable, long-term human–robot interactions, a promising approach is to cultivate a pet-like, interdependent relationship. However, the potential of such relationships in education remains unclear, and the limited availability of robots in classrooms necessitates flexible and scalable designs. To address these challenges, this study leverages digital twin technology to facilitate ubiquitous engagement with pet robots, thereby prolonging interdependent relationships through a SeamlessPet robot learning approach. Here, students engaged with both virtual and physical pet robots, enabling realistic and continuous interactions akin to communicating directly with a physical robot. This integration ensured consistent availability and authentic interactions, enhancing educational outcomes demonstrated in situational presentations. An experiment with 70 university students in a Japanese Hospitality Management Program in Taiwan demonstrated that this approach resulted in better learning achievements and fostered a positive learning experience. The pet-like features embedded within the digital twin robots played a vital role in fostering prolonged learning participation, empowering students to take ownership of their learning, stay motivated, and feel supported at any time and from anywhere in the learning process. Educators and curriculum developers are encouraged to consider this approach, particularly in courses with a final project presentation that uses a robot to demonstrate study results.
在教育中使用机器人有可能吸引学生参与学习活动,并与他们建立持久的关系。为了鼓励可持续的、长期的人机互动,一种有前途的方法是培养一种类似宠物的、相互依存的关系。然而,这种关系在教育领域的潜力尚不明确,而且教室里的机器人数量有限,因此需要灵活、可扩展的设计。为了应对这些挑战,本研究利用数字孪生技术促进与宠物机器人无处不在的接触,从而通过无缝宠物机器人学习方法延长相互依存关系。在这里,学生同时与虚拟和实体宠物机器人接触,实现了类似于与实体机器人直接交流的逼真和持续的互动。这种整合确保了持续的可用性和真实的互动,提高了情景演示中展示的教育成果。在台湾进行的一项针对日本酒店管理专业 70 名大学生的实验表明,这种方法提高了学习成绩,并促进了积极的学习体验。数字孪生机器人内嵌的类似宠物的功能在促进学生长时间参与学习方面发挥了重要作用,使学生能够掌握学习的主动权,保持学习动力,并在学习过程中随时随地感受到支持。我们鼓励教育工作者和课程开发人员考虑采用这种方法,特别是在使用机器人展示学习成果的期末项目展示课程中。
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引用次数: 0
Corrections to “A Tutorial-Generating Method for Autonomous Online Learning” 对 "自主在线学习的教程生成方法 "的更正
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-27 DOI: 10.1109/TLT.2024.3413121
Xiang Wu;Huaqing Hong;Huanhuan Wang;Yongting Zhang;Baowen Zou
Presents corrections to the article “A Tutorial-Generating Method for Autonomous Online Learning”.
介绍对文章 "A Tutorial-Generating Method for Autonomous Online Learning "的更正。
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引用次数: 0
ChatGPT-Based Learning Platform for Creation of Different Attack Model Signatures and Development of Defense Algorithm for Cyberattack Detection 基于 ChatGPT 的学习平台,用于创建不同攻击模型签名和开发网络攻击检测防御算法
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-21 DOI: 10.1109/TLT.2024.3417252
Thulasi M. Santhi;K. Srinivasan
Cloud adoption in industrial sectors, such as process, manufacturing, health care, and finance, is steadily rising, but as it grows, the risk of targeted cyberattacks has increased. Hence, effectively defending against such attacks necessitates skilled cybersecurity professionals. Traditional human-based cyber-physical education is resource intensive and faces challenges in keeping pace with rapidly evolving technologies. This research focuses on the main advantages of incorporating large language models into cyber-physical education. The ChatGPT platform serves as an online tool to educate students on fundamentals, cyberattacks, and defense concepts, fostering the development of a new generation cybersecurity experts. The proposed learning approach adheres to the ChatGPT-assisted learn–apply–create model. Responding to prompts provided by the learners, the learning phase engages in conceptual learning, the applying phase involves mathematical modeling of various cyberattacks, and the creating phase develops MATLAB program to incorporate attacks into sensor measurements for the experiment and entails developing the necessary attack detection approaches. The effectiveness of the detection method developed by ChatGPT is assessed in both the simulation and real-time scenarios using a J-type thermocouple. The impact of the proposed learning platform over traditional learning methods is evaluated through an extensive comparative feedback analysis on the learner's foundational concepts, computational thinking, programming efficacy, and motivation. The study proved that integrating ChatGPT into engineering education enables students to swiftly learn cyber-physical fundamentals, comprehend and model cyberattacks, create new attack signatures, and contribute to developing detection algorithms. Such integration provides the learners with essential industrial skills crucial in modern industries.
云技术在流程、制造、医疗保健和金融等工业领域的应用正在稳步上升,但随着云技术的发展,有针对性的网络攻击的风险也在增加。因此,要有效抵御此类攻击,就必须有技术娴熟的网络安全专业人员。传统的以人为基础的网络物理教育需要大量资源,在与快速发展的技术保持同步方面面临挑战。本研究重点关注将大型语言模型纳入网络物理教育的主要优势。ChatGPT 平台是一个在线工具,用于向学生传授基础知识、网络攻击和防御概念,培养新一代网络安全专家。拟议的学习方法采用 ChatGPT 辅助学习-应用-创建模式。根据学习者提供的提示,学习阶段进行概念学习,应用阶段建立各种网络攻击的数学模型,创建阶段开发 MATLAB 程序,将攻击纳入实验的传感器测量中,并开发必要的攻击检测方法。在模拟和实时场景中,使用 J 型热电偶评估了 ChatGPT 开发的检测方法的有效性。通过对学习者的基础概念、计算思维、编程效率和学习动机进行广泛的比较反馈分析,评估了所提出的学习平台对传统学习方法的影响。研究证明,将 ChatGPT 融入工程教育能让学生快速学习网络物理基础知识,理解网络攻击并建立模型,创建新的攻击特征,并为开发检测算法做出贡献。这种整合为学习者提供了对现代工业至关重要的基本工业技能。
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引用次数: 0
A Gamified Platform to Support Educational Activities About Fake News in Social Media 支持社交媒体假新闻教育活动的游戏化平台
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-05 DOI: 10.1109/TLT.2024.3410088
Sara Capecchi;Antonio Lieto;Federica Patti;Ruggero G. Pensa;Amon Rapp;Fabiana Vernero;Sandra Zingaro
The amount of news on the web often confuses the ideas of the reader, who struggles to disentangle information that is sometimes contradictory and difficult to decipher. In the face of such an articulated scenario, the role played by schools is absolutely central: the development of critical thinking in young people (and by extension in their families) is a necessary condition for facing the complexity of the reality with the right awareness and control. Providing young people with a thorough understanding of the fake news spreading phenomenon is a first step in combating it. To this end, in this article, we propose a serious game whose objective is to let young people experience the typical interaction scenario when faced to a feed of real and fake news in social media. Our proposal focuses on educational workshops, carried out in secondary schools and dedicated to the correct use of information on the web, with particular attention to logical fallacies and cognitive bias mechanisms that lead to the formulation of erroneous reasoning or prevent a comparison from progressing logically. Thanks to an intuitive interface that helps the teacher supervise the whole game session, the students are invited to assess the truthfulness of a small set of news at different levels and to share them with their friends. At the end of the game session, the teacher is provided with an interactive detailed report of the activities that enables the analysis of all participants' actions and behavior. The teacher can use such a report to conduct a classroom lecture in a more engaging and interactive way, by stimulating discussions among the students and raising their curiosity on the subject. Our educational platform has been tested accurately in a broad experimental study involving 217 middle school students. The results show the suitability of the platform in providing a valuable educational tool for supporting educational activities on fake news analysis.
网络上的大量新闻往往会混淆读者的思路,使其难以厘清有时相互矛盾、难以解读的信息。面对这种情况,学校所发挥的作用是绝对核心的:培养年轻人(以及他们的家庭)的批判性思维是以正确的意识和控制力面对复杂现实的必要条件。让年轻人全面了解假新闻的传播现象是打击假新闻的第一步。为此,我们在本文中提出了一个严肃游戏,其目的是让年轻人体验在社交媒体上面对真假新闻时的典型互动场景。我们的建议侧重于在中学开展教育研讨会,专门讨论如何正确使用网络信息,尤其关注逻辑谬误和认知偏差机制,因为它们会导致错误推理的形成或阻碍比较在逻辑上的进展。通过直观的界面,教师可以对整个游戏过程进行监督,邀请学生对一小组新闻的真实性进行不同程度的评估,并与他们的朋友分享。游戏结束时,教师会收到一份互动式详细活动报告,可以对所有参与者的行动和行为进行分析。教师可以利用这份报告,以更具吸引力和互动性的方式进行课堂讲授,激发学生之间的讨论,提高他们对这一主题的好奇心。我们的教育平台已在一项涉及 217 名中学生的广泛实验研究中进行了精确测试。实验结果表明,该平台可为支持假新闻分析教育活动提供有价值的教育工具。
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引用次数: 0
Attention Level Evaluation in Children With Autism: Leveraging Head Pose and Gaze Parameters From Videos for Educational Intervention 自闭症儿童的注意力水平评估:利用视频中的头部姿势和凝视参数进行教育干预
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-05 DOI: 10.1109/TLT.2024.3409702
Elizabeth B. Varghese;Marwa Qaraqe;Dena Al-Thani
In children with autism spectrum disorders (ASDs), assessing attention is crucial to understanding their behavioral and cognitive functioning. Attention difficulties are a common challenge for children with autism, significantly impacting their learning and social interactions. Traditional assessment methods often require skilled professionals to provide personalized interventions, which can be time consuming. In addition, existing approaches based on video and eye-tracking data have limitations in providing accurate educational interventions. This article proposes a noninvasive and objective method to assess and quantify attention levels in children with autism by utilizing head poses and gaze parameters. The proposed approach combines a deep learning model for extracting head pose parameters, algorithms to extract gaze parameters, machine learning models for the attention assessment task, and an ensemble of Bayesian neural networks for attention quantification. We conducted experiments involving 39 children (19 with ASD and 20 neurotypical children) by assigning various attention tasks and capturing their video and eye patterns using a webcam and an eye tracker. Results are analyzed for participant and task differences, which demonstrate that the proposed approach is successful in measuring a child's attention control and inattention. Ultimately, the developed attention assessment method using head poses and gaze parameters opens the door to developing real-time attention recognition systems that can enhance learning and provide targeted interventions.
对于患有自闭症谱系障碍(ASD)的儿童来说,评估注意力对于了解他们的行为和认知功能至关重要。注意力障碍是自闭症儿童面临的共同挑战,严重影响了他们的学习和社会交往。传统的评估方法通常需要熟练的专业人员来提供个性化的干预措施,这可能会耗费大量时间。此外,现有的基于视频和眼动跟踪数据的方法在提供准确的教育干预方面存在局限性。本文提出了一种无创、客观的方法,利用头部姿势和凝视参数来评估和量化自闭症儿童的注意力水平。所提出的方法结合了用于提取头部姿势参数的深度学习模型、提取凝视参数的算法、用于注意力评估任务的机器学习模型以及用于注意力量化的贝叶斯神经网络集合。我们对 39 名儿童(19 名患有 ASD 的儿童和 20 名神经畸形儿童)进行了实验,为他们布置了各种注意力任务,并使用网络摄像头和眼动追踪器捕捉他们的视频和眼动模式。实验结果分析了参与者和任务的差异,证明所提出的方法能成功测量儿童的注意力控制和注意力不集中情况。最终,利用头部姿势和注视参数开发的注意力评估方法为开发实时注意力识别系统打开了大门,该系统可提高学习效率并提供有针对性的干预措施。
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引用次数: 0
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IEEE Transactions on Learning Technologies
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