arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.01668cs.CYcs.AIcs.CVcs.GRcs.HC
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通过虚拟现实和人工智能确定阅读障碍学生的困难:一项探索性分析

Determining the Difficulties of Students With Dyslexia via Virtual Reality and Artificial Intelligence: An Exploratory Analysis

University of Córdoba(科尔多瓦大学);University of Tuscia(图西亚大学)

Enrique Yeguas-Bolívar, José M. Alcalde-Llergo, Pilar Aparicio-Martínez, Juri Taborri, Andrea Zingoni, Sara Pinzi

AI总结:

本研究通过VRAIlexia项目,结合虚拟现实数据收集与人工智能分析,识别阅读障碍学生的核心困难,其预测模型平均准确率约90%。

摘要:

学习障碍是影响大脑连接交流区域能力的神经系统疾病。阅读障碍学生在阅读、记忆和表达概念方面存在问题;然而,这些问题的严重程度可以通过治疗和补偿机制的建立来减轻。为了缓解这些问题,已经做出了多项努力,为接受小学和中学教育的特定学习障碍学生创建了数字资源。相反,在高等教育中仍然缺乏标准方法。VRAIlexia项目旨在解决这一问题,提出了两种不同的工具:一个集成虚拟现实(VR)的移动应用程序,用于快速简便地收集数据,以及一个基于人工智能(AI)的软件,用于分析收集的数据,以定制每个学生的支持方法。第一个工具已经创建,并正在高等教育机构的阅读障碍学生中分发,用于进行特定的心理和心理测量测试。第二个工具将特定的人工智能算法应用于通过该应用程序和其他调查收集的数据。这些人工智能技术使我们能够识别学生群体面临的最相关困难。我们的不同模型在预测支持工具和学习策略方面获得了约90%的平均准确率。

英文摘要:

Learning disorders are neurological conditions that affect the brain's ability to interconnect communication areas. Dyslexic students experience problems with reading, memorizing, and exposing concepts; however the magnitude of these can be mitigated through both therapies and the creation of compensatory mechanisms. Several efforts have been made to mitigate these issues, leading to the creation of digital resources for students with specific learning disorders attending primary and secondary education levels. Conversely, a standard approach is still missed in higher education. The VRAIlexia project has been created to tackle this issue by proposing two different tools: a mobile application integrating virtual reality (VR) to collect data quickly and easily, and an artificial intelligencebased software (AI) to analyze the collected data for customizing the supporting methodology for each student. The first one has been created and is being distributed among dyslexic students in Higher Education Institutions, for the conduction of specific psychological and psychometric tests. The second tool applies specific artificial intelligence algorithms to the data gathered via the application and other surveys. These AI techniques have allowed us to identify the most relevant difficulties faced by the students' cohort. Our different models have obtained around 90\% mean accuracy for predicting the support tools and learning strategies.

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。

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