基于机器学习模型的学习障碍分析与早期诊断工具

Nandha D Anand, Sanitha Lakshmi K Das, Rajalakshmi V R
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引用次数: 0

摘要

学习缺陷是一种特殊类型的神经系统疾病,它会影响孩子的心理能力、单词识别能力、读写能力以及解决问题的能力。这些残疾被称为特殊学习障碍,因为它们主要影响个人的学习成绩,特别是阅读(失读症),写作(书写困难)和数学(计算障碍)(SLD)的问题。这些学生必须在早期阶段被发现,以便在正确的帮助下,他们可以获得足够的特定任务经验,并磨练他们与残疾有关的技能。测试量表工具已被建议用于诊断和识别SLD。建议的工具使可能患有SLD的学生能够参加测试。根据考试的类型,一些单独的问题是重复的。机器学习算法CNN和Random Forest在测试结束后接收测试结果作为输入。算法根据学生的成绩和花在孩子身上的时间来预测有学习障碍的孩子。建议的工具用于创建一个用户友好的集成系统,用于诊断阅读、写作和数学障碍。它还建议家长和教师最好的方法和教学活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Early Diagnosing Tool for Learning Disability using Machine Learning Models
The learning deficit is one particular type of neurological disorder that can affect a kid's mental abilities, word identification, ability to write and read, as well as their capacity for problem-solving. These disabilities are known as Particular Learning Disabilities because they primarily influence individuals' academic performance, particularly reading (dyslexia), writing (dysgraphia), and trouble with mathematical (dyscalculia) (SLD). These pupils must be discovered at an early stage so that, with the right assistance, they can gain sufficient experience with a particular task and hone their disability-related skills. The testing scale tool has been suggested for use in diagnosing and identifying SLD. The suggested tool enables the student who may have SLD to participate in the quiz. Depending on the type of test, some individual questions are repeated. The machine learning algorithms CNN and Random Forest receives the test results as input after the test is over. The algorithms predict children with learning impairments based on student grades and the amount of time spent by the kids. The suggested tool is used to create a user-friendly, integrated system for diagnosing reading, writing, and math impairments. It also suggests to parents and instructors the best methods and instructional activities.
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