一个基于机器学习的分类模型,以支持患有阅读障碍的大学生使用个性化工具和策略
Andrea Zingoni1, Juri Taborri2, Giuseppe Calabrò2
1Department of Economics, Engineering, Business and Society, University of Tuscia, 01100, Viterbo, Italy. andrea.zingoni@unitus.it.
Scientific reports
|January 3, 2024
概括
这项研究引入了一种机器学习模型,以个性化为大学生提供阅读障碍支持. 该算法准确预测有效的数字工具和学习策略,促进高等教育的包容性.
科学领域:
- 教育心理学教育心理学
- 计算机科学 计算机科学
- 神经多样性研究 神经多样性研究
背景情况:
- 阅读障碍影响全球10%的人口,影响阅读,理解和自尊.
- 有效的阅读障碍支持需要个性化,以满足学生的个人需求.
- 目前的大学支持往往侧重于减轻工作量,而不是定制的学习策略.
研究的目的:
- 开发和验证一种机器学习模型,用于对有效的阅读障碍支持方法进行分类.
- 为患有阅读障碍的大学生确定个性化的数字工具和学习策略.
- 通过弥合阅读障碍学生的差距,增强高等教育的包容性.
主要方法:
- 监督机器学习技术被用来构建一个预测算法.
- 一份分发给1200多名大学生的自我评估问卷提供了培训数据.
- 该模型在与学生经历的问题和支持有效性相关的数据上进行了训练和测试.
主要成果:
- 该模型确定了17个有用的工具和22个有用的策略来支持阅读障碍.
- 平均预测准确度超过90%,可预测干预措施达到94%.
- 该算法在建议定制支持方面表现出高效率.
结论:
- 开发的算法可以有效地预测个性化的阅读障碍支持,减少成就差距.
- 这种方法使大学能够修改教学活动以满足学生的需求,促进包容性.
- 这些发现支持向适应性和个性化教育支持系统的转变,以帮助阅读障碍学生.
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