用于中学教育的个性化学习评估,使用循环直觉模糊和Aczel-Alsina bonferroni聚合运算符的混合模型
Raiha Imran1, Kifayat Ullah1,2, Hanen Karamti3
1Department of Mathematics, Riphah International University (Lahore Campus), Lahore, 54000, Pakistan.
Scientific reports
|October 23, 2025
概括
本研究引入了一种新的数据驱动模型,以准确评估中学个性化学习 (PL) 的有效性. 这种创新方法通过管理复杂的学生数据来提高学生绩效评估,以获得更好的教育成果.
科学领域:
- 教育技术的教育技术
- 数据科学数据科学数据科学
- 决策支持系统是什么?
背景情况:
- 由于缺乏可靠的数据驱动模型来评估学生表现,个性化学习 (PL) 的有效性经常受到质疑.
- 学生的数据,包括偏好,优势和弱点,是复杂和不清楚的,这使得测量学习改进具有挑战性.
- 现有的方法很难有效地汇总和分析各种数据源,以评估PL的影响.
研究的目的:
- 引入一种新的决策方法来确定中学个性化学习的有效性.
- 开发一个数据驱动模型,以解决学生数据中的不确定性和复杂性,以准确评估学习.
- 通过精确评估复杂的标准来增强学生的参与度和优化个性化的学习路径.
主要方法:
- 开发和应用循环直觉模糊的阿切尔-阿尔西娜·邦费罗尼表示 (CIFAABM) 聚合运算符 (AO).
- 在循环环境中,将Bonferroni Mean (BM) AO与Aczel-Alsina (A-A) 运营规则集成.
- 与现有方法进行比较分析,以验证拟议模型的有效性.
主要成果:
- CIFAABM方法有效地结合了多个数据源,以揭示PL对个人的影响.
- 该模型准确地捕捉了各种因素之间的相互关系,提供了对学生进步的全面评估.
- 通过比较分析的验证证实了该模型在PL评估中的精度优越性.
结论:
- 拟议的CIFAABM模型提供了一种精确和现实的方法来评估个性化学习的有效性.
- 这种数据驱动的框架为教育工作者提供了一个可扩展和经验性的决策支持工具,用于优化PL.
- 该方法提高了PL评估的准确性,有助于提高学生参与度和学习成果.
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