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用MBTI指标和优化启用反复的神经网络的优化为工程学生的反复学术路径推模型.

Anupama V1, Sudheep Elayidom M2

  • 1Division of Computer Science and Engineering, Cochin University of Science and Technology, Kalamassery, Kochi, Kerala, 682022, India. anupamajims@cusat.ac.in.

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概括

这项研究引入了一个智能推系统,使用深度循环神经网络 (DRNN) 和迈尔斯-布里格斯类型指标 (MBTI) 来引导工程学生走向合适的学术道路. 该模型有效地根据学生的背景和个性推课程,实现高精度和回忆.

关键词:
学术路径建议 学术路径建议数据挖掘是一种数据挖掘.深度循环神经网络深度循环神经网络工程专业的学生在学习.迈尔斯-布里格斯类型指示器

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科学领域:

  • 人工智能的人工智能
  • 教育技术的教育技术
  • 计算机科学 计算机科学

背景情况:

  • 在线学习内容的扩散使学生的教育途径选择变得复杂,特别是在工程领域.
  • 工程学生需要有结构的指导来选择适当的学术课程.
  • 个人背景和个性特征是学术道路选择的关键因素.

研究的目的:

  • 为工程学生提出一个智能推模型,以发现合适的学术道路.
  • 利用基于混合优化的深度循环神经网络 (DRNN) 与迈尔斯-布里格斯类型指标 (MBTI) 集成,以提供个性化的课程建议.
  • 提高学术路径推系统的准确性和有效性.

主要方法:

  • 使用日志内核进行数据转换,以提高数据质量.
  • 使用Sparse Fuzzy C-Means Clustering (Sparse FCM) 进行特征选择.
  • 使用经过磁性侵入性杂草优化 (MIWO) 算法训练的DRNN进行适应性建议.
  • MBTI人格类型分类和与使用基于MIWO的DRNN的课程的相关性.

主要成果:

  • 拟议的基于MIWO的DRNN实现了高性能指标:0.900精度,0.900回忆和0.899F测量.
  • 该模型在准确推工程学生的学术道路方面表现出有效性.
  • 在喀拉拉邦和泰米尔纳德邦的数据集上进行的评估,包括人格特征,学业表现和MBTI成绩.

结论:

  • 开发的混合DRNN模型有效地解决了对工程学生学术路径建议的挑战.
  • 将人格特征 (MBTI) 与学术数据相结合,可以显著提高推准确度.
  • 拟议的系统为工程学科的个性化教育指导提供了一个强大的解决方案.