创新创业理论研究与实践 ›› 2024, Vol. 7 ›› Issue (16): 5-7.

• 理论研究 • 上一篇    下一篇

混合式教学模式下大学生深度学习效果测评指标体系的构建

刘克芳   

  1. 九江学院 经济学院,江西九江 332005
  • 出版日期:2024-08-25 发布日期:2024-11-28
  • 作者简介:刘克芳(1983—),女,湖北仙桃人,博士研究生,副教授,研究方向:金融风险和高等教育教学创新改革。
  • 基金资助:
    江西省教育科学“十四五”规划2022年度一般课题“混合式教学模式下大学生深度学习效果测度及其提升路径研究”(22YB229)

Construction of Evaluation Index System for the Effectiveness of Deep Learning among College Students in Blended Learning Mode

LIU Kefang   

  1. School of Economics, Jiujiang University, Jiujiang Jiangxi, 332005, China
  • Online:2024-08-25 Published:2024-11-28

摘要: 深度学习成了教育信息化2.0时代的核心内容。混合式教学充分融合了线上和线下学习的优势,为深度学习提供技术、情境、资源、交互和学习评价等全方位和全过程的支持。该文借鉴国内外经典的深度学习效果测评指标,充分考虑混合式教学模式的特点,构建混合式教学模式下大学生深度学习效果测评指标体系,通过专家咨询问卷获取一级指标重要程度对比打分和二级指标重要性排序结果,再采用层次分析法(AHP)确定各指标的权重。该指标体系可以为大学生深度学习效果测评实践提供参考。

关键词: 混合式教学模式, 深度学习, AHP法, 测评指标体系, 判断矩阵, 一致性检验

Abstract: Deep learning has become the core content of the education informatization 2.0 era. Blended learning fully integrates the advantages of online and offline learning, providing comprehensive and full process support for deep learning, including technology, context, resources, interaction, and learning evaluation. This study draws on classic deep learning performance evaluation indicators from both domestic and foreign sources, fully considers the characteristics of blended learning mode, and constructs an evaluation index system for deep learning performance of college students under blended learning mode. Through expert consultation questionnaires, the importance comparison scores of primary indicators and the importance ranking results of secondary indicators are obtained. Then, the Analytic Hierarchy Process (AHP) is used to determine the weights of each indicator. This indicator system can provide reference for the practice of evaluating the effectiveness of deep learning for college students.

Key words: Blended learning mode, Deep learning, AHP method, Evaluation indicator system, Judgment matrix, Consistency check

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