基于 LDA - BP 神经网络的高校思政课教师数据驱动决策力评价研究
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G641.0

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国家社会科学基金一般项目“人工智能中因果推理模型的哲学研究”(编号:22BZX028);2022 年度教育部人文社会科学重点研究基地重大项目“复杂适应系统哲学问题研究”(编号:22JJD720016);中央高校基本科研业务费专项资金(编号:ZDPY202210)资助。


Evaluation of Data - Driven Decision - Making Ability of College Ideological and Political Teachers Based on LDA - BP Neural Network
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    摘要:

    数据驱动决策力为高校思政课教师提供科学合理的教学判断,对数据驱动决策力进行评价研究,有助于提高高校思政课教师的数据决策水平,进而提升思想政治教育教学质量。 鉴于传统评测方法缺乏客观性与可重复性,运用LDA -BP神经网络技术构建高校思政课数据驱动决策力的指标体系与评价模型。 首先,运用 LDA 方法对高校思想政治教育相关的政策文本与研究文献进行主题提取,并将主题信息作为指标构建基础;其次,通过研读文献与政策文本,并结合主题分析结果构建高校思政课教师数据驱动决策力评价指标体系;最后,通过对 BP 神经网络的训练及测试来生成高校思政课教师数据驱动决策力的评价模型。 研究表明,高校思政课教师的专业知识、教学水平以及数据分析与解读能力是影响数据驱动决策能力的关键因素,据此,理应从素养提升、文化培育、管理革新、政府支持等方面入手增强数据驱动决策力。

    Abstract:

    Data - driven decision - making power can provide scientific and reasonable judgment for teachers of ideological and po? litical courses in colleges and universities. Evaluation and research on data - driven decision - making power can help improve the level of data decision - making of teachers of ideological and political courses in colleges and universities, and then enhance the teaching quality of ideological and political education. To construct an indicator system and evaluation model for data - driven decision - making power of ideological and political education in colleges and universities, LDA - BP neural network technology is used due to the limita? tions of traditional assessment methods in terms of objectivity and repeatability. Firstly, LDA method is used to extract the subject of policy texts and research literature related to ideological and political education in colleges and universities, and the subject information is used as the basis for constructing indicators. Then, the evaluation index system for data - driven decision - making ability of ideolog? ical and political teachers in colleges and universities is constructed by reading literature and policy texts, and combining the results of theme analysis. Finally, the evaluation model of data - driven decision - making ability of college ideological and political course teach? ers is generated through the training and testing of BP neural network. The research indicates that the professional knowledge, teaching level and data analysis and interpretation ability of ideological and political teachers in colleges and universities are the key factors af? fecting the data - driven decision - making ability. Therefore, it is necessary to enhance the data - driven decision - making ability from the aspects of literacy improvement, cultural cultivation, management innovation and government support.

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  • 收稿日期:2023-06-21
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  • 在线发布日期: 2024-03-25
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