基于深度学习模型的大学生日常思想政治教育创新研究

Xianwei Zhang, Yueyan Zhang
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引用次数: 0

摘要

随着信息化的不断发展,各种网络信息混杂在一起,对教育产生了很大的影响。而信息化的发展为日常思想政治教育提供了便利,有效地解决了日常思想政治教育的时间和空间限制,实现了可持续发展。从而对大学生高尚道德的形成产生积极的影响。可以有效整合信息化教育资源,提高资源利用率。在思想政治教育信息资源方面,我们提出了一个完整的大学生日常思想系统框图。首先,设计一份完整的大学生日常思想政治教育作用互动分析问卷。通过问卷调查法,进行调查和统计权重得分,分析各指标的占比。然后,采用网络环境下的教学框架,包括课堂辅导学习、课堂互动学习、课堂深度学习、过程评价和反馈评价。通过一段时间的思想政治教育来学习。收集数据作为我们的训练语料库。最后对基于bert - bilstm - crf的训练预测模型进行训练。基于BERT-BiLSTM- CRF的F1预测可达91.09%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Innovation of Daily Ideological and Political Education for College Students based on Deep Learning Model
Various network information is mixed, which has a great impact education, with continuous development informatization. However, development of informatization has provided convenience for the daily ideological political education, effectively solved time and space limitation daily ideological, and sustainable development. Therefore, positively influence formation of college students' noble morality. The informatization education resources can be effectively integrated, and the utilization rate resources improved. Information resources of ideological and political education, we propose a complete block diagram of the daily ideological system of college students. First, design a complete interactive analysis questionnaire for college student’s role of daily ideological and political education. Through questionnaire survey method, the survey and statistical weight scores were conducted to analyze the proportion of each indicator. Then, the framework of education in the network environment is adopted, which includes, class tutoring learning, class interactive learning, class in-depth study, process evaluation and feedback evaluation. Learn through a period of ideological and political education. Collect data as our training corpus. Finally, the training prediction model BERT-BiLSTM-CRF-based trained. Prediction of F1 BERT-BiLSTM- CRF -based can reach 91.09%.
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