School of Marxism, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
J Environ Public Health. 2022 Sep 9;2022:7143786. doi: 10.1155/2022/7143786. eCollection 2022.
Building an evaluation system for new media ideology education in colleges and other higher education institutions is helpful for assessing the current ideology education and encouraging high levels of information technology integration in ideology education has emerged as a key strategy for this type of education. Based on the central tenet of deep learning theory, ideology education for university students can explore educational strategies from six perspectives in order to achieve deep learning for universities. These six perspectives are opening educational channels, integrating educational contents, assisting knowledge construction, creating educational situations, problem-solving, and developing multiple evaluations. This study proposes a deep learning-based evaluation model for ideology teaching through new media in higher education institutions and colleges, applies deep learning theory to the study's research samples, and calculates the degree of association. Test samples are used to evaluate the network, and positive test outcomes are attained. The deep learning model can effectively increase the accuracy of choosing an ideological and political education approach, as evidenced by its average ideal accuracy of 92.6 percent, which is higher than that of PS-BP and DE-BP, which are 86.4 percent and 82.2 percent, respectively.
建立高校新媒体意识形态教育评价体系，有助于评估当前的意识形态教育状况，鼓励高校将信息技术高度融合到意识形态教育中，已成为这类教育的关键策略。基于深度学习理论的中心原则，大学生的意识形态教育可以从六个角度探索教育策略，从而实现高校的深度学习。这六个角度是：开辟教育渠道、整合教育内容、辅助知识构建、创造教育情境、解决问题和开发多元评价。本研究提出了一种基于深度学习的高校新媒体思想教学评价模型，将深度学习理论应用于研究样本，并计算关联度。测试样本被用于评估网络，获得了积极的测试结果。深度学习模型可以有效地提高选择思想政治教育方法的准确性，其平均理想准确率为 92.6%，高于 PS-BP 和 DE-BP 的 86.4%和 82.2%。
