Affiliation:
1. Xi'an Technological University
Abstract
Abstract
The Ministry of Education of China proposed to build a nationwide learning system based on Internet technology, which is also the key goal of building a national level e-learning system. The Ministry of Education proposed that students should actively use new media and information technology to seek and explore in the process of lifelong learning. Based on the above background, this research proposes that in-depth learning technology can be introduced to build an online ideological and political education system. The system design can reflect good processing performance in different data set environments, and can analyze users' preferences during use to adjust the logical structure. After the completion of the design, this study developed a simulation test, and the experimental results can verify the effectiveness of the system algorithm. From the test, it can be concluded that if fixed and dynamic thresholds are selected for testing in different training stages, the dynamic threshold has higher performance in terms of accuracy. Based on the teaching characteristics and development trajectory of ideological and political education, this paper has established a relevant system to combine teaching and examination, and can conduct online information interaction, so as to improve the quality of online political teaching and make the teaching process more coordinated. This paper designs the ideological and political online education system to promote its development by integrating analytical depth learning and Internet technology.
Publisher
Research Square Platform LLC
Reference16 articles.
1. The role of facilitating conditions and user habits: a case of Indonesian online learning platform;AMBARWATI R;J Asian Finance Econ Bus,2020
2. Learner behavior analysis on an online learning platform;El Haddioui I;Int J Emerg Technol Learn (iJET),2012
3. Lu AJ, Xu X (2020) " ‘Learning for the Rise of China’: Exploring Uses and Gratifications of State-Owned Online Platform,” Proceedings of the ACM on Human-Computer Interaction, vol.4, no. CSCW1, pp. 1–25,
4. Disrupted classes, undisrupted learning during COVID-19 outbreak in China: application of open educational practices and resources;Huang R;Smart Learn Environ,2020
5. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences;Lee I;PLoS Comput Biol,2019