Rational Planning of Educational Resources Based on Big Data Fusion

Introduction

Educational resources, especially teaching materials, serve as a basic (but valuable) tool for personnel training. In addition, they are the foundation for formulating a teaching syllabus and training objectives (Zhao & Li, 2022). With the development of science and technology, educational resources have evolved from traditional, paper-based formats to CD-ROMs to digital tools.

However, educational resources under new media are no longer simply digitized. Redesigned resources make use of video, images, and other multimedia technologies to connect knowledge points. For instance, short micro-lectures are convenient for teaching and conducive to understanding (Jia, 2021). As the level of informatization continues to improve, the way we learn will also change.

Big data plays a key role in linking online technologies, profoundly impacting all walks of life (Qi & Tao, 2018). Emerging networks represented by mobile devices will continue to drive teaching modes and methods, creating an even more digital and networked resource with integration and three-dimensional capabilities. Big data can connect educational resources and promote the deep integration of information technology, education, and teaching (Long, 2020). The integration and sharing of vast, complex educational resources have, therefore, become an important research topic in the current education industry.

Research results have been obtained on the rational planning of educational resources under the integration of big data (Wang, 2017). Researchers have studied the integration and sharing of educational resources based on two-dimensional (2D) code technology, applying it to the integration and sharing of remote digital educational resources. They fully consider the context of educational resources, combining the 2D code icon and 2D code before placing it in the appropriate location within the manuscript.

Other researchers have studied the application of QR codes to an experimental education resource management system. This satisfies students’ independent learning after class, solves the inconvenience of obtaining educational resources for teachers and students, and meets the daily management of laboratories (Cui et al., 2023). Teachers and students can scan QR codes to view animations and videos or link to online massive online open courses (MOOCs) and colleges’ learning platforms to share digital resources (Chen et al., 2020). Thus, learning can be achieved at any time and any place.

Researchers have also proposed the use of artificial intelligence (AI) convolutional neural network technology to integrate shared educational resources. Device programs can be developed through convolutional neural networks. Then, teachers and students can use the program to obtain important resources during the learning process. They can be used to optimize the design of the convolutional neural network education resource integration and sharing methods, improve the model or test it in teaching activities, and carry out evaluations through questionnaires or interviews. Convolutional neural network device recognition programs in learning greatly improves learning efficiency. At the same time, the recognition program aligns with the needs of learning personnel, enabling direct access to educational resources and direct dialogue.

Some researchers have proposed changes in ideological and political educational resources under the background of big data (Rathore et al., 2016). They have analyzed the impact of the era of big data on the development of educational resources, combined with problems in the ideological and political education classrooms of public undergraduate colleges. Research has found that big data improves existing problems, puts forward innovative ideas for the integration and sharing of educational resources in the ideological and political education classrooms of public undergraduate colleges, and streamlines the integration and sharing of ideological and political educational resources (Shi et al., 2019). Some scholars have employed the integration and sharing program of the education resource platform for university martial arts and traditional sports majors via several technical methods (e.g., summary method, typical case study method, and logic analysis method). The corresponding optimization measures improve the quality and efficiency of the integration and sharing of educational resource platforms. In addition, they provide theoretical guidance for the integration and sharing of educational resources.