Teaching management of information resources: A course description

This paper integrates data mining technology into the technical processing layer of the “Artificial Intelligence +” archive intelligent service system to provide technical support for massive archive data. Using association rules, archive classification, Mean-Shift clustering, and other techniques to reveal the relationship between archives and users, archives, archives, and various practical activities so as to play the role of the information of these archive data at a deeper level. Applying the “Artificial Intelligence +” archive wisdom service system to the actual operation of the business management of college archive professional teachers, the results show that the speed of “Artificial Intelligence +” database entry (4.83s) is significantly higher than that of the traditional database (38.62s) the speed is higher. The contour coefficients of the classification results of the experimental group are all above 0.80, realizing high-precision classification of text data, image data, and audio data in digitized archives.2018-2023 The number of business declarations is rising, the total score of the scientific research type varies considerably, and the total score of the teaching type is concentrated in the distribution, with most of them being between 70-80. Mean-shift clustering algorithms can make the scientific research type, the teaching type, and the Research and Teaching and heavy type three data types to achieve a good clustering effect.

Section: Introductionmentioning

confidence: 99%

A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining

Wang,

Li,

et al. 2024

“…There are a wide variety of computer course areas covering multiple levels, from basic programming to advanced algorithms. Students' interests and abilities vary widely, so a system that provides personalized recommendations is critical to improving learning efficiency and interest [10][11]. The role of data mining techniques in this system is to analyze students' learning behaviors and discover potential patterns and trends [12].…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques

Zhao,

Jing,

Guo

2024

This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.

“…The construction of a teaching resource base can integrate excellent teaching resources, facilitate resource sharing, and achieve the fundamental objective of improving teaching quality. [6][7]. The construction of teaching resource base can also promote the integration of educational technology and curriculum, provide powerful support for innovative teaching modes, provide rich and high-quality teaching materials for the development of network courseware and teachers' lesson preparation, avoid a large number of repetitive labor, and at the same time provide the conditions for students to use the resource base to obtain information and active learning, and provide effective support for the comprehensive promotion of quality education and the realization of educational informatization [8][9].…”

Section: Introductionmentioning

confidence: 99%

The Construction of English Teaching Resource Base in Colleges and Universities Based on Knowledge Graphs

Liang

2024

Establishing a teaching resource base for English in higher vocational education is one of the effective informatization reform measures. In this paper, the basic framework of English teaching resource base based on a knowledge graph is firstly designed to semantically describe and organize the existing teaching resource organization structure and storage method in the form of a graph based on text semantic coding. Using entity linking technology and knowledge point automated association method, the entity vocabulary in the text is identified and linked to the corresponding entities in the knowledge base to realize the association between teaching resources and knowledge points. Then, the cosine similarity between historical learning resources and teaching resources in terms of semantic features is calculated, and resource recommendation is carried out based on the knowledge connectivity between user interests and resources. The evaluation of the teaching resource library’s performance and its effect on students’ English learning and teachers’ teaching ability is concluded. The results show that users’ evaluations of the indicators of the teaching resource library are concentrated between (3.9-4.4), the p-values of the dimensions of students’ English knowledge in the two cases of yes/no use of the resource library are 0.010, 0.047, 0.038, 0.035, 0.018, and 0.026, and there is a significant variability of the teacher’s teaching ability at the level of 5 percent. This study offers guidance on how to promote the reform of English curriculum construction, optimize teaching resources, and enhance teachers’ overall teaching ability.