Search

In this paper, firstly, the process of building a statistical analysis model is studied in detail, the process of calculating the structural equation is described, and the structural equation algorithm is improved. A correlation model was created based on three dimensions: cognitive involvement, behavioral involvement, and affective involvement. After analyzing the model’s reliability and validity, it was modified to examine the correlation between learning engagement and effectiveness. The results showed that the overall effects of behavioral engagement, cognitive engagement, and affective engagement on key competencies were 0.978, 0.769, and -0.819, and the overall effects of behavioral engagement, cognitive engagement, and affective engagement on basic knowledge were 0.668, -0.569, and 0.586. The correlation between learning engagement and learning effectiveness was significant. This paper’s study has significant implications for educational reform in colleges and universities and for students to enhance their learning effectiveness.

This paper constructs the architecture of the online teaching platform for education economics based on the MVC pattern coupled with three-tier architecture. The optimal value function is solved through the experience playback mechanism and the strategy function of reinforcement learning, and the value function is used to realize the update of the deep Q learning parameters, which is used to realize the guided teaching of students in the platform. A test analysis was conducted to verify the effectiveness of using the online teaching platform for education economics courses. The results show that the waiting time for teaching resource requests in the 0-3s range of this paper’s platform is 29.71% and 51.56% lower than that of the B/C and Web platforms, respectively. The mean value of students’ learning attitude toward the platform was 108.558, and the highest satisfaction score was 0.7088. The online teaching platform can effectively improve students' learning attitude and promote the innovation of information technology in education economics classroom teaching.

In this paper, for the innovative teaching of high school project-based courses, an adaptive differential evolutionary algorithm is used as one of the tools of the project to help students solve complex problems. The traditional adaptive differential evolution algorithm has the problems of local optimum and low convergence accuracy; to overcome these limitations, this paper proposes an enhanced reverse learning adaptive differential evolution algorithm. The OL-ADE algorithm is improved by introducing the reverse learning strategy in machine learning to extract the reverse elite individual population from the original individual population and select the better-adapted individuals for mutation operation. After the project-based teaching, the students’ works have high scores in layout color matching, technical operation, organization, information awareness, and information social responsibility, and their scores are 8.6, 9.75, 9, 9.35, and 8.75, respectively. Therefore, this paper proves the Adaptive Difference Evolution algorithm’s effectiveness in innovative teaching of high school project-based courses.

In this paper, we first construct ontology and entity learning to construct a knowledge graph by matching the knowledge to the schema layer. Then, we obtain users’ learning styles, extract interest keywords from user behavioral data, map them into the vector space encoded based on the KgTransH algorithm, and compare them with the original algorithm on multiple datasets. Then, the business English learning path is planned based on learning path length, learning time and achievement ranking. Finally, the effect of the knowledge graph-based business English learning path planning method is analyzed through comparative experiments. Through 50 iterations, the satisfaction of the knowledge graph method, GA method and ACO method are between 4.3-4.8, 4-4.7 and 4.05-4.7, respectively, which indicates that the method of this paper is better than the other methods. This study is important for improving the learning effect of Business English.

This paper aims to construct a resource base for adult online education using a multimodal fusion basis. The fusion processing methods of knowledge resources incorporate temporal and non-temporal feature learning. This paper proposes a process for constructing a multimodal subject knowledge space that can aid educators in organizing and presenting multimodal teaching content effectively. Also, the operation process of each major functional module of the adult online education resource library, including the online teaching process, online learning process and online resource production process, is studied in detail. Finally, the performance test of optimizing small file storage, the performance test of de-duplication of redundant data of learning resources and the stress test were conducted on the constructed resource library. The study concluded that the resource memory occupancy of the computer class was reduced by 0.35 after the de-duplication process, and in the stress test, the login response time was less than 8 seconds, and the average CPU utilization of the database server was

This paper aims to create a digital platform that uses big data to manage HR performance. Firstly, the FCM algorithm transforms the dataset according to its affiliation degree and constructs a new database. An objective function is established to optimize the classification of organizational information using the correlation between data. Finally, the AHP algorithm is introduced to set indicator weights and factor sets, making the assessment results more hierarchical. The results show that the management platform system constructed in this paper operates stably, keeping the departure rate of university staff at about 5%, and 92% of students believe that the platform can improve teachers’ teaching quality. This shows that big data technology optimizes human resources management in colleges and universities.

This paper analyzes the current situation of physical education teaching in colleges and universities from the perspective of deep learning and innovates the teaching methodology. A multidimensional data array is used to quantify students’ interests and hobbies to create a recommendation module for intelligent sports learning materials. Using the SIFT feature extraction algorithm, identify the extreme value point of the detection target and develop the sports target extraction module. Through distance transformation and morphological features, determine the skeleton image features to construct a college sports teaching model using deep learning. The results show that the model in this paper enables the coverage rate of sports courses to reach over 97.20%, with a fluctuation of no more than 1.5%. Deep learning has contributed to some innovation in college sports teaching.

This paper designs a scheme for a nursing professional teaching quality evaluation system, including constructing an evaluation index system and a nursing teaching quality evaluation model based on a fuzzy comprehensive evaluation. Among them, the establishment of a fuzzy comprehensive evaluation model involves the determination of fuzzy operators and the processing of evaluation indexes. Finally, the constructed method was used to conduct a study on evaluating nursing teaching quality in College H for students of different academic years. The results showed that freshman, sophomore, and junior students were inconsistent in all aspects of faculty teaching effectiveness and overall evaluations. The total rating of freshmen was 18.34±0.17. The total rating of sophomores was 20.50±0.15. The total rating of juniors was 23.46±0.12. And 66.7% of the faculty members thought that the quality of nursing teaching at the university was not up to standard. Therefore, this paper provides a valid assessment method for nursing education.

This paper takes the problems and ways of diversified curriculum construction of vocal music education in colleges and universities as the entry point and constructs a deep blended learning model of vocal music diversified curriculum based on the deep learning model. The deep evolutionary knowledge tracking model is constructed by combining the self-attention mechanism embedding model with vocal music knowledge evolution based on Transformer. To verify the effectiveness of the deep blended learning model constructed in this paper, model comparison experiments and empirical analysis were conducted. The results show that compared with the SAKT model, the DBL model in this paper has a 5.19% improvement in the average ROC-AUC value. The vocal posttest scores of the students in the experimental class were 5.09 points higher than those of the control class, with a two-tailed significance value of 0.027, which is less than 0.05 and a significant difference. This indicates that the deep learning model can effectively promote the innovative design of diversified curricula for vocal music education in colleges and universities and enhance students’ vocal music learning ability.

Based on structural equation modeling, this paper measures and evaluates the transformation of scientific and technological achievements of colleges and universities in terms of implicit and explicit variables. The PLS algorithm is used to measure the intrinsic factor, which is designed to follow the Gaussian distribution and reach the expected value through iterative loops. The indicators and parameters of the explicit variables are evaluated by combining the principal component analysis and the great likelihood method. According to the results, the factors that are influenced by the policy environment account for 16.5%, while the problem of capital investment accounts for 36.3%. The number of invention patents increased by 8.93% after measuring and assessing them using the method presented in this paper. It demonstrates that universities can explore their technological achievements in depth and enhance their scientific research level.