A Micro-class Teaching Data Retrieval Method of Business English Based on Network Information Classification
Abstract
In order to quickly extract the required micro-class teaching data of business English in the fragmented network information environment, a micro-class teaching data retrieval method of business English based on network information classification is offered. This way, it constructs a network information classification method on the basis of the optimised SVM model, and the parameters of the SVM model are optimised and trained by the improved artificial bee colony algorithm. After optimising the function of the SVM method, the method classifies the online teaching information of various business English micro-class teaching; In the classified network information set, the targeted retrieval method of network teaching resources according to big data technology is adopted to obtain the teaching data with the highest similarity to the user's retrieval data by clustering and complete the targeted retrieval of micro-class teaching data. The experimental results show that the retrieval delay of the proposed method for micro-class teaching data retrieval of business English is less than 1s, the number of correct retrievals is relatively high, and there are few wrong retrieval phenomena.
Full Text:
PDFReferences
L. Ma, "An immersive context teaching method for college English based on artificial intelligence and machine learning in virtual reality technology," Mobile Information Systems, vol. 2021, pp. 1-7, 2021.
Y. Yin, "Microclassroom design based on English embedded grammar compensation teaching," Mathematical Problems in Engineering, vol. 2021, pp. 1-9, 2021.
E. S. Darowski, E. Helder, and N. D. Patson, "Explicit writing instruction in synthesis: Combining in-class discussion and an online tutorial," Teaching of Psychology, vol. 49, no. 1, pp. 57-63, 2022.
S. Wang et al., "Research on PBL teaching of immunology based on network teaching platform," Procedia Computer Science, vol. 183, pp. 750-753, 2021.
H. Zhao and L. Guo, "Design of intelligent computer aided network teaching system based on web," Comput Des Appl, vol. 19, pp. 12-23, 2021.
T. Jiao, "Mobile English teaching information service platform based on edge computing," Mobile Information Systems, vol. 2021, pp. 1-10, 2021.
H. Chen and J. Huang, "Research and application of the interactive English online teaching system based on the internet of things," scientific programming, vol. 2021, pp. 1-10, 2021.
Y. Wang, H. Wang, J. Yang, and J. Chen, "Cross-model retrieval with deep learning for business application," 2021, vol. 1802: IOP Publishing, 3 ed., p. 032035.
F. Ma, T. Sun, L. Liu, and H. Jing, "Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network," Future Generation Computer Systems, vol. 111, pp. 17-26, 2020.
X. Xu, J. Tian, K. Lin, H. Lu, J. Shao, and H. T. Shen, "Zero-shot cross-modal retrieval by assembling autoencoder and generative adversarial network," ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 17, no. 1s, pp. 1-17, 2021.
X. Cheng and K. Liu, "Application of multimedia networks in business English teaching in vocational college," Journal of Healthcare Engineering, vol. 2021, 2021.
D. Jing and X. Jiang, "Optimization of computer-aided English teaching system realized by VB software," Computer-Aided Design and Applications, vol. 19, no. S1, pp. 139-150, 2021.
Y. Shu, "Experimental data analysis of college English teaching based on computer multimedia technology," Computer-Aided Design and Applications, vol. 17, no. S2, pp. 46-56, 2020.
L. Wang and Z. Xu, "An English learning system based on mobile edge computing constructs a wireless distance teaching environment," Mobile Information Systems, vol. 2021, pp. 1-9, 2021.
D. Amalia, I. Igaamoka, V. Septiani, and M. R. Fazal, "Designing of Mikrokontroler E-Learning Course: Using Arduino and TinkerCad," Journal of Airport Engineering Technology (JAET), vol. 1, no. 1, pp. 8-14, 2020.
R. M. Baker, M. E. Leonard, and B. H. Milosavljevic, "The sudden switch to online teaching of an upper-level experimental physical chemistry course: challenges and solutions," Journal of Chemical Education, vol. 97, no. 9, pp. 3097-3101, 2020.
L. Liu, "Research on IT English flipped classroom teaching model based on SPOC," Scientific Programming, vol. 2021, pp. 1-9, 2021.
D. A. Wild, A. Yeung, M. Loedolff, and D. Spagnoli, "Lessons learned by converting a first-year physical chemistry unit into an online course in 2 weeks," Journal of chemical education, vol. 97, no. 9, pp. 2389-2392, 2020.
F.-y. Yang, "Design of Traditional Teaching Method of Micro-teaching Based on Blended Learning," 2020: Springer, pp. 159-170.
F. Zhao, O. I. Fashola, T. I. Olarewaju, and I. Onwumere, "Smart city research: A holistic and state-of-the-art literature review," Cities, vol. 119, p. 103406, 2021.
A. Al-Hasan, "Effects of social network information on online language learning performance: A cross-continental experiment," in Research Anthology on Applying Social Networking Strategies to Classrooms and Libraries: IGI Global, 2023, pp. 1574-1591.
J. Sun, L. Wang, J. Li, F. Li, J. Li, and H. Lu, "Online oil debris monitoring of rotating machinery: A detailed review of more than three decades," Mechanical Systems and Signal Processing, vol. 149, p. 107341, 2021.
Z. Xiao et al., "Big data driven vessel trajectory and navigating state prediction with adaptive learning, motion modeling and particle filtering techniques," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3696-3709, 2020.
W. Zhang and Z. Wu, "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, vol. 41, no. 3, pp. 615-632, 2022.
M. Sehad and S. Ameur, "A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data," Advances in Space Research, vol. 65, no. 4, pp. 1250-1262, 2020.
X. Yu and H. Wang, "Support vector machine classification model for color fastness to ironing of vat dyes," Textile Research Journal, vol. 91, no. 15-16, pp. 1889-1899, 2021.
DOI: https://doi.org/10.31449/inf.v48i6.5450
This work is licensed under a Creative Commons Attribution 3.0 License.