Abstract
The content of biochemistry course of nursing specialty is relatively abstract, which is difficult for students to understand and has low learning enthusiasm. In order to improve students’ learning enthusiasm and improve the overall teaching effect, an online teaching method of biochemistry course of nursing specialty based on association rules was studied. The problems existing in online teaching are summarized and analyzed. Based on the existing problems and on the basis of in-depth understanding of data mining theory, association rules algorithm is applied to online learning behavior analysis, online examination score analysis, teaching resource recommendation and teaching quality evaluation to mine the hidden and useful knowledge in teaching data and realize the effective combination of teaching and learning in online teaching. The test results show that under the application of this method, the accuracy of course recommendation is maintained at about 80%, which improves the overall teaching effect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Meilan, X., Zheng, Z., Li Mengyang, X., Hongwei, L.Y., Lin, H.: Exploration of network teaching model of biochemistry course in nursing specialty. Educ. Teach. Forum 43, 294–297 (2020)
Xiaodong, R., Linzhao, L., Yali, Z.: Discussion on online teaching practice of biochemistry course combined with ideological and political education. Guide Sci. Educ. 28, 123–124 (2020)
Moslehi, F., Haeri, A., MartĂnez-Lvarez, F.: A novel hybrid GA–PSO framework for mining quantitative association rules. Soft Comput. 24(6), 4645–4666 (2020)
Tjortjis, C.: Mining Association Rules from Code (MARC) to support legacy software management. Softw. Qual. J. 28(6), 633–662 (2020)
Wu, Y., Zhang, J.: Building the electronic evidence analysis model based on association rule mining and FP-growth algorithm. Soft. Comput. 24(2), 7925–7936 (2020)
Delgado, F.: Teaching physics for computer science students in higher education during the COVID-19 pandemic: a fully internet-supported course. Fut. Internet 13(2), 35 (2021)
Boll, S.C., Müller, H., Lunte, T., et al.: Making, together, alone: experiences from teaching a hardware-oriented course remotely. IEEE Pervas. Comput. 19(4), 35–41 (2020)
Sun, J.: Research on resource allocation of vocal music teaching system based on mobile edge computing. Comput. Commun. 160(2), 342–350 (2020)
Yu, X.: Resource scheduling for piano teaching system of internet of things based on mobile edge computing. Comput. Commun. 158(99), 73–84 (2020)
Xu, N., Fan, W.H.: Research on interactive augmented reality teaching system for numerical optimization teaching. Comput. Simul. 37(11), 203–206+298 (2020)
Fund Project
Anhui Province University Humanities and Social Sciences Research Key Project (SK2019A0937); School-level Scientific Research Project (WJH202010t); Quality Engineering: Production Education Integration Training Base Project (2021cjrh020).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Chen, J., Yang, K., Pang, Y., Shen, M., Liu, P., Lu, J. (2022). Online Teaching Method of Biochemistry Course of Nursing Specialty Based on Association Rules. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-031-21161-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-031-21161-4_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21160-7
Online ISBN: 978-3-031-21161-4
eBook Packages: Computer ScienceComputer Science (R0)