Abstract
With the continuous development of network technology and the popularization of information technology, the limitations of traditional education are becoming more and more prominent, while online education has become more and more popular and become a powerful means of personalized education. This article researches and analyzes the development situation of the CAI (Computer Aided Instruction) system at home and abroad, collects the current basic CAI system, focuses on the basic technologies adopted by these systems, determines the introduction of the MVC framework function, Starting from the reality, this paper discusses the feasibility of the system development, deeply studies the system requirements and functional requirements, further understands the theoretical basis and technical requirements required by the system, and determines the overall structure of the intelligent teaching assisted MOOC system. In depth analysis of intelligent teaching assistant MOOC system modules, analysis of system use cases, architecture system functions and technology, determine the function of each module of the system, establish a database management system, introduce the teaching quality evaluation system based on BP neural network, and realize the system design and test. The experimental results show that 90% of the students are interested in the ICAI MOOC system, and 86% of the students are satisfied with the teaching video of the teaching resource management part. In the system performance test, when the number of concurrent users reaches 1000, the system response time is less than 30 ms. The above results prove that the ICAI MOOC system proposed in this paper is helpful for English teaching. After the deployment of the system, teaching satisfaction has been greatly improved, and it has been well received by teachers and students.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bin T, Weiwei Y (2017) Research on the application of computer assisted instruction in the teaching of physical education in colleges and universities. Agro Food Ind Hi Tech 28(1):1533–1537
Chen Y, Zha W (2015) Prediction of the traffic volume of henan province with a bp neural network. Jt Bone Spine Rev Du Rhum 81(28):542–542
Duren-Winfield V, Onsomu EO, Case DL (2015) Health literacy and computer-assisted instruction: usability and patient preference. J Health Commun 20(4):491–498
Esteva A, Kuprel B, Novoa RA (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118
Hiramatsu A, Ikkai Y, Ohkawa T (2015) An explanation of a composition method for client server system configuration design based on significant judgment. Electr Eng Jpn 123(3):19–27
Huang CY (2019) Integrated curriculum of multi-tier client/server web-based database applications. Int J Inf Educ Technol 9(5):318–323
Jain AK, Mao J, Mohiuddin KM (2016) Artificial neural networks: a tutorial. Computer 29(3):31–44
Liu C, Fan P, Wang H (2017) Modeling forest fire risk assessment based on BP neural network of transmission line. Power Syst Prot Control 45(17):100–105
Mathai S (2018) An efficient electrochemical approach for characterization of the interface strength of nano titania-silica (TS) composite coating on titanium implants. Orient J Chem 34(4):2180–2186
Nieder GL, Shi P, Jiang P, Hu J, Xiao Z (2017) Application of bp neural network algorithm in traditional hydrological model for flood forecasting. Water 9(1):48
Root JR, Stevenson BS, Davis LL (2017) Establishing computer-assisted instruction to teach academics to students with autism as an evidence-based practice. J Autism Dev Disord 47(2):1–10
Rugivan S, Louise G (2019) In human nephrectomy specimens, the kidney level of tubular transport proteins does not correlate with their abundance in urinary extracellular vesicles. Am J Physiol Renal Physiol 317(3):F560–F571
Schmidt E (2015) Project grow: practical computer-assisted instruction. J SMPTE 76(9):895–897
Tajbakhsh N, Shin JY, Gurudu SR (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312
Wang Z, Chen Z, Zhu Y (2020) Multi-matrices entropy discriminant ensemble learning for imbalanced problem. Neural Comput Appl 32:8245–8264
Wu L, Yang Y, Maheshwari M (2019) Parameter optimization for FPSO design using an improved FOA and IFOA-BP neural network. Ocean Eng 175:50–61
Ying L (2016) Effects of matching multiple memory strategies with computer-assisted instruction on students’ statistics learning achievement. Eurasia J Math Sci Technol Educ 12(12):2921–2931
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Contributions
XZ: manuscript, Material preparation, data collection and analysis.
Corresponding author
Ethics declarations
Conflict of interest
The author(s) declared no potential conflicts of interest with respect to the research, author- ship, and/or publication of this article.
Ethical approval
This article does not contain any studies with animals performed by any of the authors. This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhao, X. Implementation of English ICAI MOOC system based on BP neural network. J Ambient Intell Human Comput 14, 3177–3186 (2023). https://doi.org/10.1007/s12652-021-03446-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03446-9