International Journal of Educational Innovation and Science, 2021, 2(4); doi: 10.38007/IJEIS.2021.020402.
Huan Liu
Nanchang Institute of Science and Technology, Jiangxi 330108, China
College English is one of the basic core courses for college students in China. This course involves many difficult concepts and grammatical structures, which is very difficult for most students. The starting point of any teaching plan is to determine whether teaching is needed to specify what teaching should accomplish. Therefore, the needs of students are the key to the success of College English teaching. The traditional college English teaching does not consider the students' individual learning ability and feedback, which will lead to the students' lack of key knowledge and make them lose interest in English. This paper discusses the cognitive and motivational effects of animation teaching agent and alternative delivery system (simple flashing arrow with audio) when college English level students learn English relative clauses in big data and multimedia environment. This study also examined the cognitive efficiency of the two media systems in English grammar teaching. Therefore, the platform can improve students' autonomous learning ability and English ability. The results show that: in 2020, 601 papers with "artificial intelligence" and 526 papers with "English Teaching" as keywords will be published the most.
Big Data, College English, Teaching Reform, Autonomous Learning
Huan Liu. Path of College English Teaching Reform under the Background of Big Data. International Journal of Educational Innovation and Science (2021), Vol. 2, Issue 4: 8-14. https://doi.org/10.38007/IJEIS.2021.020402.
[1] Li W. Role of Machine Learning and Artificial Intelligence Algorithms for Teaching Reform of Linguistics. Journal of Intelligent and Fuzzy Systems, 2021, 40(2):3251-3262. https://doi.org/10. 3233/JIFS-189365
[2] A Brief Analysis on the Course Reform of Automation Major under the Development Trend of Artificial Intelligence. Advances in Education, 2020, 10(4):612-616. https://doi.org/10.12677/ AE.2020.104103
[3] Haisheng C. A Study on the Reform of College English Teaching Model Based on Factor Analysis. Agro Food Industry Hi Tech, 2017, 28(1):823-827.
[4] Ma W, Zhao X, Guo Y. Improving the Effectiveness of Traditional Education Based on Computer Artificial Intelligence and Neural Network System. Journal of Intelligent and Fuzzy Systems, 2021, 40(2):2565-2575. https://doi.org/10.3233/JIFS-189249
[5] Shuli, Zou. Designing and Practice of a College English Teaching Platform Based on Artificial Intelligence. Journal of Computational and Theoretical Nanoscience, 2017, 14(1): 104-108. https://doi.org/10.1166/jctn.2017.6133
[6] Juan D, Wei Y H. Particle Swarm Optimization Neural Network for Research on Artificial Intelligence College English Classroom Teaching Framework. Journal of Intelligent and Fuzzy Systems, 2020(4):1-13.
[7] Lu X. An Empirical Study on the Artificial Intelligence Writing Evaluation System in China CET. Big Data, 2019, 7(2):121-129. https://doi.org/10.1089/big.2018.0151
[8] Li X. The construction of intelligent English teaching model based on artificial intelligence. International Journal of Emerging Technologies in Learning (iJET), 2017, 12(12):35-44. https://doi.org/10.3991/ijet.v12i12.7963
[9] Hailong L. Role of Artificial Intelligence Algorithm for Taekwondo Teaching Effect Evaluation Model. Journal of Intelligent and Fuzzy Systems, 2021, 40(2): 3239-3250. https://doi.org/ 10.3233/JIFS-189364
[10] Ran D, Yingli W, Haoxin Q. Artificial Intelligence Speech Recognition Model for Correcting Spoken English teaching. Journal of Intelligent and Fuzzy Systems, 2020, 40(1):1-12. https://doi.org/10.3233/JIFS-189388