Design of a Big Data-based Evaluation System for Teacher-Student Cooperation in College English Writing


  • Jia Li Xi'an Fanyi University
  • Jinpeng Zhang Xi'an Fanyi University



big data, BP neural network, college physical test, performance prediction


INTRODUCTION: With the constant change in information technology, people have entered the era of big data, and the reforms brought about by the era of big data have profoundly affected people's way of life and learning. English writing is one of the four basic skills for mastering English, and the skilful mastery of writing is an essential form of accurate expression in English. Different from the traditional teaching modes of "separate assessment of teaching" and "separate assessment of learning", "cooperative assessment between teachers and students" is a new type of assessment mode proposed by professors at the National University in recent years. This cooperative evaluation model advocates the combination of the teaching and evaluation processes and the evaluation and learning processes. In the cooperative evaluation model, evaluation is considered an extension of the teaching process, and the evaluation process itself is also a school process. The arrival of the significant data era also brings new opportunities and challenges for university English teacher-student cooperative evaluation.

OBJECTIVES: We design a university English writing teacher-student cooperative evaluation system based on big data by combining big data with the teacher-student cooperative evaluation.

METHODS: All college English writing classes in one university were selected as the background for ample data research, and with the help of theoretical knowledge and technology related to big data, a model was constructed to analyze the factors affecting the cooperative evaluation system of college English writing teachers and students.

RESULTS: The university English writing teacher-student cooperation evaluation system is analyzed and summarized using big data.

CONCLUSION: By using big data to analyze the evaluation of college English writing teacher-student cooperation, big data can better help teachers understand the weaknesses of students' knowledge points and better help students improve their English proficiency.


Agasisti, T., Celma, D. O. R., & Montemor, D. S.. The efficiency of Brazilian elementary public schools. International Journal of Educational Development, (2022).93(2), 256–260.

Andreis, L., Knig, W., & Patterson, R. I. A. A largeヾeviations principle for all the cluster sizes of a sparse Erds–Rényi graph. Random Structures and Algorithms, (2021). 2(3), 570-.

Carter, M., & Egliston, B. What are the risks of Virtual Reality data? Learning Analytics, Algorithmic Bias and a Fantasy of Perfect Data: New Media & Society, (2023). 25(3), 485–504.

Conn, K. M., Lovison, V. S., & Hyunjung, M. C. How Teaching in Underserved Schools Affects Beliefs about Education Inequality and Reform. Public Opinion Quarterly, (2022). 5(1), 1.

Felix, K. (2021). Enhancing EFL students’ participation through translanguaging. ELT Journal, 9(10), 12-.

Giesler, T. Much ado about nothing? The impact of the Reform Movement on (northern) German English language teaching. Language and History, (2021). 2(3), 23-.

Giffen, B. V., Herhausen, D., Fahse, T., & Woodside, A. G.. Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, (2022) 144(4), 57-.

Guan, K., Matsushima, K., Noguchi, Y., & Yamada, T. Topology optimization for rarefied gas flow problems using density method and adjoint IP-DSMC. Journal of Computational Physics, (2023). 5(11), 444-.

Hamlin, D.. Can a Positive School Climate Promote Student Attendance? Evidence From New York City: American Educational Research Journal, (2021) 58(2), 315–342.

Hilali, T. S. A., & Mckinley, J. Exploring the socio-contextual nature of workplace writing: Towards preparing learners for the complexities of English L2 writing in the workplace. English for Specific Purposes, (2021). 63(3), 86–97.

Krasulina, E. G. On implementation of some systems of elementary conjunctions in the class of separating contact circuits. Discrete Mathematics and Applications, (2023). 33(1), 19–29.

Kumar, R., Nayak, S., Garbrecht, M., Bhatia, V., & Saha, B. Clustering of oxygen point defects in transition metal nitrides. Journal of Applied Physics, (2021). 129(5), 055305.

Liang, Q., Torre, J. De. La., & Law, N. Do Background Characteristics Matter in Children’s Mastery of Digital Literacy? A Cognitive Diagnosis Model Analysis. Computers in Human Behavior, (2021). 3(6), 106850.

Lin, Y., Xu, B., Feng, J., Lin, H., & Xu, K. Knowledge-enhanced recommendation using item embedding and path attention. Knowledge-Based Systems, (2021).233(3), 107484-.

Meziane, R., FaridBenhadid, MostefaNiar, AbdelatifMamache, BakirMeziane, Toufik. Comparative evaluation of two methods of pregnancy diagnosis in dairy cattle in the East of Algeria: Proteins associated with pregnancy and ultrasonography. Biological Rhythm Research(2021)., 52(1a2), 34-.

Patsy, D. P., Jonas, B., & Vera, M. 274Early adversity scale for schizophrenia (EAS-SZ) constructed and validated using linked register data. International Journal of Epidemiology, (2021). 9(Supplement_1), Supplement_1.

Shivakumar, N., Chandrashekar, A., Handa, A. I., & Lee, R. Use of deep learning for detecting, characterizing and predicting metastatic disease from computerized tomography: A systematic review. Postgraduate Medical Journal, (2021) postgradmedj-2020-139620.

Su, N., & Zhang, F. Anomalous overland flow on hillslopes: A fractional kinematic wave model, its solutions and verification with data from laboratory observations. Journal of Hydrology, (2022). 604, 127202-.

Yiran, L. Evaluation of students’ IELTS writing ability based on machine learning and neural network algorithm. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, (2021). 4(4), 40.

Yurkofsky, M. Environmental, Technical, and Representational Uncertainty: A Framework for Making Sense of the Hidden Complexity of Educational Change: Educational Researcher, (2022). 51(6), 399–410.




How to Cite

Li J, Zhang J. Design of a Big Data-based Evaluation System for Teacher-Student Cooperation in College English Writing. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 4 [cited 2024 May 25];10(6). Available from: