Research on Knowledge Tracking in Foreign Language Teaching Based on Neural Network Computing

Authors

  • Hao Wu

Abstract

Knowledge tracking offers new opportunities for integrating intelligent assistance in foreign language teaching, greatly promoting foreign language teaching in terms of research generally to accurate tracking of student progress, and suggestions. When teaching a foreign language, it is essential that teachers track students’ knowledge acquisition scientifically and accurately. In order to establish and optimize the foreign language information assisted teaching environment, this paper first proposes the integration of big data in foreign language teaching, and improves the mutual adaptation of the teaching environment. Next, it strengthens the construction of teaching informatization, then conducts an in-depth exploration of knowledge measurement in foreign language teaching and the tracking of students’ knowledge level. Finally, the paper demonstrates that the proposed knowledge tracking model can improve students’ overall learning outcomes. By means of effective, multi-dimensional and multi-index analysis methods, the paper explores the main problems that currently challenge the teaching of a foreign language. Through the data collection, knowledge point coding, testing and analysis in the process, the neural network computing knowledge tracking model is designed to indicate the students’ recall of information, effectively track students’ level of knowledge mastery, and optimize and improve teaching practices. The knowledge tracking study of 352 students shows that it is feasible to divide knowledge points according to 15 semantics, 46 phrases, 63 grammars, 95 structures and 126 logical relationships. Data preprocessing ofknowledgetracking is necessary, asit determines the effectiveness andstability of knowledge tracking, and indicates that students’ knowledge level and forgetting level follow certain rules. The reasoning ability of the model is improved after integrating knowledge tracking and forgetting information. The effective application of the knowledge tracking model can improve teachers’ teaching efficacy and students’ autonomous learning ability. The proposed knowledge tracking model and path for foreign language teaching is a new method that strengthens the driving force of informatization and provides a new means of improving and fostering foreign language learning.


Keywords: image dehazing; neural network computing; information management; knowledge tracking; foreign language teaching; model design

Cite As

H. Wu, " Research on Knowledge Tracking in Foreign Language Teaching Based on Neural Network Computing",
Engineering Intelligent Systems, vol. 33 no. 2, pp. 189-200, 2025.




Downloads

Published

2025-03-01

Issue

Vol. 33 No. 2 (2025): International Journal of Engineering Intelligent Systems

Section

Article

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.