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Research on the Application of Multimedia and Visual Image Technology in English Teaching Cases Under the Background of Network Age

Jing Sun

Journal of Cases on Information Technology2026https://doi.org/10.4018/jcit.399716article
ABDC C
Weight
0.50

What the paper says

Although multimedia technology has been widely used in language teaching, there are many visual quality problems in teaching resources, such as blurred images, compression distortion, and unbalanced brightness contrast, which seriously affect students' cognitive efficiency and learning experience. However, the existing practice lacks a systematic technical quality control mechanism. The purpose of this study is to design and verify a closed-loop optimization framework driven by information technology, so as to solve the core problem of unstable quality of multimedia resources in middle school English teaching, and explore the intelligent upgrading path of teaching media from available to high quality through algorithms and data. A one-year educational technology intervention case was put forward and implemented. The experimental results show that the students' grades and satisfaction with the optimized resources are significantly improved. This case provides a replicable and quantifiable optimization paradigm for technology empowerment teaching in the field of educational technology.

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https://doi.org/https://doi.org/10.4018/jcit.399716

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@article{jing2026,
  title        = {{Research on the Application of Multimedia and Visual Image Technology in English Teaching Cases Under the Background of Network Age}},
  author       = {Jing Sun},
  journal      = {Journal of Cases on Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/jcit.399716},
}

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Research on the Application of Multimedia and Visual Image Technology in English Teaching Cases Under the Background of Network Age

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.