Abstract
At the moment, only students’ academic achievements or questionnaire statistics are used to assess teaching quality. Its accuracy and efficiency are limited when applied to online evaluation of teaching quality. To address the aforementioned issues, this work investigates and develops an online teaching quality categorization evaluation model based on a mobile terminal. The crawler crawls the necessary data after collecting the evaluation of teaching excellence data on the mobile terminal. The online teaching quality categorization and assessment dimension is built based on data climbing, allowing for multi-dimensional teaching quality evaluation. On this basis, the teaching quality classification and evaluation index system is constructed. An adaptive variant genetic algorithm was used to improve the BP neural network and establish a classification and model for assessing teaching quality. The model test results show that the average evaluation accuracy of the model is 88.16%, and the model has good evaluation efficiency and stability.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Han, L., Lin, Q. (2023). Classified Evaluation Model of Online Teaching Quality in Colleges and Universities Based on Mobile Terminal. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-28867-8_24
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DOI: https://doi.org/10.1007/978-3-031-28867-8_24
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