Abstract
In this paper, an in-depth study and analysis of college English prediction skills and reading efficiency are conducted using an optimized BP network algorithm, which is designed separately, and the accuracy of scoring results is verified and the experimental results are analyzed using two datasets; finally, under the guidance of writing feedback theory and data visualization design criteria, a reasonable visualizable writing feedback is created. The learning and training of the BP neural network model revealed that there was significant information loss in the similar nearest-neighbor user dataset used for training, and there were instances where some items were rated by the target user but not by similar nearest-neighbor users. As a result, such data is useless in training, and the neural network loses a significant amount of useful information when learning. To address this issue, this paper proposes using the singular value decomposition technique for filling, which alleviates the sparsity of the filled matrix data and improves the accuracy of recommendations even more. Both scoring models constructed using 1D CNN and LSTM networks belong to the second class of models, and this type of “end-to-end” scoring model does not require feature engineering. Finally, using 650 spoken recordings and their corresponding manual scoring data, the model is trained and tested. The experimental results show that, with a smaller training dataset, the BP network model achieves a better overall scoring performance.
Highlights
In today’s world, the rapid development of computer technology and network information technology provides multimodal means, platforms, and spaces for English teaching and creates more opportunities for mutual communication, prompting teachers and students to access more information resources and communication channels. e development of information technology has even promoted innovation and change in English teaching philosophy, teaching methods, and educational procedures [1]. erefore, English teaching and learning have undergone great changes in methodological paths, and multimodal teaching has become a general trend
As far as English reading teaching is concerned, computer technology and information network technology provide rich multimodal discourse resources that can help students integrate into situations and contexts and gain more opportunities for language learning and language use
Since the process of reading comprehension is an act of inner activity, teaching reading has always been a challenge in English language teaching [2]. e most significant benefit of search engine technology is that it is simple, straightforward, and quick
Summary
In today’s world, the rapid development of computer technology and network information technology provides multimodal means, platforms, and spaces for English teaching and creates more opportunities for mutual communication, prompting teachers and students to access more information resources and communication channels. e development of information technology has even promoted innovation and change in English teaching philosophy, teaching methods, and educational procedures [1]. erefore, English teaching and learning have undergone great changes in methodological paths, and multimodal teaching has become a general trend. English reading teaching is mainly to help students learn English phonetics, vocabulary, grammar, discourse, and Wireless Communications and Mobile Computing other knowledge through reading, accumulate English learning experience, develop cultural awareness, develop English reading habits, and master certain English reading skills to promote the formation of students’ comprehensive language use skills. E use of the BP neural network to replace the original Resnick formula is proposed to address this deficiency, and the process of constructing the neural network model is described It is discovered during the model-building process that the data of the similar nearest-neighbor user item-rating matrix is extremely sparse, resulting in the loss of a significant amount of useful information. To alleviate the data sparsity problem, this paper proposes using the SVD technique to fill the similar nearest-neighbor user rating-item matrix with data before building the BP neural network model
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