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<p>The trend of technology becoming more widely accepted in higher education is leading to the need for smart systems that can track and improve the efficiency of reading in English for college students. Traditional assessment techniques are slow to be adapted to the changing needs of the student and the process is time-consuming and not very effective in giving personalized feedback, as they rely mostly on grading by an assessor and being performed manually. This study presents a deep learning–monitoring system that is based on Monarch Butterfly Optimized Intelligent Capsule Networks (MBO-Int-CapsNet) to deliver an accurate assessment of reading efficiency and bolster it, as well. The uniqueness of the suggested tactic is the integration of the Monarch Butterfly Optimization (MBO) with the Capsule Networks (CapsNet) where the hyperparameters are automatically set and the routing of the dynamically increased traffic is made more efficient. Contrary to the standard deep learning approaches, MBO-Int-CapsNet ensures that the spatial and hierarchical relationships among the features are maintained, hence allowing the reading-related attributes to be captured with higher precision. While MBO is improving global search, avoiding local optima and speeding up convergence, the CapsNet structure is providing robustness against variations in orientation, position and scale of the speech features. The collection of spoken English recordings from the college-level learners is the dataset, and each of them is assessed by the experts of the field who give standardized proficiency scores depending on the criteria of pronunciation accuracy, fluency, intonation, and overall reading comprehension. Data preprocessing highlights the use of singular value decomposition (SVD) based matrix completion that eliminates the sparsity completely, and the extraction of Mel-frequency cepstral coefficients (MFCCs) that serve phonetic and prosodic pattern representation. The proposed MBO-Int-CapsNet model has shown its exceptional performance by generating minimum MAE and RMSE, achieving 95% to 98% accuracy, precision, recall, and F1-score, the highest R-square, and the least latency in milliseconds. The implementation is done in Python utilizing TensorFlow and Keras, and it also includes visualization modules for interactive feedback delivery. According to the results, MBO-Int-CapsNet is a robust and versatile solution for the implementation of intelligent assessment in language teaching that can easily scale and adapt.</p>

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Improving the deep learning monitoring system for CollegeEnglish reading efficiency guided by neural networks

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Xiaohui Bian,
  • Xuefeng Wang,
  • Jingxian Xu

摘要

The trend of technology becoming more widely accepted in higher education is leading to the need for smart systems that can track and improve the efficiency of reading in English for college students. Traditional assessment techniques are slow to be adapted to the changing needs of the student and the process is time-consuming and not very effective in giving personalized feedback, as they rely mostly on grading by an assessor and being performed manually. This study presents a deep learning–monitoring system that is based on Monarch Butterfly Optimized Intelligent Capsule Networks (MBO-Int-CapsNet) to deliver an accurate assessment of reading efficiency and bolster it, as well. The uniqueness of the suggested tactic is the integration of the Monarch Butterfly Optimization (MBO) with the Capsule Networks (CapsNet) where the hyperparameters are automatically set and the routing of the dynamically increased traffic is made more efficient. Contrary to the standard deep learning approaches, MBO-Int-CapsNet ensures that the spatial and hierarchical relationships among the features are maintained, hence allowing the reading-related attributes to be captured with higher precision. While MBO is improving global search, avoiding local optima and speeding up convergence, the CapsNet structure is providing robustness against variations in orientation, position and scale of the speech features. The collection of spoken English recordings from the college-level learners is the dataset, and each of them is assessed by the experts of the field who give standardized proficiency scores depending on the criteria of pronunciation accuracy, fluency, intonation, and overall reading comprehension. Data preprocessing highlights the use of singular value decomposition (SVD) based matrix completion that eliminates the sparsity completely, and the extraction of Mel-frequency cepstral coefficients (MFCCs) that serve phonetic and prosodic pattern representation. The proposed MBO-Int-CapsNet model has shown its exceptional performance by generating minimum MAE and RMSE, achieving 95% to 98% accuracy, precision, recall, and F1-score, the highest R-square, and the least latency in milliseconds. The implementation is done in Python utilizing TensorFlow and Keras, and it also includes visualization modules for interactive feedback delivery. According to the results, MBO-Int-CapsNet is a robust and versatile solution for the implementation of intelligent assessment in language teaching that can easily scale and adapt.