Narrow Results

The innovation and optimization of college English teaching based on a simulated neural network algorithm are studied in detail. A neural network algorithm for optimizing English teaching is constructed. Based on cognitive process simulation, the characteristics of current English teaching are applied to English teaching, and the influence of error on sentence analysis is eliminated. In the process, the latest network algorithm is used to calculate the detailed data of college English education. The convergence rate of the neural network algorithm is tested and analyzed, and the basic algorithm is identified in the changing trend of local problem test data. Experiments show that the data obtained based on the simulated neural network algorithm are more efficient. Traditional prediction models or machine learning methods include linear regression, decision trees, random forests, support vector machines (SVM), naive Bayesian classifiers, etc. These models have been used in the past for prediction or classification tasks in the field of education. However, the prediction model based on the simulated neural network algorithm proposed in this paper shows higher performance in accuracy, stability, and reliability, and significantly reduces interlayer errors.

To explore the realm of English education through contemporary advancements in artificial intelligence (AI), language education methods have developed significantly traditional approaches that usually have trouble with individualized learning needs and cannot handle diverse proficiency levels. This paper reveals the most prominent problem of inefficiency and non-personalized learning experiences in college English as a significant concern in the existing literature. In response, it suggests a novel method called Artificial Intelligence based English Teaching Method (AI-ETM). It has linked intelligent algorithms that can dynamically adapt teaching strategies and learning materials according to individual student’s needs, rate of comprehension and proficiency levels. The main aim of AI-ETM is to integrate AI technologies with hybrid particle swarm optimization (PSO) into English language teaching to improve effectiveness and efficiency and provide personalized learning experiences for students. This paper expects several outcomes from the implementation of AI-ETM. Among these is enhanced student engagement and motivation due to personalized learning experiences customized according to personal preferences and level of competence. Additionally, AI-ETM is expected to improve learning outcomes by offering targeted feedback and adaptive learning resources. This paper suggests that once AI-ETM is introduced, efficiency in delivering language instruction services will improve, thereby optimizing the allocation of educational resources.

As digital technologies advance, the need for interactive teaching methodologies in higher education has become crucial, particularly in the context of English language learning. This study explores innovative approaches utilizing deep learning and virtual reality to enhance student engagement and learning outcomes in college environments. The primary aim is to investigate the effectiveness of an Intelligent Coral Reef Optimization-driven Redefined Long Short-Term Memory (ICR-RLSTM) model, designed to facilitate a more dynamic and personalized learning experience in college English courses. The dataset comprises diverse English language materials, including texts, audio and interactive exercises, sourced from various educational platforms. Data pre-processing involves standardization techniques and the application of Natural Language Processing (NLP) algorithms to ensure the information is clean and structured for optimal analysis. The proposed interactive teaching model integrates gamification elements and virtual environments to provide students with immersive learning experiences. The ICR-RLSTM algorithm enhances predictive capabilities by optimizing long-term memory retention through intelligent coral reef optimization techniques, adapting to individual learner progress. Preliminary results indicate that the proposed model significantly improves student engagement and knowledge retention compared to traditional methods. The findings suggest that integrating deep learning and virtual reality into English language teaching can create adaptive and effective learning environments. This research contributes valuable insights into enhancing interactive teaching strategies and informs future educational practices.

The evaluation system of education effect is an important part of the whole teaching process, and the establishment of the evaluation system of college English teaching effect is an important work to test the effect of college English teaching. The traditional evaluation model is widely used and cannot be applied to a variety of teaching situations. Therefore, this paper proposes an evaluation model of college English education effect based on big data analysis. This paper determines the selection principle of the evaluation index of college English education effect, and on this basis, selects the evaluation index factors of college English education effect (experts, students and teachers), calculates the weight and membership matrix of the evaluation index, and outputs the comprehensive evaluation results of college English education effect, which realizes the construction of the evaluation model of college English education effect. The results show that: under the background of the experimental subjects (senior one and senior two), the evaluation errors of English education effect meet the needs of colleges and universities, which proves that the construction model is effective and feasible, and provides the basis and support for the reform of college English education. The range of assessment errors is between 0.78% and 1.44%, all consistent with the demands of the evaluation of the English education effect which demonstrates that the model is successful.