Educational data mining is becoming a more and more popular research field in recent years, mainly with the help of cross research conducted by various disciplines, so as to solve various difficult problems in the teaching and education process. In this paper, we proposed a hybrid approach for student performance prediction. We collected the dataset, including 15 characteristics of students from three categories (individual basic information, individual education information, and individual behavior information). Based on the random forest (RF) and simulated annealing (SA) algorithms, we binary encode the relevant parameters (number of features, tree size, and tree decision weights) as the target variables for algorithm optimization, use the out-of-bag error as the optimization objective function, and then propose the IRFC (improved random forest classifier) algorithm in this paper. Compared with other mainstream improved random forest algorithms, the research results demonstrate that the proposed algorithm in this paper has higher generalization ability and smaller OOB error. This study provides a methodological reference for the prediction of student achievement and also makes a marginal contribution to student management work.

To address the problem that the traditional English composition is manually reviewed, which leads to low efficiency of grammar error checking and heavy teaching burden for teachers and seriously affects the quality of English teaching, a verb grammar error checking algorithm based on the combination of a multilayer rule model and a deep learning model is proposed with current multimedia technology. First, the basic principles and network structure of the long short-term memory network LSTM are specifically analyzed; then, a multilayer rule model is constructed; after that, an attention model-based LSTM verb grammar checker is designed, and a word embedding model is used to encode the text and map it to the status space to retain the textual information. Finally, the proposed algorithm is experimentally validated using the corpus dataset. The experimental results revealed that the accuracy, recall, and F1 values of the proposed algorithm in verb error detection were 45.51%, 28.77%, and 35.5%, respectively, which were higher than those of the traditional automatic review algorithm, indicating that the proposed algorithm has superior performance and can improve the accuracy and efficiency of grammar error detection.