Research on Teaching Management Methods and Approaches of Chinese-Foreign Cooperation in Running Schools in Colleges and Universities under the Background of Big Data

Several Chinese universities are setting up systems to anticipate students’ academic performance and to offer them academic early notifications to boost their students’ performance effectively. Chinese-Foreign Cooperation in Running Schools (CFCRS) is one of China’s fastest-growing sectors of education. The academic achievement of CFCRS students is lower than that of students in noncooperatively organized programs. A realistic and timely academic prediction is critical to identifying students at risk of academic failure and providing them with the support they demand. A unique CNN-BiLSTM-AM approach will be used in this study to forecast the academic achievement of CFCRS learners more accurately and efficiently. Convolutional neural networks (CNN), bidirectional long-short-term memory (Bi-LSTM), and attention mechanism (AM) form the basis of this technique. The characteristics of the input information are extracted using a CNN. For good forecasting accuracy, AM is employed to obtain the contribution of characteristic levels on student results in various teaching methods. Initially, the Chinese students’ datasets are gathered from the big data for this investigation and are partitioned into 4 different groups. Four teaching methods are provided in groups. The proposed approach is used to forecast the performance of the students. Finally, the performance of the proposed approach is examined and compared with certain existing approaches to obtain the proposed approach with maximum correctness. The findings of this research are indicated in chart formations by employing the Origin tool.