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.
With the continuous progress of artificial intelligence technology, mobile online education is developing rapidly. Compared with traditional classrooms, the mo-bile online education mode can realize sharing of high-quality education resources efficiently and at a low cost, thus the education informatization policy can be im-plemented. However, the college students’ willingness to use mobile online edu-cation platforms is complicated due to the lack of a market access threshold and relatively single course resources. Based on the theory of perceived value, the hypothesis of the influencing factors of the college students’ willingness to use mobile online education platforms was proposed, and the hypothesis with a ques-tionnaire survey method and structural equation model was verified. Results show that the questionnaire designed in this study has good reliability and validi-ty. The exploratory learning and good public praise of the network positively af-fects the college students’ perceived ease of use and perceived usefulness of mo-bile online education platforms. The perceived usefulness has a significant direct and positive effect on their willingness to use online education platforms. The conclusions have a certain reference value for improving the quality and service of online education platforms and enriching the connotation and usage of mobile online education.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.