Using BERT-Based Textual Analysis to Design a Smarter Classroom Mode for Computer Teaching in Higher Education Institutions

Abstract

Smarter teaching has been widely popularized in computer teaching in higher education institutions as a key part of modern education. However, this practice faces some problems, such as excessive learning content, a tight teaching schedule, low learning enthusiasm among students, and limited time for practice. These shortcomings can be addressed by incorporating smarter teaching. A computer course in an engineering college was taken as an example in this study to construct a new mode for computer teaching based on deep learning theory, which includes five teaching stages, namely: introduction of new knowledge, pre-testing of knowledge, discussion of knowledge, task-oriented training, and post-testing of knowledge. An intelligent test database was constructed for computer teaching to be carried out under the guidance of the bidirectional encoder representation from transformers (BERT)-based textual analysis approach. Results show that (1) the test database constructed using the BERT-based textual analysis approach is more scientific and effective than other databases. (2) When validated on relevant teaching information and materials, the proposed approach improves students’ learning enthusiasm, problem-solving ability, and practicing capability. (3) The smarter teaching mode constructed based on deep learning theory can significantly improve the quality of course teaching and enhance students’ professional skills. The conclusions provide necessary technical support for the construction of computer-targeted test databases, which are conducive to pushing the reform and development of smarter teaching in computer science.