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This paper uses deep learning algorithms to detect and extract student behavioral features in the Civics classroom. It first predicts and detects the target location and behavior of students in classroom images by FastR-CNN algorithm, extracts the detected student data features by MSResNet-50 algorithm, and then introduces the attention mechanism to screen the extracted behavioral features, and analyzes the accurate location information and channel information to get the student’s behavioral performance in the Civics classroom. The Civics element framework is used to construct a digital transformation path for Civics courses in this study. In order to reflect the value of the digital transformation of Civics teaching, the study conducted a teaching comparison practice. After a semester’s test, the Civics scores of the students in the five classes were above 80 (good or excellent). In contrast, the mean score of Civic Literacy in the post-test reached 3.863, which was significantly different from that before the practice (p=0.007 |
