College English Smart Classroom Learning Model Utilizing Data Mining Technology

Abstract

The integration of Internet technology and the collaborative development of smart classrooms is an essential step for colleges and universities to advance English instruction reform. This study utilized data mining (DM) technology to analyze the learning process in college English smart classrooms. The results indicate that the DM algorithm used in this study outperforms the other two algorithms across all metrics. After conducting 15 experiments, the centrality of the DM algorithm in this study reached 0.58, exceeding the ant colony algorithm's centrality of 0.42. The decision tree algorithm exhibited the lowest centrality, reaching a maximum value of only 0.39. Consequently, the methodology utilized in this study demonstrates a significant centrality within the classroom, indicating its suitability for investigating University English smart classroom learning. Hence, implementing a University English smart classroom learning model utilizing DM technology represents the primary approach to achieving intelligent education.