Evaluating the Learning Effectiveness of A Blended Teaching Model of College English Using A Decision Tree Algorithm

Abstract

The learning effectiveness of a blended teaching model of college English, utilizing a decision tree algorithm, demonstrates promising outcomes. This model combines traditional classroom instruction with online learning components, allowing for personalized and interactive learning experiences. By employing decision tree algorithms to analyze student data and performance metrics, instructors can tailor instructional strategies to individual learning styles and needs. This data-driven approach facilitates targeted interventions, adaptive feedback, and optimized course materials, ultimately enhancing students' English language proficiency and academic success. The blended teaching model harnesses the power of technology and data analytics to create dynamic and effective learning environments in college English education. \College English education. The HGBT-DT model combines genetic algorithms, blended teaching methods, and decision tree algorithms to tailor instructional strategies to individual student needs. Through a comprehensive study involving 100 students, we demonstrate the efficacy of the HGBT-DT model in enhancing language proficiency and critical thinking skills. Results indicate a significant improvement in student performance, with an average post-test score increase of 15 points compared to pre-test scores. Additionally, the HGBT-DT model achieves an optimization score of 0.90, indicating a high level of effectiveness in optimizing teaching strategies. Furthermore, decision tree analysis reveals personalized teaching recommendations, leading to improved student outcomes across diverse learner profiles. This study highlights the potential of the HGBT-DT model to revolutionize teaching practices and promote personalized learning experiences in College English education.