Student performance prediction is one of the most important subjects in educational data mining. As a modern technology, machine learning offers powerful capabilities in feature extraction and data modeling, providing essential support for diverse application scenarios, as evidenced by recent studies confirming its effectiveness in educational data mining. However, despite extensive prediction experiments, machine learning methods have not been effectively integrated into practical teaching strategies, hindering their application in modern education. In addition, massive features as input variables for machine learning algorithms often leads to information redundancy, which can negatively impact prediction accuracy. Therefore, how to effectively use machine learning methods to predict student performance and integrate the prediction results with actual teaching scenarios is a worthy research subject. To this end, this study integrates the results of machine learning-based student performance prediction with tiered instruction, aiming to enhance student outcomes in target course, which is significant for the application of educational data mining in contemporary teaching scenarios. Specifically, we collect original educational data and perform feature selection to reduce information redundancy. Then, the performance of five representative machine learning methods is analyzed and discussed with Random Forest showing the best performance. Furthermore, based on the results of the classification of students, tiered instruction is applied accordingly, and different teaching objectives and contents are set for all levels of students. The comparison of teaching outcomes between the control and experimental classes, along with the analysis of questionnaire results, demonstrates the effectiveness of the proposed framework.

翻译：学生表现预测是教育数据挖掘领域最重要的课题之一。作为一种现代技术，机器学习在特征提取和数据建模方面具有强大能力，为多样化的应用场景提供了关键支持，近期研究已证实其在教育数据挖掘中的有效性。然而，尽管已开展大量预测实验，机器学习方法尚未有效融入实际教学策略，阻碍了其在现代教育中的应用。此外，将海量特征作为机器学习算法的输入变量常导致信息冗余，可能对预测准确性产生负面影响。因此，如何有效利用机器学习方法预测学生表现，并将预测结果与实际教学场景相结合，是一个值得研究的课题。为此，本研究将基于机器学习的学生表现预测结果与分层教学相整合，旨在提升目标课程的学生学习成效，这对教育数据挖掘在当代教学场景中的应用具有重要意义。具体而言，我们收集原始教育数据并进行特征选择以降低信息冗余。随后，对五种代表性机器学习方法的性能进行分析与讨论，其中随机森林表现最佳。进一步地，基于学生分类结果实施分层教学，为不同层级学生设定差异化的教学目标与内容。通过对照班与实验班教学效果的比较，结合问卷调查结果的分析，验证了所提框架的有效性。