摘 要
本文研究了决策树算法在学生课程成绩分析中的应用。通过对学生的个人信息、兴趣爱好、学习情况等特征进行数据预处理和特征选择，构建决策树模型，可以预测学生在不同课程上的表现和成绩，为学生提供个性化的选课推荐、学习路径规划和学业风险预警。从而提高学生的学习效果和成绩。此外，决策树算法还可以用于教师的教学评估和课程改进。通过对教师的教学方法、教学内容等特征进行分析，可以评估教师的教学水平和课程质量，为学校提供教学改进的参考依据。同时，决策树算法还可以应用于学生的就业指导和职业规划。通过对学生的专业背景、实习经历等特征进行分析，可以为学生提供个性化的就业推荐和职业规划建议，帮助学生更好地实现自己的职业目标。总之，决策树算法在学生课程成绩分析中具有广泛的应用前景和实际意义。
关键词：决策树算法；学生课程成绩分析；职业目标
Research on the application of decision tree algorithm in the analysis of students' course achievement
英文姓名
Directive teacher:×××
Abstract：This paper studies the application of decision tree algorithm in the analysis of students' course achievement. Through data preprocessing and feature selection of students' personal information, interests, learning and other characteristics, a decision tree model is constructed to predict students' performance and achievement in different courses, and provide students with personalized course selection recommendation, learning path planning and academic risk warning. So as to improve the learning effect and achievement of students. In addition, decision tree algorithm can also be used for teachers' teaching evaluation and curriculum improvement. Through the analysis of the characteristics of teachers' teaching methods and teaching contents, the teaching level and curriculum quality of teachers can be evaluated, and the reference basis for teaching improvement can be provided for schools. At the same time, decision tree algorithm can also be applied to students' employment guidance and career planning. Through the analysis of students' professional background, internship experience and other characteristics, we can provide students with personalized employment recommendations and career planning suggestions to help students better achieve their career goals. In a word, decision tree algorithm has wide application prospect and practical significance in the analysis of students' course achievement.
Key words: Decision tree algorithm; Analysis of students' course performance; Career goal
目 录
1 引言 1
2 决策树算法概述 1
2.1 决策树算法的定义和基本原理 1
2.2 决策树算法的分类和特点 2
3 决策树算法在学生课程成绩中的分析 2
3.1 数据预处理和特征选择 2
3.2 决策树模型的构建和训练 3
3.3 决策树模型的评估和优化 4
4 决策树算法在学生课程成绩分析中的应用 4
4.1 学生选课推荐 4
4.2 学生学习路径规划 5
4.3 学生学业风险预警 6
结论 7
参考文献 7
致谢 8
