职业教育背景下基于机器学习的教学效果评估——从技能大赛角度分析  

Evaluation of Teaching Effectiveness Based on Machine Learning in the Context of Vocational Education—Analysis from the Perspective of Skill Competitions

作　　者：邵文昭 张书强[1] 牛英群[1] 王丽平[1] 杨彦龙 张飞鸿 

机构地区：[1]邯郸职业技术学院数字智能学院,河北 邯郸

出　　处：《教育进展》2024年第9期838-842,共5页Advances in Education

摘　　要：当前,在职业教育中,对课程的教学效果的评估指标存在主观性强、与职业技能关联性弱的缺陷。为了克服上述缺陷,本文以学生的课程成绩与职业院校技能大赛成绩(以下简称“竞赛成绩”)之间的关联性作为评价教学效果的依据。为了找到二者之间的关联性,本文使用了机器学习的决策树模型和统计学的相关性计算。本文以邯郸职业技术学院大数据技术2022级第一学年上学期的课程成绩和第一学年下学期的竞赛成绩作为实验对象。实验结果表明,在所有的课程当中,Python混合式教学课程成绩与竞赛成绩之间存在最强的正向关联性。为了进一步提升职业技能的培养效果,本文建议提高以Python混合式教学为代表的实践性强的专业课程在总课时当中所占比重。Currently, in vocational education, the evaluation indicators for the teaching effectiveness of courses have the shortcomings of strong subjectivity and weak correlation with vocational skills. In order to overcome the above shortcomings, this article uses the correlation between students’ course grades and vocational school skills competition scores (Hereinafter referred to as “competition results”) as the basis for evaluating teaching effectiveness. In order to find the correlation between the two, this article used machine learning decision tree models and statistical correlation calculations. This article takes the course grades of the first semester of the first year of 2022 and the competition grades of the second semester of the first year of Big Data Technology at Handan Polytechnic College as the experimental subjects. The experimental results indicate that among all courses, there is the strongest positive correlation between Python blended learning course scores and competition scores. In order to further enhance the effectiveness of vocational skills training, this article suggests increasing the proportion of practical professional courses represented

关 键 词：教学评估 机器学习 决策树 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]