基于课程成绩分析的高校学生评教结果识别与应用  

Recognition and Application of Teaching Evaluation Results of College Students Based on Curriculum Achievement Analysis

作　　者：马朝珉 李伟凯 袁晓东[1] 孟军[1] 吴秋峰[1] MA Zhaomin;Li Weikai;YUAN Xiaodong

机构地区：[1]东北农业大学,哈尔滨150030

出　　处：《高教学刊》2023年第28期14-18,22,共6页Journal of Higher Education

基　　金：全国教育科学“十四五”规划2022年度课题“学术型研究生学术志趣:测量工具、影响因素与提升路径研究”(BIA220099);教育部新农科研究与改革实践项目“高等农林院校教学质量监控体系改革与实践”(教高厅函〔2020〕20号);黑龙江省教育科学“十四五”规划2021年度重点课题“大数据背景下高校学生评教数据自动聚类研究”(GJB1421223)。

摘　　要：该文以某高校思政类必修课程的学生评教结果为研究样本,从评教分数(封闭式问题)和意见建议(开放式问题)两部分出发,分析课程成绩与评教结果的关系,识别评教结果的有效性,探索高校学生评教结果应用路径。研究发现,学生课程成绩与评教分数整体数据呈现不相关,个别课程出现负相关;在大多数情况下,课程成绩为“中”(70分≤中<80分)的学生评教分数有效性最高;大一年级课程成绩中等以上(≥70分)的学生评教分数有效性高于其他年级。运用K-means聚类算法对评教数据进行聚类分析,将学生评教样本分为高满意高收获型、高满意低收获型、低满意高收获型和低满意低收获型四个类别。在课程成绩分析基础上,提出高校学生评教结果合理应用的建议。Using the students'teaching evaluation results of ideological and political compulsory courses in a university as a research sample,the article analyzes the relationship between course scores and evaluation results,identifies the effectiveness of evaluation results,and explores its application path from two parts-closed question and open questions.The study found that the overall data of student course scores and evaluation scores are not correlated,and individual courses are negatively correlated;in most cases,students whose course scores are"medium"(70 points≤medium<80 points)have the highest effectiveness in evaluation scores;the effectiveness of evaluation scores for students with average grades(≥70 points)in the freshman year is higher than other grades.The article uses K-means clustering algorithm to cluster the teaching evaluation data,and the students'evaluation samples are divided into four categories:high satisfaction-high yield,high satisfaction-low yield,low satisfaction-high yield,and low satisfaction-low yield.Finally,the article puts forward some suggestions on the reasonable application of university students'teaching evaluation results.

关 键 词：高校学生评教 课程成绩 评教结果识别与应用 思政类必修课程 K-MEANS聚类算法 

分 类 号：G642[文化科学—高等教育学]