高校课程考试成绩正态分布辨析

Systemic Analysis of Normal Distribution for the Scores of Course Tests in Higher Education:A Case Study of English Reading Courses

目前我国高校在教学管理中普遍存在对学生课程考试成绩呈正态分布的要求。以高校英语阅读课程为例,在掌握学习的理论框架下,通过理论推理和考试数据分析发现,学校课程考试成绩呈明显偏态分布。把学习作为一个系统,对考试要素与其他要素之间的信息交互进行分析发现,正态分布下的考试要素信息呈现熵最大趋势,给整个学习系统提供混乱信息,课程难以形成负反馈调节机制,教学目标、学习目标和学习达成结果之间的匹配度被削弱,在教学决策和学习决策上均表现出信息熵增加,造成系统信息无序化。建议高校取消对课程考试的正态分布要求,分析具体课程的掌握达成分布规律,使用更准确的分布描述考试成绩。

At present,there is general expectation or requirement of normal distribution for the scores of course tests in higher education in China.This paper analyzes the college English reading course as a case under the theoretical framework of“Mastering Learning”.The results suggest that scores of the course test are approximately logarithmic normal distribution instead of normal distribution.On this basis,the study takes learning as a system to analyze the information interaction between test elements and other elements.It is found that the test element information under the normal distribution shows the largest entropy trend,providing chaotic information to the entire learning system.Negative feedback is hard to achieve in the curriculum adjustment mechanism,weakening the matching degree between the teaching goal,the learning goal and the learning achievement.Therefore,the information entropy is increased in both the teaching decision and the learning decision,causing the disorder of the system information.The study suggests reducing or canceling the expectation of the normal distribution of the course examination.Instead,the practitioners are suggested to process the examination score records more comprehensively by analyzing the mastery achievement distribution of a specific course,and using the statistical analysis of the achievement rate.