成绩优良率概率分布研究
首发时间:2024-06-17
摘要:学院学生的学习状态往往以期末成绩为评判尺度,但考虑到每个专业涵盖多门学科,对每门学科进行详细评估的复杂性令人望而却步。为此,本研究以某一学院学生为例,采用学生成绩的优良率来评估学习成绩。首先,通过期望和方差分析揭示了优良率数据的统计规律;随后,使用最小二乘拟合将优良率的分布列与正态分布密度函数相匹配,得到近似的正态分布密度函数。为进一步将离散的优良率连续化并计算相应区间的概率,构建了二次密度函数曲线。将优良率模型的目标函数和约束函数导入金枪鱼群优化算法进行1000次迭代,得到了最优的二次密度函数。最后,根据最优二次密度函数列出了优良率的连续分布函数,该函数可方便地计算任意优良率区间的概率,从而更准确地描述学生的学习状态。这一模型简化了成绩评估过程,为学生提供了更深入和全面的学习状态分析手段。
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study on the probability distribution of excellent performance rates
abstract:the learning status of college students is often judged by their final grades, but considering that each major covers multiple disciplines, the complexity of conducting a detailed assessment for each subject is daunting. therefore, this study takes students from a certain college as an example and uses the excellence rate of student grades to evaluate their learning performance. firstly, the statistical patterns of the excellence rate data are revealed through expectation and variance analysis. then, the distribution of the excellence rate is matched with the normal distribution density function by using the least squares fitting method, resulting in an approximate normal distribution density function. to further make the discrete excellence rate continuous and calculate the probability of corresponding intervals, a quadratic density function curve is constructed. by introducing the objective and constraint functions of the excellence rate model into the particle swarm optimization algorithm, the optimal quadratic density function is obtained after 1000 iterations. finally, based on the optimal quadratic density function, the continuous distribution function of the excellence rate is derived, which can conveniently calculate the probability of any excellence rate interval, thereby providing a more accurate description of the students\' learning status. this model simplifies the process of grade evaluation and provides students with a more in-depth and comprehensive means of analyzing their learning status.
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