摘要
为了降低在网络教学中,由于学生自主学习行为的多样性和技术平台存在的差异性,导致传统评价方法难以准确给出评价反馈的问题,引入LMBP(Levenberg-Marquardt Back Propagation)算法,构建了一个能够利用权重化的评价指标对学生的学习表现进行量化分析的自动评价模型。确定网络教学在线学习的评价指标权重,筛选出关键评价指标,并合理分配权重值,降低数据的无序性。基于LMBP算法构建自动评价模型,通过模型的运算,自动计算出每个学生的在线学习评价分数,降低评价的滞后性,实现客观、准确的评价。实验结果显示,模型计算得到的各项指标权重值在0.96以上,拟合度高于0.98,评价分数高于97分,可以实现网络教学的有效评价。
In order to reduce the problem of traditional evaluation methods being difficult to provide accurate feedback due to the diversity of students'self-directed learning behaviors and differences in technology platforms in online teaching,the LMBP(Levenberg Marquardt Back Propagation)algorithm is introduced to construct an automatic evaluation model that can quantitatively analyze students'learning performance using weighted evaluation indicators.Determine the weight of evaluation indicators for online teaching and learning,screen out key evaluation indicators,and allocate weight values reasonably to reduce data disorder.Based on the LMBP algorithm,an automatic evaluation model is constructed to automatically calculate the online learning evaluation score of each student through the operation of the model,reducing the lag of evaluation and achieving objective and accurate evaluation.The experimental results show that the weight values of various indicators calculated by the model are above 0.96,the fitting degree is higher than 0.98,and the evaluation score is higher than 97 points,which can achieve effective evaluation of online teaching.
作者
曾光辉
ZENG Guanghui(Guangzhou Institute of Technology,Guangzhou 510900,China)
出处
《微处理机》
2025年第3期17-23,共7页
Microprocessors
基金
2024年度广东省普通高校特色创新类项目“基于深度学习的在线学习智能评价方法研究”(2024KTSCX318)
2024年度广州市高等教育教学质量与教学改革工程产教融合实训基地项目“新一代信息技术产教融合实训基地”(2024CJRHJD008)。
关键词
LMBP算法
网络教学
在线学习
在线学习评价
权重化
LMBP algorithm
Online teaching
Online learning
Online learning evaluation
Weightization
