[1]刘金晶,王丽英.在线学习社区发帖质量评价的回归模型研究[J].南京师范大学学报(工程技术版),2020,20(01):033-41.[doi:10.3969/j.issn.1672-1292.2020.01.006]
　Liu Jinjing,Wang Liying.Regression Model Research on Posting Quality Evaluationin Online Learning Community[J].Journal of Nanjing Normal University(Engineering and Technology),2020,20(01):033-41.[doi:10.3969/j.issn.1672-1292.2020.01.006]
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在线学习社区发帖质量评价的回归模型研究
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
20卷
期数:
2020年01期
页码:
033-41
栏目:
信息与通信工程
出版日期:
2020-03-15

文章信息/Info

Title:
Regression Model Research on Posting Quality Evaluationin Online Learning Community
文章编号:
1672-1292(2020)01-0033-09
作者:
刘金晶; 王丽英
南京师范大学教育科学学院,江苏 南京 210023
Author(s):
Liu Jinjing; Wang Liying
School of Education Science,Nanjing Normal University,Nanjing 210023,China
关键词:
机器学习; 文本质量评价; 概念关系图; 在线学习社区; 开放话题
Keywords:
machine learning; text quality evaluation; conceptual relationship diagram; online learning community; open topic
分类号:
G434
DOI:
10.3969/j.issn.1672-1292.2020.01.006
文献标志码:
A
摘要:
在线学习社区中,多样化教学情境下基于开放话题的发帖使学生能够阐述自我知识更新的进展,但也伴随着越来越高的阅读评价解析成本. 为此,可应用机器学习理论构建发帖质量评价回归模型来实现文本自动评价. 首先构建文本质量评价指标及其计算所依赖的概念关系图,然后结合专家评分标准与评分结果选用多种拟合回归算法对文本质量进行预测评价,最后以拟合优度、交叉验证精度方差和均方误差为指标评估算法效果模型,测试以倡导知识建构学习理念的“数课”平台《网络安全与维护》课程的575条发帖为数据集,实现了网络安全领域的概念关系图存取和发帖质量的特征提取与预测评价. 实验表明,梯度树上升回归算法的准确性、稳定性均优于其他算法模型. 该回归模型能够从5个与文本质量显著相关的特征维度,即可读性、相关度、内聚度、专业度和探究度,有效地实现文本质量自动评价,从而为教师减负和学生自我诊断提供帮助.
Abstract:
In the online learning community,postings based on open topics in a variety of teaching contexts enable students to articulate the progress of self-knowledge updat, but with the increasing costs of reading, evaluating and analyzing these posts. To alleviate it,the machine learning theory is applied to construct a regression model for posting quality evaluation. Firstly,the model constructs five dimensions of text quality evaluation and the conceptual relationship diagram on which the calculation depends. Secondly,combined with the expert scoring standard and the scoring result,the model uses multiple fitting regression algorithms to predict and evaluate the text quality. Finally,the algorithm is evaluated by coefficient of determination,cross-validation accuracy and mean square error. This paper takes 575 posts of the“Shu Ke”platform which advocates knowledge construction from“Network Security and Maintenance”course as a dataset. The model can realize the feature extraction and predict the posting quality evaluation by means of conceptual relationship diagram. The experimental results show that the accuracy and stability of the gradient decision boosting tree regression algorithm are better than those of other algorithm models. The regression model can effectively realize the automatic evaluation of text quality based on five characteristic dimensions that are significantly related to text quality:readability,relevance,cohesion,professionalism and exploration,thus reducing the burden on teachers and helping students with self-diagnosis.

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备注/Memo

备注/Memo:
收稿日期:2019-05-26.
基金项目:全国教育科学“十三五”规划教育部重点课题(DIA170375).
通讯作者:王丽英,博士,讲师,研究方向:计算机图形学、信息技术. E-mail:wangliying@njnu.edu.cn
更新日期/Last Update: 2020-03-15