|Table of Contents|

Regression Model Research on Posting Quality Evaluationin Online Learning Community(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2020年01期
Page:
33-41
Research Field:
信息与通信工程
Publishing date:

Info

Title:
Regression Model Research on Posting Quality Evaluationin Online Learning Community
Author(s):
Liu Jinjing; Wang Liying
School of Education Science,Nanjing Normal University,Nanjing 210023,China
Keywords:
machine learning; text quality evaluation; conceptual relationship diagram; online learning community; open topic
PACS:
G434
DOI:
10.3969/j.issn.1672-1292.2020.01.006
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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Last Update: 2020-03-15