摘要
当前,关于在线课程质量的研究多是通过问卷调查数据的视角展开。而问卷调查模式存在样本量不足、样本缺乏代表性等问题。在在线环境下,平台上存在大量反映学习者对课程质量感知的评论数据。基于学习评论的在线课程质量分析模式能够有效克服问卷调查模式的缺陷。文章以中国大学MOOC等在线学习平台上学习者评论文本为研究数据,结合扎根理论和KANO模型,采用有序Logit回归方法对在线开放课程质量的影响因素进行了分析。结果表明:系统特性、视频制作、教师的教学水平、教学内容的有用性和教学内容的全面性属于必备型质量因素;课程辅助资料属于期望型质量因素;教师的教学风格和课程交互性属于魅力型质量因素。最后,针对不同类型的质量因素,提出相应的在线课程质量迭代改进策略以提升学习者的满意度。
The current research on the quality of online courses is mostly carried out from the perspective of questionnaire data. However, there are some problems in the questionnaire survey model, such as insufficient sample size and shortage of representative samples. In the online environment, there is a large amount of comment data reflecting learners’ perception of course quality on the platform. The online course quality analysis model based on learning review can overcome the defects of questionnaire survey model effectively. Based on the study data of learners’ comments on online learning platforms such as MOOC in Chinese universities, this paper analyzes the influencing factors of the quality of open online courses by using the ordered Logit regression method combined with grounded theory and KANO model. The results show that system characteristics, video production, teachers’ teaching level, the usefulness of teaching content and the comprehensiveness of teaching content belong to the essential quality factors. The auxiliary data belong to the expected quality factor. Teachers’ teaching style and course interaction belong to charismatic quality factors. Finally, according to different types of quality factors, the corresponding iterative improvement strategies of online course quality are proposed to improve learners’ satisfaction.
作者
王锐
王锦萱
罗仙平
WANG Rui;WANG Jin-xuan;LUO Xian-ping(Jiangxi University of Science and Technology,Ganzhou 341000,China)
出处
《黑龙江高教研究》
北大核心
2021年第10期155-160,共6页
Heilongjiang Researches on Higher Education
基金
江西省教改课题(2020)“基于学习评论挖掘的在线课程质量评价研究”(编号:JXJG-20-7-14)。
作者简介
王锐,江西理工大学经济管理学院副教授,博士后,研究方向:学习行为分析;通讯作者:王锦萱,江西理工大学经济管理学院,研究方向:MOOC教育;罗仙平,江西理工大学经济管理学院教授,博士研究生导师,研究方向:在线学习绩效评估。
