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Design and validation of a diagnostic MOOC evaluation method combining AHP and text mining algorithms

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成果类型:
期刊论文
作者:
Nie, Yanjiao;Luo, Heng*;Sun, Di
通讯作者:
Luo, Heng
作者机构:
[Nie, Yanjiao; Luo, Heng] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Peoples R China.
[Sun, Di] Syracuse Univ, Dept Instruct Design Dev & Evaluat, Syracuse, NY USA.
通讯机构:
[Luo, Heng] C
Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Peoples R China.
语种:
英文
关键词:
Massive open online course;diagnostic evaluation;Analytic Hierarchy Process;emotion classification;quality assurance
期刊:
Interactive Learning Environments
ISSN:
1049-4820
年:
2021
卷:
29
期:
2
页码:
315-328
基金类别:
This work was supported by the Basic Research Funding of Central China Normal University (CCNU20DC006) and the SEIT e-Learning Research Grant of Central China Normal University (CCNUSEIT202001).
机构署名:
本校为第一且通讯机构
院系归属:
教育信息技术学院
摘要:
The proliferation of massive open online courses (MOOCs) highlights the necessity of developing accurate and diagnostic evaluation methods to assess the courses’ quality and effectiveness. Hence, this study proposes a diagnostic MOOC evaluation (DME) method that combines the Analytic Hierarchy Process algorithm and learner review mining to integrate expert opinions, standardized rubrics, and learner feedback into the evaluation process. For this purpose, 30 MOOCs from the Coursera website were purposively selected and evaluated using the DME m...

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