Purpose： Compared with traditional course materials used in the classroom, the massive open online course（MOOC） forum that delivers unlimited learning content to students has various advantages. Yet MOOC has also r...Purpose： Compared with traditional course materials used in the classroom, the massive open online course（MOOC） forum that delivers unlimited learning content to students has various advantages. Yet MOOC has also received criticism recently, notably the problem of extremely low participation rates in its discussion forums. This study aims to explore the correlation between forum activity and student course grade in MOOC, and identify more accurately the forum activity levels of participants and the quality of threads in MOOC.Design/Methodology/Approach： We crawled students’ tests, final exams, exercises, discussions performance data and total scores from a course in Chinese College MOOC from May 2014 to August 2014. And we use the data to analyze the correlation between Forum Participation and Course Performance based on nonparametric tests as well as multiple linear regressions with the software of R. The study provides definitions and algorithms of super degrees based on the supernetwork model to help find high-quality threads and active participants.Findings： A positive correlation between forum activity and course grade is found in this study. Students who participate in the forum have better performance than those who do not. Using the definitions and algorithms of super degrees in the supernetwork, forum activity levels of participants as well as the quality of threads they employ are identified. Research limitation： Only limited representative forum participants and threads are used to analyze the activity level and significance of the MOOC forum. Also, the study only investigates one Chinese course on information retrieval. More data and more data sources could be helpful in better understanding the MOOC forum phenomenon.Practical implications： As super degrees can reveal more latent information and recognize high-quality threads as well as active participants, these parameters can be used to assess needs to improve forum settings and alleviate the problem of low forum participation. The proposed super degrees can be applied in social network domains for further research.Originality/Value： Definitions and algorithms of super degrees are provided and used for forum analysis. Super degrees can be applied to find high-quality threads and active participants, which is beneficial to guide students to participate in these high-quality threads and have a better understanding of knowledge MOOC provides.展开更多
基金supported by grants from the National Natural Science Foundation of China (Grant Numbers: 71373194 and 71573197)the Fundamental Research Funds for the Central Universities
文摘Purpose： Compared with traditional course materials used in the classroom, the massive open online course（MOOC） forum that delivers unlimited learning content to students has various advantages. Yet MOOC has also received criticism recently, notably the problem of extremely low participation rates in its discussion forums. This study aims to explore the correlation between forum activity and student course grade in MOOC, and identify more accurately the forum activity levels of participants and the quality of threads in MOOC.Design/Methodology/Approach： We crawled students’ tests, final exams, exercises, discussions performance data and total scores from a course in Chinese College MOOC from May 2014 to August 2014. And we use the data to analyze the correlation between Forum Participation and Course Performance based on nonparametric tests as well as multiple linear regressions with the software of R. The study provides definitions and algorithms of super degrees based on the supernetwork model to help find high-quality threads and active participants.Findings： A positive correlation between forum activity and course grade is found in this study. Students who participate in the forum have better performance than those who do not. Using the definitions and algorithms of super degrees in the supernetwork, forum activity levels of participants as well as the quality of threads they employ are identified. Research limitation： Only limited representative forum participants and threads are used to analyze the activity level and significance of the MOOC forum. Also, the study only investigates one Chinese course on information retrieval. More data and more data sources could be helpful in better understanding the MOOC forum phenomenon.Practical implications： As super degrees can reveal more latent information and recognize high-quality threads as well as active participants, these parameters can be used to assess needs to improve forum settings and alleviate the problem of low forum participation. The proposed super degrees can be applied in social network domains for further research.Originality/Value： Definitions and algorithms of super degrees are provided and used for forum analysis. Super degrees can be applied to find high-quality threads and active participants, which is beneficial to guide students to participate in these high-quality threads and have a better understanding of knowledge MOOC provides.
