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Journal of Data and Information Science  2018, Vol. 3 Issue (2): 62-77    DOI: 10.2478/jdis-2018-0009
Research Paper     
Is Participating in MOOC Forums Important for Students? A Data-driven Study from the Perspective of the Supernetwork
Chaocheng He, Panhao Ma, Lusha Zhou, Jiang Wu()
School of Information Management, Center for E-commerce Research and Development, Wuhan University, Luojia Mountain, Wuhan 430072, China
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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.

Key wordsMOOC      Participate      Forum      Supernetwork      Super degrees     
Published: 14 June 2018
Cite this article:

Chaocheng He, Panhao Ma, Lusha Zhou, Jiang Wu† . Is Participating in MOOC Forums Important for Students? A Data-driven Study from the Perspective of the Supernetwork. Journal of Data and Information Science, 2018, 3(2): 62-77.

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N Minimum Maximum Mean Std. Deviation
Tests/113.0 1,917 0 113 69.23 38.98
Exercises/30.0 2,625 3 30 15.30 7.21
Exams/30.0 1,264 0 30 28.38 2.67
Discussions/30.0 3,231 0 30 22.10 13.21
Posts 804 0 15 0.57 1.46
Comments 804 0 46 0.43 2.29
Replies 804 0 116 3.59 8.02
Total Score/100 3,231 0 99.8 48.15 34.97
Table 1 Descriptive statistics of student scores and forum activities.
Test Score Exercises Score Exam Score Total Score
Z -4.439 -5.06 -5.983 -3.918
P-value .000 .000 .000 .000
Table 2 Test statistics.
Group N Mean Rank Mean score
Tests Non-discussion 1,343 798.94 58.97
Discussion 573 1,332.49 94.31
Exercises Non-discussion 2,056 1,138.56 13.5
Discussion 568 1,942.11 21.77
Exams Non-discussion 755 591.66 28.02
Discussion 508 691.96 28.87
Scores Non-discussion 2,599 1,400.70 40.14
Discussion 631 2,500.22 81.05
Table 3 Results of nonparametric tests.
Coefficient P-value
Comment 0.879 .004
Reply 3.937 .000
Post 1.776 .000
(Constant) -.900 .242
Adjusted R2 0.657
P-value 0.000
Table 4 Regression analysis results.
Figure 1. Example of a Hypergraph.
Username Thread 1 Thread 2 Thread 3 Thread 4 Thread 5
Lin Wei ykt1123
Red Fruit mooc4
Chongqing WZZ
Table 5 Participants and threads they participate in.
Username Node degree Super-node degree
Viannn 5 5
Winner 4 3.59
Day@4 4 3.31
mooc15951364231 3 2.17
Lin Wei ykt1123 3 1.86
Red Fruit mooc4 2 1.10
Chongqing WZZ 2 1.10
AYmooc 2 0.90
Monogram 2 0.90
SUNSET 2 0.76
Table 6 Node degree and super-node degree of participants.
Thread Edge degree Super-edge degree
Thread 1 8 6.62
Thread 2 8 6.90
Thread 3 5 3.10
Thread 4 5 2.93
Thread 5 3 1.14
Table 7 Edge degree and super-edge degree of threads.
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