Presentation by Yi Cui at LAK'17 on the importance of tie definition and content partitioning in SNA of MOOC forums

1. Honing in on Social Learning in MOOC Forums:
Examining Critical Network Definition Decisions
Alyssa Wise, Yi Cui, Wan Qi Jin
LAK’17, March 2017, Vancouver, CANADA

2. Investigation of the interaction practices in
large-scale learning environments based on
analysis of the artifacts left behind by
learners’ and instructors’ activity.
MOOCEOLOGY
1

3. QUESTIONS DATA METHODS RESULTS CONCLUSION
RQ1: What effects do different tie definitions have on
network characteristics?
RQ2: In what ways do unpartitioned, content-related, and
non-content social networks show distinct characteristics?
RQ3: What differences in the discussion interactions may
account for the distinctions between networks?
Research Questions
2

4. Learning Context & Data
• A MOOC on statistics in medicine
• 3-level posting structure
• Forums facilitated by 2 instructional team
members
• 567 users
• 817 threads containing 3124 posts 3
QUESTIONS DATA METHODS RESULTS CONCLUSION

5. Content-based Partitioning
• Content-related = Q&As/comments related to the course subject
• Non-Content = Others (logistical & technical Q&As, socilizing)
• Method: Dynamic Interrelated Post and Thread Categorization
(Cui, Jin, & Wise, in press), est. accuracy = .88
4
Non-
content
Content-
related
THREADS
468 threads
349 threads
Content-
related
activities
178
31%
Both
157
28%
Non-
content
activities
232
41%
PARTICIPANTS
QUESTIONS DATA METHODS RESULTS CONCLUSION

6. 5
S1 R1
R2
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S1 R1
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S1 R1
R2
RR1
RR2
RR3
S1 R1
R2
RR1
RR2
RR3
Direct Reply Star
Direct Reply
+ Star
Limited
Copresence
Total
Copresence
RQ1: Tie Definition Effects
QUESTIONS DATA METHODS RESULTS CONCLUSION

7. 6
UnpartitionedContent-relatedNon-content
Number of edges Avg. node degree Avg. edge weight
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Tie Definition Effects - Resulting Network Properties

8. Direct Reply Star Direct Reply + Star Limited Copresence Total Copresence
UnpartitionedContent-relatedNon-content
Tie Definition Effects - Resulting Networks

9. Direct Reply Total CopresenceUnpartitionedContent-relatedNon-content
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Number of edges Avg. node degree Avg. edge weight
Direct Reply Total Copresence

10. 0
1
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Avg edge weight
0
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3
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Avg node degree
9
Content-related network
(# of nodes = 335, # of edges = 848)
Non-content network
(# of nodes = 389, # of edges = 724)
RQ2 & 3: Content vs Non-Content Networks
Content-related
Non-content
QUESTIONS DATA METHODS RESULTS CONCLUSION

11. 0
2
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CL1 NL1 NL2
Avg node degree
0
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CL1 NL1 NL2
Avg edge weight
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Learner Modules
CL = Content-related
Learner Module
NL = Non-content
learner module
CL1
(# of nodes = 23, # of edges =57)
NL1
(# of nodes = 62, # of edges =71)
NL1
(# of nodes = 23, # of edges = 28)
QUESTIONS DATA METHODS RESULTS CONCLUSION

12. 11
Content vs Non-Content Discussions
1. Thread Characteristics
Content-related
Module 1
Non-content
Module 1
Non-content
Module 2
# of contributing threads 30 2 11
Avg # of posts per participant in thread 2.41 (1.62) 1.13 (0.07) 1.42 (0.53)
Avg # of posts per thread (SD) 13.4 (14.85) 6, 93 6 (5.75)
2. Activity characteristics: content-related activities involved more
• complicated topics
• interaction techniques
• social presence cues
QUESTIONS DATA METHODS RESULTS CONCLUSION

13. 12
U225: Congrats [u10]! Yes, it has been hard, but fun, and
we learned an awful lot, right?
U110: Great! Everyone it was a pleasure to work with
you. Thank you….
U10: YES [u225]! And [u110] - the test was scary - I
thought of my discussion board friends often!!
U216: Thanks, thanks so much to [u10], [u152], [u110],
[u225] and everybody who helped us to understand this
beautiful course! And in my case also for writing many
posts, I see I have improved my English skills and my
statistics vocabulary!!!
U225: [u10], [u216], [u152], [u110], [u515] and everyone,
your discussions helped me so much. I was always a few
days behind you in homework - glad I was able to catch
up in the last weeks and participate a little bit….
Content-related
learner module 1
Examining Learner Interaction
U225: [u10], [u216], [u152], [u110], [u515] and everyone,
your discussions helped me so much. I was always a few
days behind you in homework - glad I was able to catch
up in the last weeks and participate a little bit….
U10: YES [U225]! And [u110] - the test was scary - I
thought of my discussion board friends often!!
QUESTIONS DATA METHODS RESULTS CONCLUSION

14. 13
CI1 CI2 NI1 NI2
# of nodes (% in network) 184 (54.93%) 75 (22.39%) 168 (43.19%) 47 (12.08%)
# of edges (% in network) 400 (47.17%) 105 (12.38%) 315 (43.51%) 55 (7.6%)
Avg node degree (SD) 4.35 (11.06) 2.8 (7.56) 3.75 (11.18) 2.34 (6.03)
Avg edge weight (SD) 2.23 (3.21) 1.83 (1.72) 2.11 (2.48) 1.20 (0.44)
Instructor Modules
CI = Content-related instructor module NI = Non-content instructor module
CI1 CI2 NI1 NI2
QUESTIONS DATA METHODS RESULTS CONCLUSION

15. U1
• Responses at all levels
• Coaching and supporting
• Social presence cues
U417
• Responses to thread starters
• Straight forward answers
• Little social presence
14
Comparing Intervention Approaches
QUESTIONS DATA METHODS RESULTS CONCLUSION
“Think about it again using the
hint and let me know if you have
any other questions.”
“That is correct - Nice! So how
would you use this to solve the
question?”
“A bell shape is not necessary.
You could have a 'bimodal'
distribution where the two
groups do not follow a bell
shape.”

16. Conclusions
• Tie definition affects network and interpretation.
• Content-related and non-content networks had
distinct characteristics.
• Content-related discussions led to wider and
deeper learner connection; instructor’s
intervention approach may also affect it.
15
QUESTIONS DATA METHODS RESULTS CONCLUSION

17. Thanks!
alyssa.wise@nyu.edu
yca231@sfu.ca
wanqij@sfu.ca