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MOOCEOLOGY - Honing in on Social Learning in MOOC Forums: Examining Critical Network Definition Decisions

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MOOCEOLOGY - Honing in on Social Learning in MOOC Forums: Examining Critical Network Definition Decisions

  1. 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. 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. 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. 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. 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. 6. 5 S1 R1 R2 RR1 RR2 RR3 S1 R1 R2 RR1 RR2 RR3 S1 R1 R2 RR1 RR2 RR3 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. 7. 6 UnpartitionedContent-relatedNon-content Number of edges Avg. node degree Avg. edge weight 0 1000 2000 3000 4000 5000 6000 0 5 10 15 20 25 0 5 10 15 20 25 Direct Reply Star Direct Reply + Star Limited Copresence Total Copresence 0 1000 2000 3000 4000 5000 6000 0 5 10 15 20 25 0 5 10 15 20 25 0 1000 2000 3000 4000 5000 6000 0 5 10 15 20 25 0 5 10 15 20 25 Tie Definition Effects - Resulting Network Properties
  8. 8. Direct Reply Star Direct Reply + Star Limited Copresence Total Copresence UnpartitionedContent-relatedNon-content Tie Definition Effects - Resulting Networks
  9. 9. Direct Reply Total CopresenceUnpartitionedContent-relatedNon-content 0 5 10 15 20 25 0 5 10 15 20 25 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 0 5 10 15 20 25 0 5 10 15 20 25 0 1000 2000 3000 4000 5000 6000 0 5 10 15 20 25 0 5 10 15 20 25 Number of edges Avg. node degree Avg. edge weight Direct Reply Total Copresence
  10. 10. 0 1 2 3 4 5 6 7 8 Avg edge weight 0 1 2 3 4 5 6 7 8 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. 11. 0 2 4 6 8 10 CL1 NL1 NL2 Avg node degree 0 2 4 6 8 10 12 14 16 CL1 NL1 NL2 Avg edge weight 10 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. 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. 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. 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. 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. 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. 17. Thanks! alyssa.wise@nyu.edu yca231@sfu.ca wanqij@sfu.ca

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