The document discusses the analysis and prediction of learner interactions in online discussions within MOOCs, highlighting issues like participant heterogeneity and low engagement. It presents a methodology for predicting future interactions and potential social learning networks among learners based on their past behavior. The findings indicate that while most contributions are singular, recurring interactions have a pattern, and the predictive model shows some promise, although there are discrepancies between predicted and actual interactions.

1. ANALYSING AND PREDICTING
RECURRENT INTERACTIONS AMONG
LEARNERS DURING ONLINE
DISCUSSIONS IN A MOOC
Ayşe Saliha Sunar
06/11/15 ICKM 2015 Osaka @aysesCS
1
ass1a12@soton.ac.uk
@aysesCS

2. My background
Gazi University, TURKEY
BSc in Mathematics
Non-thesis master in Teaching mathematics to
secondary school students
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Nagoya University, JAPAN
MSc in Computer Supported Education
& Intelligent Tutoring Systems
University of Southampton,
UNITED KINGDOM
PhD in Learning Analytics
& Personalisation
& MOOCs

3. • MOOC Datasets management
• Data Analysis
• Curation:
• Academic Literature (Mendeley)
• Journalistic literature (Scoop.it)
• Blog
• Training
• Publications
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4. • MOOC Datasets management
• Data Analysis
• Curation:
• Academic Literature (Mendeley)
• Journalistic literature (Scoop.it)
• Blog
• Training
• Publications
06/11/15 ICKM 2015 Osaka @aysesCS 4
Massive
Open
Online
Courses
 Since 2007…
 Learners communicate

5. • MOOC Datasets management
• Data Analysis
• Curation:
• Academic Literature (Mendeley)
• Journalistic literature (Scoop.it)
• Blog
• Training
• Publications
06/11/15 ICKM 2015 Osaka @aysesCS 5

6. My motivation in
• Track and contribute to the development in
mass personalisation in MOOCs
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Some issues:
• Heterogeneity of learners
• High dropouts
• Low participation in online
discussions
Possible solution:
Personalisation services
by using learning
analytics

7. First task: To Understand the Current
Situation in Personalisation of MOOCs
• 7th International Conference on Computer Supported
Education, 23-25 May, 2015, Lisbon available on eprints
http://eprints.soton.ac.uk/381181/
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Ayse Saliha SUNAR
Nor Aniza ABDULLAH
Hugh C DAVIS
Su WHITE

8. Results from the Literature Review
• Some personalisation services aim at helping learners
through online communication
• Excessive information on discussion forums
• Less number of participants
• Difficulty in finding like-minded peer to discuss
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• If we predict learners’ future activity in online
discussions, it could be very helpful to intervene
their learning by offering personalised service.
And, eventually learners may even complete the
course.

9. Preliminary experiment: The Nature of
Social Learning Networks in MOOCs
• Focus of study:
• How much did the learners contribute to online discussions?
• Did they sustain their contribution to online discussions?
• Did recurrent interactions occur over the weeks?
• Can we predict learners’ potential relationships?
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It is important to understand learners’ behaviour and the
nature of their communications in MOOCs.

10. Methodology
• Analysis of Develop Your Research Project MOOC on
FutureLearn MOOC platform (15 September – 5 November
2014 )
• Dataset: Learners’ comments on the discussion boards (15
September – 22 November 2014
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• A tool is developed to
identify relationships
between learners through
their communication on
discussion board.

11. Identifying Social Learning Networks
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• There are two types of comments
• Individual comment: single comments reflecting learner’s opinion,
thought, question and so on.
• Interaction (between two learners): reply to somebody’s comment.
• The strength of relationships based on a peer’s interactions is
calculated.
• These directed and weighted relationships are illustrated by a
graph and matrix.

12. Results of General Analysis (1/4)
General Analysis of the Data
• Funnel participation (Clow, 2013) has been observed in
the studied MOOC’s course i.e. Developing Your
Research Project
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13. Results of General Analysis (2/4)
• Learners’ interactions and the strength of their interactions
in each week
13
Week1
Week4
Week7
Week2
Week5
Week8
Week3
Week6

14. Results of General Analysis (3/4)
• The illustrations denote that while 1867 learners
contributed to online discussions by posting at least one
comment, only less than half of them replied to the
comments.
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15. Results of General Analysis (4/4)
• Recurrent interactions in a week and over the weeks.
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• Despite the low
number of
recurrent
interactions, their
interactions have a
pattern.
• When an interaction occurred, it is more likely recur in the
immediate week.

16. Strength of Relationships
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Strength of relationship between the learner u and the
learner v
• The frequency of interactions between them is considered
by this formula:
where is the number of interaction from the learner
u to the learner v and is the total number of contributions
the learner has done in the MOOC.

17. Learner’s Overall Interest
Learner’s Overall Interest towards Online Discussions in a
MOOC
• Overall interest is the social interest that a learner has
shown from the beginning of the course until a current
week. It is calculated as follows:
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where cu denotes the total number of comments made by
the learner u and c is the total number of comments made
by all learners.

18. Prediction Method
Predicted Social Learning Networks
• If a learners has not initiate any friendship yet in the course, it
might be possible to predict their potential social learning
network.
• In order to identify a learner’s predicted social learning
networks, predicted strength of friendships with every other
learner needs to be first determined.
• However, the predicted strength of friendship between two
learners varies according to their kind of friendship history.
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19. Prediction Method
Case 1 – friendship with zero-comment learners:
• Learners in this category have not contributed to the
online discussions yet.
• Therefore, they have no social learning network
and learning history in the MOOC.
• Thus, strength of a possible friendship cannot be
predicted.
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20. Prediction Method
Case 2 – persistent friendship:
• Friendship between learners who have been friends
before
• Use arithmetic mean to predict the strength of
relationship between the learner u and the learner v
whom the learner u has previously interacted with
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where n is the number of mutual courses taken by the
learner u and v.

21. Prediction Method
Case 3 – indirect friendship:
• Friendship with the learner v through mutual friend(s)
• Use correlation between the learner u and the
learner v through the mutual friend(s) j
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where k is the number of mutual friends of the learner
u and the learner v.

22. Prediction Method
Case 4 – isolated friendship (1/3):
• Friendship with the learner v who has no mutual
friend
• Use a probabilistic model for prediction of the
strength of possible friendship between the learner u
and the learner v
• Therefore, learners possible interest to the new
course is calculated first based on their previous
activities.
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23. Prediction Method
Case 4 – isolated friendship (2/3):
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• Therefore, each learner’s interest in common courses are:
where A and B are the sets of learners enrolled in the
MOOC A and B, respectively.
Overall common interest towards two MOOCs
• If the number of common learners are high, it is assumed
that the overall interest towards MOOCs is high.
where ci is the set of MOOCs previously taken by the
learner u.

24. Prediction Method
Case 4 – isolated friendship (3/3):
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• Finally, the predicted strength of friendship between the
learner u and the learner v in the new MOOC A is
estimated by the following formula:

25. Results of Prediction Method (1/3)
• Comparison of prediction values and strengths in each week
25

26. Results of Prediction Method (2/3)
• Results are promising.
• For example, in Week 4, the method predicts possible
interactions for learners who have persisted and indirect
friendships. These learners get interacted in real and
have relatively higher friendship strength value.
26

27. Results of Prediction Method (3/3)
• Negatively, even though the method predicts some
interactions could happen, some of those interactions
are never observed between learners and vice versa.
• For example, there are several interactions occurred
in Week 3 that have not been predicted.
27

28. Conclusion and Future Work
• Most of the participations in online discussions are one-
time posting
• Interactions between learners are remarkably low in
comparison to number of comments posted to the online
discussion board
• If learners interacted with each other once, it appears
likely that they will interact again in subsequent weeks
• We are going to test our method on the other MOOCs’
discussion forums to statistically show the causality
between participation in online discussions and the
attrition rate.
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29. Mendeley
• Collection of paper on personalisation in MOOCs
23-25 May 2015 29Ayse Saliha Sunar @aysesCS
https://www.mendeley.com/groups/4715311/mooc-personalisation

30. • Find this
presentation
online!
23-25 May 2015 Ayse Saliha Sunar @aysesCS 30
http://www.slideshare.net/aysessunar/ickm-2015-analysing-predicting-recurrent-
interaction-in-moocs-forums
SlideShare

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