ICKM 2015 - Analysing & Predicting Recurrent Interaction in MOOCs forums

ICKM 2015 Osaka @aysesCS

ANALYSING AND PREDICTING RECURRENT INTERACTIONS AMONG

LEARNERS DURING ONLINE DISCUSSIONS IN A MOOC

Ayşe Saliha Sunar

06/11/15

1

[email protected]

@aysesCS

ICKM 2015 Osaka @aysesCS 2

My background Gazi University, TURKEYBSc in Mathematics Non-thesis master in Teaching mathematics to secondary school students

06/11/15

Nagoya University, JAPAN MSc in Computer Supported Education & Intelligent Tutoring Systems University of Southampton,

UNITED KINGDOM PhD in Learning Analytics & Personalisation & MOOCs


• MOOC Datasets management• Data Analysis• Curation:

• Academic Literature (Mendeley)• Journalistic literature (Scoop.it)

• Blog• Training• Publications

06/11/15





06/11/15

Massive

Open

Online

Courses

Since 2007…

Learners communicate





06/11/15


My motivation in

• Track and contribute to the development in mass personalisation in MOOCs

06/11/15

Some issues: • Heterogeneity of learners • High dropouts • Low participation in online

discussions

Possible solution:Personalisation services by using learning analytics


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 eprintshttp://eprints.soton.ac.uk/381181/

06/11/15

Ayse Saliha SUNAR

Nor Aniza ABDULLAH

Hugh C DAVIS

Su WHITE


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

06/11/15

• 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.


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?

06/11/15

It is important to understand learners’ behaviour and the nature of their communications in MOOCs.


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

06/11/15

• A tool is developed to identify relationships between learners through their communication on discussion board.


Identifying Social Learning Networks

06/11/15

• 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

30/09/15

13

Results of General Analysis (2/4)• Learners’ interactions and the strength of their interactions

in each week

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.

30/09/15

15

Results of General Analysis (4/4)• Recurrent interactions in a week and over the weeks.

30/09/15

• 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.


Strength of Relationships

06/11/15

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:

30/09/15

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.

30/09/15

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.

30/09/15

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

30/09/15

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

30/09/15

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.

30/09/15

23


30/09/15

• 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


30/09/15

• 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

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.

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.


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.

06/11/15

29

Mendeley • Collection of paper on personalisation in MOOCs

23-25 May 2015 Ayse Saliha Sunar @aysesCS

https://www.mendeley.com/groups/4715311/mooc-personalisation

Ayse Saliha Sunar @aysesCS 30

• Find this presentation online!

23-25 May 2015

http://www.slideshare.net/aysessunar/ickm-2015-analysing-predicting-recurrent-interaction-in-moocs-forums

SlideShare

ICKM 2015 Osaka @aysesCS 3106/11/15

Technology

ICKM 2015 - Analysing & Predicting Recurrent Interaction in MOOCs forums