FLAN conference: MOOCs, personalisation, social networks
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This is the work presented at FutureLearn Academic Network Conference on 15.06.2015, in UK Open University. The presentation gives a brief explanation of analysis I have done to understand the importance of social network analysis in MOOCs through personalised MOOCs.
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FLAN conference: MOOCs, personalisation, social networks
- 1. The State of the Art of Personalised MOOCs & The Importance of Social Learning Networks Analysis Ayşe Saliha SUNAR 15.06.2015
- 2. My education journey 15 June 2015Ayşe Saliha Sunar @aysesCS 1 Turkey BSc: Mathematics Japan MSc: Intelligent Tutoring Systems United Kingdom PhD: Learning Analytics & Personalisation & MOOCs
- 3. 15 June 2015Ayşe Saliha Sunar @aysesCS 2 MOOC Datasets management Data Analysis Curation: Academic Literature (Mendeley) Journalistic literature (Scoop.it) Blog Training Publications
- 4. Motivation 15 June 2015Ayşe Saliha Sunar @aysesCS 3 Opportunity Personalisation services by using learning analytics Possible solution Drawbacks MOOCs produce large datasets from a large number of learners. Lack of customised education to meet each learner’s need Low participation in online discussions & high attrition rate
- 5. The State of the Art of Personalised MOOCs 15 June 2015Ayşe Saliha Sunar @aysesCS 4
- 6. CSEDU paper 7th International Conference on Computer Supported Education, 23-25 May, 2015, Lisbon 15 June 2015Ayşe Saliha Sunar @aysesCS 5 Ayse Saliha SUNAR Nor Aniza ABDULLAH Hugh C DAVIS Su WHITE
- 7. Focus of the paper What is the nature of the literature on personalisation in MOOCs? What kind of issues are addressed by this literature? What kind of techniques are applied for personalising MOOC education? Which directions could personalisation attempts in MOOCs lead to? 15 June 2015Ayşe Saliha Sunar @aysesCS 6
- 8. Methodology: Critical & Systematic Literature Review 15 June 2015Ayşe Saliha Sunar @aysesCS 7 Search for the keywords “MOOCs personalisation” and “adaptive MOOCs” between 2011 and 2014 Google Scholar The British Journal of Educational Technology American Journal of Distance Education Journal of Online Learning and Teaching ISI Web of Knowledge IEEEXplorer
- 9. Growing attention to MOOCs personalisation 15 June 2015 0 100 200 300 400 500 600 700 2011 2012 2013 2014 Total results for "MOOCs personalisation" Total results for "adaptive MOOCs" Relevant results for "MOOCs personalisation" Relevant results for "adaptive MOOCs" Ayşe Saliha Sunar @aysesCS 8 The total number of papers and relevant papers by the searches
- 10. Findings Several techniques used for addressing several issues 15 June 2015 Adaptive content presentation Poor pedagogical design Adaptive navigation • Poor content engagement • Lack of study skills Adaptive feedback Poor feedback Recommender system • Heterogeneity of learners • High dropouts • Excessive information on discussion forums Ayşe Saliha Sunar @aysesCS 9
- 11. Use of social network analysis Researchers analysed enormous data of learners on discussion forums and other social media tools to answer: Which social media tools are used often? Who contributed more? Who interacted with whom? Is there any pattern of relations? 15 June 2015Ayşe Saliha Sunar @aysesCS 10
- 12. The Importance of Social Learning Networks Analysis 15 June 2015Ayşe Saliha Sunar @aysesCS 11
- 13. Social networks on FL Analysis of Develop Your Research Project MOOC (15 September – 5 November 2014 ) Dataset: Learners’ comment on the discussion boards (15 September – 22 November 2014) 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? 15 June 2015Ayşe Saliha Sunar @aysesCS 12
- 14. Analysis of DYRP MOOC This study has been submitted as a full paper to ICKM2015. 15 June 2015Ayşe Saliha Sunar @aysesCS 13 Week1 Week4 Week7 Week2 Week5 Week8 Week3 Week6 Learners’ interactions and the strength of their interactions in each week
- 15. Recurrent interactions in DYRP 15 June 2015Ayşe Saliha Sunar @aysesCS 14 Only 19 peers interacted multiple times in different weeks! Those learners joined all weeks and completed the course. The recurrent interactions
- 16. Use of ‘following’ tab on FL 15 June 2015Ayşe Saliha Sunar @aysesCS 15 Do FutureLearn learners use FOLLOW function efficiently? Do they use FOLLOWING tab on discussion board for active communication with their following list? Is the following list usually empty?
- 17. My PhD research The Follow function on FutureLearn is very convenient for promoting continuous friendships. But it needs to be improved. A recommender system for friend recommendation could enhance learners’ interactions. 15 June 2015Ayşe Saliha Sunar @aysesCS 16 Learners who have continuous friendships could sustain their interactions and most likely complete the course. Hypothesis
- 18. Mendeley 15 June 2015Ayşe Saliha Sunar @aysesCS 17 Collection of paper on personalisation in MOOCs https://www.mendeley.com/groups/4715311/mooc-personalisation
- 19. 15 June 2015Ayşe Saliha Sunar @aysesCS @aysesCS ass1a12@soton.ac.uk 18
Editor's Notes
- I like to talk about my educational background first. CLICK I received my bachelor degree in mathematics in Turkey, CLICK then I went to Japan for master education in technology enhanced learning, specifically ITS. CLICK Shortly after, I have started my phd in the University of Southampton. Now, I am a 2nd year phd student. My general phd interests are learning analytics, personalisation and MOOCs.
- Student’s studies output
- MOOCs have some drawbacks. For example, the curriculum of MOOCs doesn’t meet every needs of learners. Also, low participation in the course is commonly observed. However, since MOOCs produce a large amount of data, this is an opportunity for research to overcome those shortcomings.
- I like to talk about the fundamental study I have completed.
- We have done a critical literature review on personalisation of MOOCs. This paper is presented in CSEDU conference last month.
- The focus of the paper is to understand how researchers discussed the personalisation in MOOCs and what kind of personalisation techniques they have applied.
- We have searched two keywords in between 2011 and 2014 on these digital academic database.
- According to the analysis results, the total number of retrieved paper sharply increase in 2013. Yet the related number of papers are quite low. I should express that most of the related paper were on Google Scholar and the related papers we consider here are not only peer-reviewed paper, but also from the grey literature for example working papers, project reports.
- In the conducted and partly or fully implemented researches, several techniques used for addressing several issues such as adaptive content presentation, adaptive navigation and so on. Among the papers, the most commonly applied technique is recommender system and mostly used for fostering learners engagement in online discussion forums and helping them finding useful information and reducing high dropouts.
- In those researches, researchers especially looking for the answer those question to propose a solution.
- Next, I like to talk about my search and analysis on social learning networks in MOOCs.
- I have analysed learners’ interactions in the Develop Your Research Project MOOC delivered by the University of Southampton. I have analysed learners’ comments and replies to someone else’s comment in order to answer these questions.
- These are the learners’ interactions graphic. Interactions means that a learner actually post a reply to somebody’s comment. These graphics do not show the individual comments , but only replies. The z_axis shows the strength of interactions. I will not talk about how I calculated the strength of learner’s interactions. However, the strength is mainly based on the frequency of interactions. As you can see, the number of interactions is decreasing towards the end of the course. So do the strength of relationships.
- This is the graphic showing the recurrent interactions. The numbers are anonymised learner’s IDs. Each mark identifies a recurrent interaction in a different week. If there are overlapped marks, that means those two learners got interacted again in another week. However, this kind of learners are only 19 peers. Also, we observed that those learners completed the course.
- These results lead us to think about the functionality of FOLLOW functions. Do the FutureLearn MOOC learners follow each other? Do they check the following tab on the discussion boards? If they did, the recurrent interaction rate would have been higher.
- Therefore, I have conducted my research on the hypothesis that Learners who have continuous friendships could sustain their interactions and most likely complete the course. In order to enhance learners’ interactions, a recommender system for friend recommendation could be a very convenient method. This also could improve the Follow function on FutureLearn platform.
- Lastly, this is the Mendeley group that you can find all literature on personalisation of MOOCs I have cited in my papers and more. You are very welcome to contribute to the group.
- All feedbacks are appreciated. Thank you very much.