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First year report 
Ayşe Saliha Sunar 
The University of Southampton 
Electronics and Computer Science 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Outline 
 My PhD interest and the first year of the PhD 
 Research aim and questions 
 Background of the study 
 Literature survey: personalisation of MOOCs 
 PhD approach 
 Future plan 
2 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
My PhD Interest 
 MOOCs: rapidly growing area 
 Growing number of learners 
https://www.edsurge.com/n/2013-12-22-moocs-in-2013-breaking-down-the-numbers 
 Growing number of researches 
3 
http://www.irrodl.org/index.php/irrodl/article/view/1455/2531 
• Master project: on Intelligent Tutoring Systems 
• PhD research interest: Personalisation of MOOCs 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
The first year of the PhD 
Oct 
• Orientation 
Nov-Feb 
• Read to gain fundamental knowledge about the area 
Mar-Jul 
• Clarified the idea for the PhD project 
• Wrote the first year report 
Aug 
• Submitted the first year report 
• Done deep examination as a holiday homework 
Sept 
• Resubmitted the first year report 
• Done the viva preparation 
4 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Research Aim and Questions 
 To investigate viability and 
potential value of integration a 
personalised approach into a 
MOOC environment to increase 
learners’ interaction and 
engagement in the course 
subject 
 Can providing personalised 
recommendations to learners who 
engage with MOOCs help 
1. building a personal network for each 
5 
learner, which includes people whom 
with share common interests 
2. identifying digital resources in which 
learners are interested 
3. maintaining learners’ motivation 
during the course 
4. making learners more satisfied with 
their MOOC experience 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Background: MOOCs 
 Massive Open Online Courses 
Strength Weakness 
Widely accessible Limited interaction 
Low coast Pedagogy 
Self-paced learning Assessment 
Life-long learning Feedback 
Academic credits (optional) Decrease in motivation 
6 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
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Background: Personalisation in e-learning 
systems 
 Personalised learning systems 
 Intelligent Tutoring Systems 
 Adaptive Hypermedia Systems 
INPUT(s) SYSTEM OUTPUT(s) 
• Adaptive content 
delivery 
• Adaptive 
representation of 
content 
7 
• Adaptive assessment 
• Personalised feedback 
• Recommendations 
• Knowledge level 
• Errors/Misconceptions 
• Motivation 
• Progress on tasks 
• Learning approach 
• Learners’ preferences 
• Systems’ pedagogical 
approach 
User Model 
Adaptive 
Model 
Domain 
Model 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey on Personalisation 
of MOOCs 
 Demonstrating contributions of studies in different aspect of MOOC 
personalisation 
Registration 
& 
Log-in 
Adaptive planner 
(Alario-Hoyos et al. 
2014) 
Course(s) 
selection from 
the course list 
Learning pathways 
(Bansal 2013; Marinda et al. 2013 
Henning et al. 2014) 
Wiki and blogs 
(if there any) 
Feedback 
Course selection 
among selected 
courses 
Automatic assessment 
generation 
(Marinda et al. 2013) 
Learning contents 
(Sonwalker 2013; 
Nesterko, 2014) 
Lecture 
selection in 
the course 
Assessment 
Discussion 
forums 
Lecture 
content 
delivery 
Forum Thread Recommendation 
(Researchers from the EDGE Lab., 
2013; Yang et al. 2014) 
8 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey on Personalisation 
of MOOCs 
 Summarising motivation and targets of the current researches 
Subject Motivation Target learners 
Study plan Helping learners to arrange their schedule 
according to priorities 
9 
Have lack of experience in 
studying 
Learning materials 
& pathways 
Designing suitable learning materials and 
pathways to each individual in a diverse 
MOOCs learners’ community 
Actually planned but did 
not finish the course they 
enrolled in 
Learning contents Designing contents based on learners’ goal 
and learning style 
All types of MOOC 
learners 
Forum threads Helping learners to find useful threads among 
overloaded information 
MOOC learners who get 
engaged in discussion 
forums 
Assessments Facilitating authoring assessments and giving 
right material to learners 
All types of MOOC 
learners 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Personalisation 
of MOOCs tasks 
 Adaptive planner for facilitating the management of tasks in 
MOOCs (Alario-Hoyos et al., 2014) 
 Target: those who has lack of experience in studying MOOCs, 
may benefit from personalised planning and feedback to 
develop work habits and study skills 
10 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Personalisation 
of learning materials 
 Adaptive Recommendation System for MOOC (Bansal, 2013) 
 A Project Stage Report for a PhD Thesis 
 Objective: providing the recommendation/feedback of some 
tasks to complete based on learners’ activities in the current 
week before the start of the next week 
 Target: all types of MOOC learners 
 Benefit: learners would be aware of the concepts they are 
lacking and have a chance to recover it by getting some 
recommended tasks. 
11 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Personalisation 
of learning pathways 
 Personalized Web Learning: Merging Open Educational 
Resources into Adaptive Courses for Higher Education 
(Henning et al., 2014) 
 The outline of the study is represented. 
 Objective: recommending personal learning pathways for each 
learner 
 Target: learners who actually planned but did not finish the 
course they enrolled in 
 Benefit: high dropout rates would decrease. 
12 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Personalisation 
of learning contents 
 The First Adaptive MOOC: A Case Study on Pedagogy 
Framework and Scalable Cloud Architecture—Part I 
(Sonwalker, 2013) 
 Aim: improving pedagogical effectiveness of MOOCs 
 Objective: adapting the content of learning materials to the 
way a learner would like to learn 
 Target: all types of MOOC learners 
13 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Personalisation 
of learning contents 
 MOOC Research Initiative - Final Report 
Project Title: MOOCs Personalization for various Learning Goals 
(Nesterko, 2014) 
 Project: funded by The Bill and Melinda Gates foundations and 
the project leader is Dr. Sergiy Nesterko from HarvardX 
Research Fellow. 
 Achievement: developed a predict model learners’ future 
activity in the MOOC 
 Benefits: it could be helpful to design the course to support 
individuals. 
14 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Ranking forum 
threads 
 Learning about social learning in MOOCs: From statistical 
analysis to generative model (Brinton et al., 2013) 
 Objective: ranking forum threads based on learners’ behavior 
for each learner 
 Target: MOOC learners who get engaged in discussion forums 
 Benefits: helping learners to deal with overloaded information 
on forums and sharp decline rate of forum 
15 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Forum threads 
recommendation 
 Forum Thread Recommendation for Massive Open Online Courses 
(Yang et al., 2014) 
 Aim: dealing with rapidly increasing number of forum threads 
 Objective: recommending right forum threads to each learner 
 Target: MOOC learners who get engaged in discussion forums 
16 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Literature Survey: Automatic 
assessment generation 
 Automatic Generation of Assessment Objects and Remedial 
Works for MOOCs (Miranda et al., 2013) 
 Objective: providing pedagogy-based guided quizzes and 
giving personalised learning path regarding the evaluation of 
assessments 
 Benefits: 
 less effort for instructors in the assessment authoring phase 
 could fill the lack of a one-to-one tutoring 
 could mitigate the drop-out problem in MOOCs 
17 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Mendeley Group 
 Created a Mendeley group to 
share papers related to 
personalisation of MOOCs 
and personalisation in other 
e-Learning environments 
 Helpful to meet people in the same area and 
share useful papers 
 http://www.mendeley.com/groups/4715311/moo 
c-personalisation 
18 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Approach 
 Demonstrating the focus of my research and possible instruments 
will be used 
Registration 
 Socialising MOOCs 
 Twitter 
 Discussion forums 
 Gamifying MOOCs 
 Badges 
 Leaderboards (Dashboards) 
 Personalising MOOCs 
 Recommendation system 
& 
Log-in 
Course(s) 
selection from 
the course list 
Course 
selection among 
selected 
courses 
Lecture 
selection in 
the course 
Assessment 
Discussion 
forums 
Lecture 
content 
delivery 
19 
Feedback 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Socialising MOOCs 
Address the research question: 
• building a personal network for each learners, which includes 
people whom with share common interests 
 Promoting learners to use forum and Twitter hashtags 
 Connecting learners if they interacted each other at least one 
time 
 Three types of interacting: 
 Liking someone’s comment on Future Learn or Twitter 
 Replying someone’s comment on Future Learn or Twitter 
 Following someone’s profile on Future Learn or Twitter 
20 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Personal Network 
 Benefits 
 Learners could find people whom they 
may be interested in and build a 
personal network 
21 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Gamifying MOOCs 
Address the research question: 
• maintaining learners’ motivation during the course 
• making learners more satisfied with their experience of MOOCs 
 Three kinds of badges 
 Based on activeness on social platforms 
 Based on scores on quizzes 
 Based on completion rates of course materials 
 Leaderboards for all those three types 
 Benefits 
 Learners could get motivated to study when they see their fellows. 
 Learners could be more happy with their MOOC experience. 
22 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Personalising MOOCs 
Address the research question: 
• building a personal network for each learners, which includes 
people whom with share common interests 
• identifying digital resources in which learners are interested 
• making learners more satisfied with their experience of MOOCs 
 Recommending a learner conversations (on Future Learn or 
Twitter) which his/her connected learners involved 
 Benefits 
 Could help learners to find people or information which are relevant 
to themselves 
23 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Indicative Sketch of the Proposed 
Tool 
 Possible screenshot showing information to be presented to 
learners 
Your network 
Feedback 
Conversation 
recommendations 
Profile 
★Badge★ 
Name 
Other courses 
A visual 
representation of 
the learner’s 
network 
Course name 
Progress 
A list of other 
enrolled courses 
Feedback to the 
system 
Twitter 
FL profile 
Leaderboard 
The list of the 
most active 
learners (and 
links to their 
profile) 
The most 
successful 
learners in 
assignments 
Learners who 
completed 
higher numbers 
of the course 
materials 
Recommended 
conversations from 
the course forum 
Recommended 
conversations from 
Twitter 
24 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Future Plan 
 2014-2015 
 Clarifying the techniques that are utilized in the research 
 Writing 1 conference and 1 journal paper (in the first 6 months) 
 Evaluating the techniques (in the second 6 months) 
 Taking academic English classes offered by the university 
 Taking other compulsory lectures 
 2015-2016 
 Experimenting the system technically 
 Experimenting the system in practice 
 Evaluating results 
 Writing findings to journal(s) and conference(s) 
 Writing up 
25 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
Conclusion 
 Summarised the first year research to contribute to studies on 
personalising MOOCs by applying social and gamified features 
 Clarified the following years’ plans 
26 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
+ 
27 
Thank you very much for your patience! 
 
Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation

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Personalisation of MOOCs

  • 1. + First year report Ayşe Saliha Sunar The University of Southampton Electronics and Computer Science Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 2. + Outline  My PhD interest and the first year of the PhD  Research aim and questions  Background of the study  Literature survey: personalisation of MOOCs  PhD approach  Future plan 2 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 3. + My PhD Interest  MOOCs: rapidly growing area  Growing number of learners https://www.edsurge.com/n/2013-12-22-moocs-in-2013-breaking-down-the-numbers  Growing number of researches 3 http://www.irrodl.org/index.php/irrodl/article/view/1455/2531 • Master project: on Intelligent Tutoring Systems • PhD research interest: Personalisation of MOOCs Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 4. + The first year of the PhD Oct • Orientation Nov-Feb • Read to gain fundamental knowledge about the area Mar-Jul • Clarified the idea for the PhD project • Wrote the first year report Aug • Submitted the first year report • Done deep examination as a holiday homework Sept • Resubmitted the first year report • Done the viva preparation 4 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 5. + Research Aim and Questions  To investigate viability and potential value of integration a personalised approach into a MOOC environment to increase learners’ interaction and engagement in the course subject  Can providing personalised recommendations to learners who engage with MOOCs help 1. building a personal network for each 5 learner, which includes people whom with share common interests 2. identifying digital resources in which learners are interested 3. maintaining learners’ motivation during the course 4. making learners more satisfied with their MOOC experience Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 6. + Background: MOOCs  Massive Open Online Courses Strength Weakness Widely accessible Limited interaction Low coast Pedagogy Self-paced learning Assessment Life-long learning Feedback Academic credits (optional) Decrease in motivation 6 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 7. + Background: Personalisation in e-learning systems  Personalised learning systems  Intelligent Tutoring Systems  Adaptive Hypermedia Systems INPUT(s) SYSTEM OUTPUT(s) • Adaptive content delivery • Adaptive representation of content 7 • Adaptive assessment • Personalised feedback • Recommendations • Knowledge level • Errors/Misconceptions • Motivation • Progress on tasks • Learning approach • Learners’ preferences • Systems’ pedagogical approach User Model Adaptive Model Domain Model Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 8. + Literature Survey on Personalisation of MOOCs  Demonstrating contributions of studies in different aspect of MOOC personalisation Registration & Log-in Adaptive planner (Alario-Hoyos et al. 2014) Course(s) selection from the course list Learning pathways (Bansal 2013; Marinda et al. 2013 Henning et al. 2014) Wiki and blogs (if there any) Feedback Course selection among selected courses Automatic assessment generation (Marinda et al. 2013) Learning contents (Sonwalker 2013; Nesterko, 2014) Lecture selection in the course Assessment Discussion forums Lecture content delivery Forum Thread Recommendation (Researchers from the EDGE Lab., 2013; Yang et al. 2014) 8 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 9. + Literature Survey on Personalisation of MOOCs  Summarising motivation and targets of the current researches Subject Motivation Target learners Study plan Helping learners to arrange their schedule according to priorities 9 Have lack of experience in studying Learning materials & pathways Designing suitable learning materials and pathways to each individual in a diverse MOOCs learners’ community Actually planned but did not finish the course they enrolled in Learning contents Designing contents based on learners’ goal and learning style All types of MOOC learners Forum threads Helping learners to find useful threads among overloaded information MOOC learners who get engaged in discussion forums Assessments Facilitating authoring assessments and giving right material to learners All types of MOOC learners Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 10. + Literature Survey: Personalisation of MOOCs tasks  Adaptive planner for facilitating the management of tasks in MOOCs (Alario-Hoyos et al., 2014)  Target: those who has lack of experience in studying MOOCs, may benefit from personalised planning and feedback to develop work habits and study skills 10 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 11. + Literature Survey: Personalisation of learning materials  Adaptive Recommendation System for MOOC (Bansal, 2013)  A Project Stage Report for a PhD Thesis  Objective: providing the recommendation/feedback of some tasks to complete based on learners’ activities in the current week before the start of the next week  Target: all types of MOOC learners  Benefit: learners would be aware of the concepts they are lacking and have a chance to recover it by getting some recommended tasks. 11 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 12. + Literature Survey: Personalisation of learning pathways  Personalized Web Learning: Merging Open Educational Resources into Adaptive Courses for Higher Education (Henning et al., 2014)  The outline of the study is represented.  Objective: recommending personal learning pathways for each learner  Target: learners who actually planned but did not finish the course they enrolled in  Benefit: high dropout rates would decrease. 12 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 13. + Literature Survey: Personalisation of learning contents  The First Adaptive MOOC: A Case Study on Pedagogy Framework and Scalable Cloud Architecture—Part I (Sonwalker, 2013)  Aim: improving pedagogical effectiveness of MOOCs  Objective: adapting the content of learning materials to the way a learner would like to learn  Target: all types of MOOC learners 13 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 14. + Literature Survey: Personalisation of learning contents  MOOC Research Initiative - Final Report Project Title: MOOCs Personalization for various Learning Goals (Nesterko, 2014)  Project: funded by The Bill and Melinda Gates foundations and the project leader is Dr. Sergiy Nesterko from HarvardX Research Fellow.  Achievement: developed a predict model learners’ future activity in the MOOC  Benefits: it could be helpful to design the course to support individuals. 14 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 15. + Literature Survey: Ranking forum threads  Learning about social learning in MOOCs: From statistical analysis to generative model (Brinton et al., 2013)  Objective: ranking forum threads based on learners’ behavior for each learner  Target: MOOC learners who get engaged in discussion forums  Benefits: helping learners to deal with overloaded information on forums and sharp decline rate of forum 15 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 16. + Literature Survey: Forum threads recommendation  Forum Thread Recommendation for Massive Open Online Courses (Yang et al., 2014)  Aim: dealing with rapidly increasing number of forum threads  Objective: recommending right forum threads to each learner  Target: MOOC learners who get engaged in discussion forums 16 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 17. + Literature Survey: Automatic assessment generation  Automatic Generation of Assessment Objects and Remedial Works for MOOCs (Miranda et al., 2013)  Objective: providing pedagogy-based guided quizzes and giving personalised learning path regarding the evaluation of assessments  Benefits:  less effort for instructors in the assessment authoring phase  could fill the lack of a one-to-one tutoring  could mitigate the drop-out problem in MOOCs 17 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 18. + Mendeley Group  Created a Mendeley group to share papers related to personalisation of MOOCs and personalisation in other e-Learning environments  Helpful to meet people in the same area and share useful papers  http://www.mendeley.com/groups/4715311/moo c-personalisation 18 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 19. + Approach  Demonstrating the focus of my research and possible instruments will be used Registration  Socialising MOOCs  Twitter  Discussion forums  Gamifying MOOCs  Badges  Leaderboards (Dashboards)  Personalising MOOCs  Recommendation system & Log-in Course(s) selection from the course list Course selection among selected courses Lecture selection in the course Assessment Discussion forums Lecture content delivery 19 Feedback Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 20. + Socialising MOOCs Address the research question: • building a personal network for each learners, which includes people whom with share common interests  Promoting learners to use forum and Twitter hashtags  Connecting learners if they interacted each other at least one time  Three types of interacting:  Liking someone’s comment on Future Learn or Twitter  Replying someone’s comment on Future Learn or Twitter  Following someone’s profile on Future Learn or Twitter 20 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 21. + Personal Network  Benefits  Learners could find people whom they may be interested in and build a personal network 21 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 22. + Gamifying MOOCs Address the research question: • maintaining learners’ motivation during the course • making learners more satisfied with their experience of MOOCs  Three kinds of badges  Based on activeness on social platforms  Based on scores on quizzes  Based on completion rates of course materials  Leaderboards for all those three types  Benefits  Learners could get motivated to study when they see their fellows.  Learners could be more happy with their MOOC experience. 22 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 23. + Personalising MOOCs Address the research question: • building a personal network for each learners, which includes people whom with share common interests • identifying digital resources in which learners are interested • making learners more satisfied with their experience of MOOCs  Recommending a learner conversations (on Future Learn or Twitter) which his/her connected learners involved  Benefits  Could help learners to find people or information which are relevant to themselves 23 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 24. + Indicative Sketch of the Proposed Tool  Possible screenshot showing information to be presented to learners Your network Feedback Conversation recommendations Profile ★Badge★ Name Other courses A visual representation of the learner’s network Course name Progress A list of other enrolled courses Feedback to the system Twitter FL profile Leaderboard The list of the most active learners (and links to their profile) The most successful learners in assignments Learners who completed higher numbers of the course materials Recommended conversations from the course forum Recommended conversations from Twitter 24 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 25. + Future Plan  2014-2015  Clarifying the techniques that are utilized in the research  Writing 1 conference and 1 journal paper (in the first 6 months)  Evaluating the techniques (in the second 6 months)  Taking academic English classes offered by the university  Taking other compulsory lectures  2015-2016  Experimenting the system technically  Experimenting the system in practice  Evaluating results  Writing findings to journal(s) and conference(s)  Writing up 25 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 26. + Conclusion  Summarised the first year research to contribute to studies on personalising MOOCs by applying social and gamified features  Clarified the following years’ plans 26 Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation
  • 27. + 27 Thank you very much for your patience!  Ayse Saliha Sunar ass1a12@soton.ac.uk http://www.mendeley.com/groups/4715311/mooc-personalisation

Editor's Notes

  1. An overview of my first year studies of the PhD.
  2. This is the outline of the presentation.
  3. MOOCs are rapidly growing area. There are some researches showing the increasing number of learners and researchers. My master project was on ITS. That is why offering personal assistance to the huge number of learners is an interesting topic for the PhD study to me.
  4. This slide shows how I spent my first year.
  5. This research’s aim is to investigate viability and potential value of integration a personalised approach into a MOOC environment to increase learners’ interaction and engagement in the course subject. In order to fulfil this aim my research question includes for subpoints.
  6. This is one of the background of my study: MOOCs. MOOCs have some advantage such as being widely accessible and open to all level of learners. However, there also some weaknesses such as limited interaction, poor pedagogy and feedback.
  7. Personalisation techniques have been applied to e-learning systems for many many years. Basically, these systems use some information about learners to produce adapted outputs such as personalised sequence of learning material, personalised feedback, personalised recommendations etc.
  8. This is the literature survey I have done. Since MOOCs is a recent area, there are only few studies about the personalisation of MOOCs.
  9. This is the table showing a rough summary of the literature on personalisation of MOOCs.
  10. Adaptive planner for facilitating the management of tasks in MOOCs
  11. Adaptive Recommendation System for MOOC
  12. Personalized Web Learning: Merging Open Educational Resources into Adaptive Courses for Higher Education
  13. The First Adaptive MOOC: A Case Study on Pedagogy Framework and Scalable Cloud Architecture—Part I
  14. MOOC Research Initiative - Final Report Project Title: MOOCs Personalization for various Learning Goals
  15. Learning about social learning in MOOCs: From statistical analysis to generative model
  16. Forum Thread Recommendation for Massive Open Online Courses
  17. Automatic Generation of Assessment Objects and Remedial Works for MOOCs
  18. This is the public Mendeley group I created. All referred papers are included in this group.
  19. This slide shows the my approach. I am particularly interested in social MOOCs. I plan to use three theme: socialising MOOCs, gamifying MOOCs and personalising MOOCs.
  20. This slide simply explain what it is meant by socialising MOOCs.
  21. Imagination of personal networks. (For human icons: http://paintcity.net/?p=4289)
  22. This slide explains how gamification could be used in MOOCs.
  23. This is my approach to personalise MOOCs.
  24. Representative screenshot of the external tool for helping learners on MOOC platforms.
  25. This is the future plan for the following years.
  26. This is the conclusion of today’s presentation.
  27. Thank you.