Personalisation of MOOCs

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First year report

Ayşe Saliha Sunar The University of Southampton Electronics and Computer Science

Ayse Saliha Sunar [email protected] 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


3+My PhD Interest

Growing number of learners


https://www.edsurge.com/n/2013-12-22-moocs-in-2013-breaking-down-the-numbers

Growing number of researches

MOOCs: rapidly growing area

http://www.irrodl.org/index.php/irrodl/article/view/1455/2531

• Master project: on Intelligent Tutoring Systems • PhD research interest: Personalisation of MOOCs

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


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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 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


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


7+Background: Personalisation in e-learning systems

Personalised learning systems Intelligent Tutoring Systems Adaptive Hypermedia Systems

• Adaptive content delivery

• Adaptive representation of content

• Adaptive assessment

• Personalised feedback

• Recommendations

• Knowledge level• Errors/

Misconceptions• Motivation • Progress on tasks• Learning approach• Learners’

preferences • Systems’

pedagogical approach

User Model

Adaptive Model

DomainModel

INPUT(s) SYSTEM OUTPUT(s)


8+Literature Survey on Personalisation of MOOCs

Registration &

Log-in

Course(s) selection from the

course list

Wiki and blogs (if there

any)

Feedback

Course selection among

selected courses

Automatic assessment generation (Marinda et al. 2013)

Adaptive planner (Alario-Hoyos et al. 2014)

Learning pathways(Bansal 2013; Marinda et al. 2013Henning et al. 2014)

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)

Demonstrating contributions of studies in different aspect of MOOC personalisation


9+Literature Survey on Personalisation of MOOCs

Subject Motivation Target learnersStudy plan Helping learners to arrange their

schedule according to priorities 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


Summarising motivation and targets of the current researches

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


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.


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.


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


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.


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


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


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


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/mooc-personalisation

19+Approach

Socialising MOOCs Twitter Discussion forums

Gamifying MOOCs Badges Leaderboards

(Dashboards)

Personalising MOOCs Recommendation system

Registration & 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

Feedback


Demonstrating the focus of my research and possible instruments will be used

20+Socialising MOOCs

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

Address the research question:• building a personal network for each learners, which

includes people whom with share common interests


21+Personal Network

Benefits Learners could find people whom

they may be interested in and build a personal network


22+Gamifying 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.

Address the research question:• maintaining learners’ motivation during the course • making learners more satisfied with their experience of

MOOCs


23+Personalising 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

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


24+Indicative Sketch of the Proposed Tool

Your network

Feedback

Conversation recommendations

Profile

★Badge★

Other courses

A visual representation of the learner’s network

Course name

Progress

Name

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


Possible screenshot showing information to be presented to learners

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 Ayse Saliha Sunar [email protected] 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


27+

Thank you very much for your patience!


Personalisation of MOOCs

Technology

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