Personalisation of MOOCs

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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

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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
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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
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http://www.irrodl.org/index.php/irrodl/article/view/1455/2531
• Master project: on Intelligent Tutoring Systems
• PhD research interest: Personalisation of MOOCs

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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
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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

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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
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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
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• 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

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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)
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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
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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

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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
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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.
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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.
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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
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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.
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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
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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
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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
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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
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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
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Feedback

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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
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Personal Network
 Benefits
 Learners could find people whom they
may be interested in and build a
personal network
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Gamifying MOOCs
• 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.
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Personalising MOOCs
• 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
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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
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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
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Conclusion
 Summarised the first year research to contribute to studies on
personalising MOOCs by applying social and gamified features
 Clarified the following years’ plans
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Thank you very much for your patience!


Personalisation of MOOCs

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