ICADEIS 2020 keynote

Assoc. Prof. Dr. Nurfadhlina Mohd Sharef
Universiti Putra Malaysia, Malaysia
nurfadhlina@upm.edu.my
Becoming a Super
Educator: the Quest of
Artificial Intelligence in
Personalising Learning
Keynote speech
International Conference of Advancements in Data Science, e-Learning and Information Systems
(ICADEIS’20)
20th October 2020

The way forward
Personalised
learning
- -
01
04.
Outline
Artificial
Intelligence
02
Learning
Analytics
03.

The quest of a Super Educator
5

Personalised learning concept
Teaching the teachers
Learner-centered design
Boost student engagement
Better learning experience
Recommended based on
preferences and competencies
Relevant and surprising,
timely and well-aged content
6

Source: https://www.netsolutions.com/insights/wp-content/uploads/2018/07/Dynamic-Personalization-to-
create-image-of-different-personas-to-serve-them-personalized-user-experience-1.jpg
7

Personalised
learning
techniques
Adaptive learning
technology used to assign
human or digital resources to
learners based on their
unique needs
Individualized learning
the pace of learning is adjusted
to meet the needs of individual
students
Differentiated learning
the approach to learning is
adjusted to meet the needs of
individual students
Competence-based learning
learners advance through a
learning pathway based on their
ability to demonstrate
competency
Adapted from https://medium.com/swlh/personalized-
learning-through-artificial-intelligence-b01051d07494
8

Computer-driven adaptive
learning - branching the learning path
based upon responses the student makes.
Adaptive learning, while it has
provided an important step forward in
helping to assure that all learners
get the material that they need to
achieve learning outcomes, has
fallen short in cultivating full
engagement with the individual
student.
That's where personalized
learning takes the next step.
Personalization
algorithms that influence
what you’ve chosen
yesterday, what you
choose today and what
you’ll be choosing
tomorrow. 9

The real power that AI brings to
education is connecting our
learning intelligently to make us
smarter (in the way we
understand ourselves, the world
and how we teach and learn)
(Professor Rose Luckin, UCL
Knowledge Lab - Dr Who of AI in
Education)
11

Source: https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcRoo6I7D8ADk2tl3zfi9XsEoLprd-wkQbHQ_Q&usqp=CAU

Ref: Luckin, Rosemary & Holmes, Wayne. (2016).
“Intelligence Unleashed: An argument for AI in Education”,
Open Ideas, London
One-to-one tutoring is untenable for all
students. Not only will there never be
enough human tutors; it would also
never be afordable.
All of this begs the question: how
can we make the positive impact of
one-to-one tutoring available to all
learners across all subjects?
AIEd can provide an intelligent, personal
tutor for every learner.
AIEd system showing a simplifed picture of
a typical model-based adaptive tutor.
14

Human derived
Static pace
Static recommendation
Limited routes
Standard clothes sizing
Rules-based
personalization human-derived and have to be
explicitly programmed into the
software
learners are grouped into cohorts
and each cohort has its own route
explicitly telling the program what
different routes are for students,
based on what they’ve done
previously
number of routes are fixed
Not doing learning at all
15

Personalised Learning LMS
Brightspace is a cloud-based learning platform that makes online and blended learning easy, flexible and smart.
Brightspace is a quantum leap beyond traditional Learning Management Systems (LMS) – it is easy to drag-and-drop
content to create engaging courses, supports all mobile devices, has industry-leading up-time and is accessible for all
learners. Plus, Brightspace enables the future of learning with a gaming engine, adaptive learning, video
management, intelligent agents, templated interactives for course design, full support for outcomes or competency-
based learning, and actionable learning analytics.

human curation,
algorithmic
systems and
machine learning
methods don’t yet
learn or deliver
fast enough
obtaining
feedback for
optimizing the
learning
environment and
learners
themselves
system do not let
users express
themselves as
unique individuals
dissecting data
that represents
signals related to
personalization
Challenges of Adaptive Personalization
Learning to
personalise
System
limitation
FeedbackSpeed
17

Data
the algorithmic
environment only has
limited data about the user
Computing
today's fastest systems is
still slow when trying to
understand the complexity
of an individual
Interest
Conflicting interests of users,
platforms and third-party
actors: whose interests and
preferences to prioritize?
Personalization gaps
Action
desired actions vs provided
functions eg: unhappy online
experience but no way to
indicate it
Content
limited resources diversity
and volume that serves
your exact intentions or
needs
Discovery paraadox
serving based on personal
preferences vs new discovery
18

- measurement, collection, analysis and
reporting of data about learners and their
contexts, for purposes of understanding and
optimizing learning and the environments
in which it occurs” (Long and
Siemens 2011).
- can enhance understanding of learning
behaviors (Wong and Chong 2018); provide
useful suggestions for policymakers,
instructors, and learners (Hwang et
al. 2014); and help educational practitioners
to improve teaching and learning
effectiveness (Bienkowski et al. 2012). Analyze Your Lesson To Discover More About Your Students
Learning Analytics
19

Types of Learning Analytics
“What has already
happened?”
Visualization of
learning patterns
“Who could be at
risk?”
Prediction of low
achievement or
engagamenet
“What should we do?”
Recommended action
for further teaching and
learning
Descriptive
Predictive
Prescriptive
20

Which algorithms and features best predict
the end of term academic performance of
students by comparing different
classificationalgorithms and pre-processing
techniques and whether or not
academicperformance can be predicted in
the earlier weeks using these features and
theselected algorithm
21

22
Examples of
Learning
Analytics
Dashboard

Features
Students Really
Expect from
Learning
Analytics
● time spent online
● collaborative learning with friends and colleagues
● learning recommendation for successful course completion
● prefer self/independent learning rather than conventional
classroom setting
● timeline showing current status and goal
● time needed to complete a task or read a text
● prompts for self-assessments
● further learning recommendations
● comparison with fellow students
● considering the students personal calendar for appropriate
learning recommendations
● newsfeed with relevant news matching the learning content
● revision of former learning content
● feedback for assignments
● reminder for deadlines
● term scheduler, recommending relevant courses

To cite:
Sharef, N. M., et. al (2020), “Learning-
Analytics based Intelligent Simulator for
Personalised Learning”, International
Conference of Advancements in Data
Science, e-Learning and Information
Systems (ICADEIS’20)

ProfilingLearning preferences
Learning competency
Learning behavior
- System
participation log
- Heart rate
Learning satisfaction
Learning Personalization
Analytics
Descriptive
(preferences, satisfaction,
competency, achievement)
Diagnostic-learning plan
adaptability
(preferences-behavior,
satisfaction-achievement)
Predictive-learning plan
recommendation
(performance, satisfaction)
Prescriptive-Adaptive
learning plan
(performance, satisfaction)
Assessment records
Learning Materials
Learning dashboard
Learning engagement
Learning achievement
Satisfaction prediction
Performance prediction
Learning competency
Customization
Model of Intelligent Simulator for Personalised Learning (ISPerL)

Features:
1. View
- Course Info
- Course Assessment
- Course PO
- Comparison between
semesters
- Analysis of heart rate
1. Plan a new lesson &
view lessons for current
semester
2. Get prediction of content,
delivery and engagement
satisfaction
Intelligent Simulator for Personalised Learning
(ISPerL)

Descriptive Learning Analytic of Learning Outcome distribution
- Comparing grade distribution
across groups within the
same semester
- Comparing marks distribution
by PO across groups within
the same semester
by PO across gender and
groups within the same
semester
by PO across gender, groups
and semester
by PO across gender, groups
and semester of each grade
Showcase: https://public.tableau.com/profile/nurfadhlina.mohd.sharef#!/vizhome/LearningAnalytic-Course1/Story-
LearningAnalytic?publish=yes

Descriptive and predictive analytics
Pilot 1

POWERPOINT TEMPLATE
WhirlWin
d
| Email : example@example.com | Web :
www.example.com
This is a sample text, Insert your desired text here this is a sample text.
Pilot 2

Temporal Integration in Recommendation System
Al-Hadi, I. A. A.,
Sharef, N. M.,
Sulaiman, M. N.,
Mustapha, N., (2018),
“Temporal based
Factorization
Approach for Solving
Drift and Decay in
Sparse Scoring
Matrix", Advances in
Intelligent Systems
and Computing, pp.
340-350
Al-Hadi, I. A. A., Sharef,
N. M., Sulaiman, M. N.,
Mustapha, N., (2017),
“Review Of The
Temporal
Recommendation
System With Matrix
Factorization”,
International Journal of
Innovative Computing,
Information and
Control, 13(5), pp. 1579-
1594
Al-Hadi, I. A. A.,
Sharef, N. M.,
Sulaiman, M. N.,
Mustapha, N.,
(2016), "Ensemble
Divide and Conquer
Approach to solve
the rating scores’
deviation in
Recommendation
System", Journal of
Computer Science,
12(6), pp. 265-275
VS VS
34
Al-Qasem, A. I. A.,
Sharef, N. M.,
Sulaiman, M. N.,
Mustapha, N.,
(2018), "Latent
based temporal
optimization
approach for
improving the
performance of
collaborative
filtering", PeerJ
Computer Science

Khairudin, N., Sharef, N. M., Mustapha, N.,
Noah, S A. M., (2018), "Embedded Learning
For Leveraging Multi-Aspect In Rating
Prediction Of Personalized
Recommendation", Malaysian Journal of
Computer Science , Dec 2018, 31-47.
Khairudin, N., Sharef, N. M., Mustapha, N., Noah,
S A. M., (2018), "Enhancing Multi-Aspect
Collaborative Filtering for Personalized
Recommendation", 2018 Fourth International
Conference on Information Retrieval and
Knowledge Management (CAMP18), Kota
Kinabalu.
35
Multi-Aspect Recommendation System

Reinforcement Learning
Deep Recurrent Q-Network Approach for Multi Objective Markov Decision
Process in Partially Observable Environment , ASIAN OFFICE OF AIR FORCE R&D
36
Keat, E. Y.,
Sharef, N. M.,
Kasmiran, K. A.,
Yaakob, R.,
(2020), "LSTM
Based Recurrent
Enhancement of
DQN for Stock
Trading", 2020
IEEE
Conference on
Big Data &
Analytics

Opportunities and way
forward
1. Understand that each user is different and designing
learning with individual learner’s specific needs in mind
2. Accommodate personal preferences and learning to
provide personalized content and its presentation
3. ML to drive the overall effectiveness of an individual
learner journey
4. ML to provide to provide specific content based on a
learner’s past performance and individual goals
5. Learning components, including the pace of learning,
personal preferences, instructional design, and learning
styles, are adjustable to meet the needs of learners
6. Learners get control to access learning resources at their
pace and convenience
7. Predict learning outcomes, enabling to deliver content that
measures up to individual learner’s goals and past
performance
8. Deliver customized and just-in-time teaching for your
learners
9. Track the previous performance of individual learners,
identify gaps and easy to deliver one-size-fits-one learning
10. Generate new content for use
37

3
1
2
4
5
AI is bound to also
impact on
educational
technology
huge potential
of AI to
democratise
education
technology will
improve, not
diminish, the
role of humans
in teaching
strong ethical
foundation,
education and
regulation
mechanism
focus more on
what the
technology can
achieve vs what
the technology is
38
Conclusions

References
● Lee, L. K., Cheung, S. K. S., Kwok, L. F., (2020),
“Learning analytics: current trends and
innovative practices”, Journal of Computers in
Education, vo. 7, pp. 1–6
● https://elearningindustry.com/learning-analytics-
analyze-lesson
● https://www.century.tech/news/no-nonsense-guide-
to-ai/
● Machine Learning and Human Intelligence: the
future of education in the 21st century (Rose
Luckin)
● https://www.wizcabin.com/personalized-learning-
through-artificial-intelligence/
● https://edwiser.org/blog/6-best-moodle-reporting-
plugins-for-learning-analytics-in-2020/
● https://www.umass.edu/it/support/moodle/individu
al-profile-reports
● https://www.emerald.com/insight/content/doi/10.1108/
ITSE-05-2018-0026/full/html
● https://uxdesign.cc/progressive-personalization-
designing-a-better-personalized-experience-
3b2f0fd392e4
● https://techcrunch.com/2015/06/25/the-future-of-
algorithmic-personalization/
● https://techcrunch.com/2015/09/19/is-personalized-
discovery-a-feature-category-or-newparadigm/
● https://elearningindustry.com/benefits-of-artifcial-
intelligence-in-personalized-learning
● Akçapınaret al., (2019), “Using learning analytics to
develop early-warning system for at-risk students”,
International Journal of Educational Technology in
Higher Education,16(40)
39

Special thanks to the Ministry of Higher Education Malaysia and my wonderful team
members in the project entitled Enhancing Education for Human Capital Development
through Establishing Future Learning Ecosystem, Malaysia Research University Network
Grant, Ministry of Education Malaysia, 2018-2021
40

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