- AI has huge potential to democratize education through personalized learning techniques enabled by learning analytics and adaptive technologies.
- Personalized learning aims to tailor educational content, activities and resources to individual learners based on preferences, interests, competencies and behaviors.
- Key challenges in developing truly personalized learning include limitations of data and computing power to fully understand individual learners, as well as balancing personalization with new discovery and conflicting interests of different stakeholders.
Chatbot is an Artificial Intelligence (AI) technology that serves as a digital assistant that interprets and processes users’ requests. Existing chatbot applications for teaching and learning have addressed subjects like language, and economics, but none are available to facilitate learning AI or ability to communicate in Malay language.
Therefore, CikguAIBot, a chatbot that focuses on assisting the Malay-speaking community in learning the basic concepts and algorithms of AI is developed. The purpose of the CikguAIBot is to provide an alternative to learning materials and interaction modality with the instructor. The target user of the chatbot ranges from secondary school learners to lifelong learners. CikguAIBot is deployed as a Telegram application and executable through mobile apps and web access. The completion of learning, activities and assessments of the whole content of CikguAIBot takes about one hour.
The chatbot consists of 65 intents and 7 entities, and is developed using DialogFlow, a Google-based tool. Suggestion chips and cards are used as the interaction means which allow users to navigate from one content to another. Natural language interaction is also allowed so users can chat with the chatbot. Quizzes in the form of true-false and multi-choice questions are created within each topic as a learning reinforcement purpose. Immediate feedback to answers in the quiz is also provided so the students could use the responses as self-learning. The chatbot also offers infographic, links to external resources and videos.
Learning analytics based intelligent simulator for personalised learning slideNurfadhlina Mohd Sharef
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)
Alict evaluation of active learning materialsCorneliaBrodahl
Presentation from ALICT summer school n Kranjska Gora, Slovenia, August 5th. 2014, sponsored by Slovene Scholarship Fund EEA/NFM.
This project has been funded with support from the EEA Financial Mechanism 2009-2014 and the Norwegian Financial Mechanism 2009-2014 between the Republic of Iceland, the Principality of Liechtenstein, the Kingdom of Norway and the Republic of Slovenia. This publication (communication) is the sole responsibility of the author and in no way represents the views of the project funders.
AN AUTOMATED MULTIPLE-CHOICE QUESTION GENERATION USING NATURAL LANGUAGE PROCE...kevig
Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language
Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data.
Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming
and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for
Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are
important words in a given lesson material. To validate that the system is not perverse, five lesson materials
were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the
teacher were compared to the auto-generated keywords and the result shows that the system was capable of
extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a
user-friendly interface for easy accessibility.
Chatbot is an Artificial Intelligence (AI) technology that serves as a digital assistant that interprets and processes users’ requests. Existing chatbot applications for teaching and learning have addressed subjects like language, and economics, but none are available to facilitate learning AI or ability to communicate in Malay language.
Therefore, CikguAIBot, a chatbot that focuses on assisting the Malay-speaking community in learning the basic concepts and algorithms of AI is developed. The purpose of the CikguAIBot is to provide an alternative to learning materials and interaction modality with the instructor. The target user of the chatbot ranges from secondary school learners to lifelong learners. CikguAIBot is deployed as a Telegram application and executable through mobile apps and web access. The completion of learning, activities and assessments of the whole content of CikguAIBot takes about one hour.
The chatbot consists of 65 intents and 7 entities, and is developed using DialogFlow, a Google-based tool. Suggestion chips and cards are used as the interaction means which allow users to navigate from one content to another. Natural language interaction is also allowed so users can chat with the chatbot. Quizzes in the form of true-false and multi-choice questions are created within each topic as a learning reinforcement purpose. Immediate feedback to answers in the quiz is also provided so the students could use the responses as self-learning. The chatbot also offers infographic, links to external resources and videos.
Learning analytics based intelligent simulator for personalised learning slideNurfadhlina Mohd Sharef
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)
Alict evaluation of active learning materialsCorneliaBrodahl
Presentation from ALICT summer school n Kranjska Gora, Slovenia, August 5th. 2014, sponsored by Slovene Scholarship Fund EEA/NFM.
This project has been funded with support from the EEA Financial Mechanism 2009-2014 and the Norwegian Financial Mechanism 2009-2014 between the Republic of Iceland, the Principality of Liechtenstein, the Kingdom of Norway and the Republic of Slovenia. This publication (communication) is the sole responsibility of the author and in no way represents the views of the project funders.
AN AUTOMATED MULTIPLE-CHOICE QUESTION GENERATION USING NATURAL LANGUAGE PROCE...kevig
Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language
Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data.
Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming
and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for
Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are
important words in a given lesson material. To validate that the system is not perverse, five lesson materials
were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the
teacher were compared to the auto-generated keywords and the result shows that the system was capable of
extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a
user-friendly interface for easy accessibility.
The project aims at developing an intelligent tutoring system, to be applied in open source learning environments, able to monitor, track, analyze and give formative assessment and feedback loop to students within the learning environment, and give inputs to tutors and teachers involved in distance learning to better their role during the process of learning. The software will be developed in java thus could be easily implemented and re-used in most of the common free platforms for eLearning.
The Uses of Storytelling In Simplifying the Complex Concept in ProgrammingKhairul Shafee Kalid
The use of storytelling as a teaching tool for programming courses is explored in this presentation slide. One of the purpose of using stories in teaching is that stories simplifies complex concept. This slides contains the development of a prototype that could facilitate the process of constructing stories for programming. The stories can be use by the instructor in class to demonstrate complex programming concepts.
The project aims at developing an intelligent tutoring system, to be applied in open source learning environments, able to monitor, track, analyze and give formative assessment and feedback loop to students within the learning environment, and give inputs to tutors and teachers involved in distance learning to better their role during the process of learning. The software will be developed in java thus could be easily implemented and re-used in most of the common free platforms for eLearning.
The Uses of Storytelling In Simplifying the Complex Concept in ProgrammingKhairul Shafee Kalid
The use of storytelling as a teaching tool for programming courses is explored in this presentation slide. One of the purpose of using stories in teaching is that stories simplifies complex concept. This slides contains the development of a prototype that could facilitate the process of constructing stories for programming. The stories can be use by the instructor in class to demonstrate complex programming concepts.
A Comprehensive Overview of Higher Education Learning SolutionsDaisy Wilson
Adaptive higher education learning solutions use data analytics to deliver personalized learning experiences. The goal is to provide learners with personalized content that matches their proficiency level and learning pace.
This is the slides from a webinar I gave to the senate of Universiti Padjajaran, Inodonesia as part of the activities in discussing on AI implications in education at their institution.
KeyNote Speech
10th International Conference of Science, Mathematics & Technology Education
Mauritius Institute of Education, Reduit, Mauritius
6 November 2019
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
5 Best Ways To Use Ai Tools To Meet Students’ Needs | Future Education MagazineFuture Education Magazine
Here are 5 ways to use AI tools to meet students’ needs: 1. Personalized Learning Paths 2. Intelligent Tutoring Systems 3. Enhanced Accessibility 4. Efficient Assessment and Feedback 5. Predictive Analytics for Early Intervention
This talk was delivered for the National Defense University of Malaysia (Universiti Pertahanan Malaysia), Malaysia, in their academic staffs induction course program, delivered on 9th August 2023. The title is Regenerating learning experience with AI.
This webinar is conducted by the Centre for Academic Development and Leadership Excellence (CADe-Lead) on 14th April 2023. Here is the link to the event page https://cadelead.upm.edu.my/kandungan/olcpd2023_14_apr_ada_apa_dengan_chatgpt_tanyalah_dr_fadh-72294
This was the first session on Generative AI in teaching and learning, focusing on ChatGPT that was conducted in Malaysia. The event was organised by the Centre for Academic Development and Leadership Excellence (CADe-Lead) UPM. The YouTube video of the session is here https://www.youtube.com/watch?v=p6Zk370bxJo&t=1s
This talk is organised by HELWA ABIM to create awareness on big data and artificial intelligence. Delivered by Nurfadhlina Mohd Sharef on 5th November 2020
Basketball players performance analytic as experiential learning approachNurfadhlina Mohd Sharef
To cite: Sharef, N.M., Mustapha, A., Azmi, M.N., Nordin, R., (2020), "Basketball Players Performance Analytic as Experiential Learning Approach in Teaching Undergraduate Data Science Course", International Conference on Advancement in Data Science, E-learning and Information Systems (ICADEIS 2020).
Enhancing Multi-Aspect Collaborative Filtering for Personalized RecommendationNurfadhlina Mohd Sharef
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
Aspect Extraction Performance With Common Pattern of Dependency Relation in ...Nurfadhlina Mohd Sharef
A. S., Shafie, Sharef, N. M., Murad, M. A. A., Azman, A., (2018), "Aspect Extraction Performance With Common Pattern of Dependency Relation in Multi Aspect Sentiment Analysis", 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP18), Kota Kinabalu, in press.
a system called natural language interface which transforms user's natural language question into SPARQL query
find related papers here https://sites.google.com/site/fadhlinams81/publication
The semantic technology enhances big data advancements by allowing sophisticated analysis of texts. Through the Linked Data technology, tremendous amount of information can be connected. However, this inherits ambiguity when it needs to be manipulated for certain purpose like natural language interface, semantic search and question answering. There are limited works which address ambiguity in semantic search. This paper introduces a technique based on self-adaptive disambiguation which utilizes the possible concept annotations of terms in the natural language queries. This will allow users to compose query in natural language and receive accurate answers without having to master the formal syntax of the semantic query language.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
2024.06.01 Introducing a competency framework for languag learning materials ...
ICADEIS 2020 keynote
1. 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
6. 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
8. 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
9. 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
11. 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
14. 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
15. 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
16. 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.
17. 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
18. 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
19. - 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
20. 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
21. 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
24. 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
25. 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)
26. 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)
27. 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)
28. 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
- Comparing marks distribution
by PO across gender and
groups within the same
semester
- Comparing marks distribution
by PO across gender, groups
and semester
- Comparing marks distribution
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
32. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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