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)

1. Learning-Analytics
based Intelligent
Simulator for
Personalised
Learning
Assoc. Prof. Dr. Nurfadhlina Mohd Sharef
Universiti Putra Malaysia
nurfadhlina@upm.edu.my

3. Personalised learning through meaningful experiential
learning analytic
1. Instructor must determine suitable
pedagogies that could optimize
meaningful learning experience.
2. The choice and preparation of learning
materials should be optimized as well.
3. Instructor has good competencies and
familiarity with the course.
4. The instructor should also be informed on
the learners’ profile and able to identify
the students’ learning needs.
1. Instruction in which the pace of learning
and the instructional approach are
optimized for the needs of each learner
where the learning objectives,
instructional approaches, and
instructional content (and its sequencing)
may all vary based on learner needs.
2. Learning activities are made available
that are meaningful and relevant to
learners, driven by their interests and
often self-initiated.
Characteristics Actions

4. Objectives

5. Why is it important to conduct Learning Analytics?

6. Intelligent Simulator for Personalised Learning (ISPerL)
Benefits and purpose of ISPerL for meaningful learning analytic can be
further described as follows:
a. Plan a personalized lesson
i. Plan upcoming lesson and get prediction on the satisfaction
score
ii. View current course summary and past lesson
information
b. Conduct adaptive teaching
i. View students heart rate
ii. View participation and engagement score
c. Learning scaffolding
i. Analyse students’ achievement by assessment types
ii. Analyse students’ achievement by program outcomes
iii. Analyse students’ participation and engagement
iv. Analyse students’ satisfaction
There are two chatbots developed in ISPerL.
The purpose of the first chatbot is to
provide alternative to learning materials
and interaction modality with the
instructor. The chatbot is designed in
Bahasa Melayu, which is the national
language in Malaysia. The target user of the
chatbot ranges from secondary school
learners to lifelong learners. The second
chatbot aims to provide customer service
related to academic program’s admission
enquiries.

7. 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
7

8. 8

9. 9
Examples of
Learning
Analytics
Dashboard

10. 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

11. Descriptive Learning Analytic Diagnostic Learning Analytic
Personalised Learning Facilitation through Meaningful Learning
Experience Analysis
Predictive Learning Analytic
Prescriptive Learning Analytic
● Lesson records visualization and alert on low
attendance
● Assessments record visualization and alert on low
assessment achievements
● PO achievements records visualization and alert
on low PO achievements
● Statistics on learning materials, learning activities,
satisfaction records and reaction in each lesson
● Correlate assessment achievement with
demographic profile, attendance,
participation, engagement and satisfaction
● Correlate PO achievement with
demographic profile, attendance,
participation, engagement and satisfaction
● Predict grade based on attendance,
participation, engagement and satisfaction
● Predict satisfaction based on materials,
activities and student demography
● Prescribe remedial actions based on
gap in students attendance
participation, engagement, satisfaction
and achievements
● Prescribe remedial actions based on
predicted grade performance

12. Lesson
Satisfaction
Learning AnalyticReaction to Lesson
Learning Materials
Assessment Records
Descriptive Diagnostic
Predictive Prescriptive
Chatbot
Student
Lecturer and
Administrator
Intelligent Simulator for
Personalised Learning
(ISPerL) model)
Version
1

13. 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)
Version
2

14. For Learners For Instructors
- Pre-lesson execution
- Planning of lesson
- Prediction of satisfaction score
- Prediction of grade
- During lesson execution
- Average heart rate of class
- View heart rate of each student
- Participation and engagement by each
students
- Participation and engagement alert of
class
- Post-lesson execution
- Analysis of participation, engagement,
heart rate, satisfaction rating and
achievement
ISPerL Features
For Instructors and Administrators
- Descriptive analytic of course,
lesson and student info
- Diagnostic analytic on course
performance by gender, PO and
assessment type
- Comparative analytic on course
performance across gender, PO
and assessment type, groups and
semesters
- Predictive analytic on students
and course performance
(assessment , PO for current and
cross group , semester)
- View course info
- View lesson plan
- View graph of heart beat vs
ideal heart beat
- Assign rating for content,
delivery, engagement
- View self-standing vs average
participation in the course
- View self-standing vs average
achievement in the course
- Execute chatbot

15. 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)

16. 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

19. Descriptive and predictive analytics
Pilot 1

20. POWERPOINT TEMPLATEWhirlWind | 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

21. Pilot 3

22. Guru AI Bot- teaches the basics of
artificial intelligence, in Malay language.
Modifying current chatbot based on input
from pilot study - 50%
Development of questionnaires - 100%
User study setup - 50%
FSKTMBot - customer service
chatbot that entertains admission
inquiries.
Development of chatbot - 100%
Development of questionnaires - 100%
User study setup - 40%
1. What? A virtual assistant for the Artificial Intelligence (AI)
subject
2. Scope? Basic information on AI
3. Who? Anyone who likes to learn about the basic of AI
4. Language? Malay
1. What? A virtual assistant for ADMISSION inquiries into Faculty
of Computer Science and Information Technology, UPM.
2. Scope? Information (Programme, Requirement, Fees, Contact
Us)
3. Who? Future and current students that seeking for basic
information of the FCSIT
4. Language? English

23. Version 1
Version 2

24. A customer service chatbot that
entertains admission inquiries.
FSKTMBot
1. What? A virtual assistant for ADMISSION inquiries into
Faculty of Computer Science and Information Technology,
UPM.
2. Scope? Information (Programme, Requirement, Fees,
Contact Us)
3. Who? Future and current students that seeking for basic
information of the FCSIT
4. How? Rule-based Natural Language Processing using
DialogFlow framework
5. Language? English

25. Thank you from us..
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)
Please visit our website https://qrgo.page.link/7EEdp