1. SMARTE (SMART MENTAL LEVEL ASSESSMENT RETRIEVAL
EDUCATION SYSTEM)
SUPERVISOR: SIR MUHAMMAD ADNAN
ABID ISLAM – CGPA:3.45
ASIM WADOOD – CGPA:3.53

2. OUTLINE
• WHAT’S ADAPTIVE E-LEARNING
• INTRODUCTION/BACKGROUND
• PROBLEM STATEMENT
• PROJECT IDEA
• HOW TO DO (TOOLS INVOLVED)

3. WHAT IS ADAPTIVE E-
LEARNING?
 ADAPTIVE E-LEARNING IS AN EDUCATIONAL METHOD WHICH USES
COMPUTERS AS INTERACTIVE TEACHING DEVICES. COMPUTERS ADAPT THE
PRESENTATION OF EDUCATIONAL MATERIAL ACCORDING TO STUDENTS'
LEARNING NEEDS, AS INDICATED BY THEIR RESPONSES TO QUESTIONS AND
TASKS
 ADAPTIVE E-LEARNING CREATES THE BEST POSSIBLE LEARNING EXPERIENCE
FOR STUDENTS BY EMULATING THE TALENTS OF GREAT EDUCATORS. THIS
IS ACHIEVED BY USING TECHNOLOGIES THAT ADAPT AND SHAPE TEACHING
TO THE NEEDS OF THE INDIVIDUAL STUDENT.
 ADAPTIVE LEARNING HAS ALSO BEEN KNOWN AS
 ADAPTIVE EDUCATIONAL HYPERMEDIA,
 COMPUTER-BASED LEARNING,
 ADAPTIVE INSTRUCTION,
 INTELLIGENT TUTORING SYSTEMS,
 AND COMPUTER-BASED PEDAGOGICAL AGENTS.

4. ACCORDING TO PARAMYTHIS, AN E-LEARNING SYSTEM IS
CONSIDERED TO BE ADAPTIVE “IF IT IS CAPABLE OF:
 MONITORING THE ACTIVITIES OF ITS USERS;
 INTERPRETING THESE ON THE BASIS OF DOMAIN-SPECIFIC
MODELS;
 INFERRING USER REQUIREMENTS AND PREFERENCES OUT
OF THE INTERPRETED ACTIVITIES,
 AND, FINALLY, ACTING UPON THE AVAILABLE KNOWLEDGE
ON ITS USERS AND THE SUBJECT MATTER AT HAND, TO
DYNAMICALLY FACILITATE THE LEARNING PROCESS.”
WE WANT TO ADD TO THIS DEFINITION THAT AN
ADAPTIVE E-LEARNING SYSTEM IS ACTING ACCORDING
THE META-KNOWLEDGE THAT SPECIFIES THE CONTEXT OF
ADAPTATION, I.E. HOW, WHERE, AND WHEN THE SYSTEM
COULD BE ADAPTED.

5. INTRODUCTION/BACKGROUND
IN THE LAST DECADE, THE ROLE OF INFORMATION
TECHNOLOGY FOR EDUCATION HAS CHANGED RAPIDLY AND
SIGNIFICANTLY WITH THE OCCURRENCE OF E-LEARNING
SYSTEMS.
E-LEARNING PLAYS A MAJOR ROLE IN DELIVERING
EDUCATIONAL MATERIAL TO THE LEARNERS.
ADAPTIVE E-LEARNING IS A NEW APPROACH THAT CAN MAKE
AN E-LEARNING SYSTEM MORE EFFECTIVE BY ADAPTING THE
PRESENTATION OF INFORMATION AND OVERALL LINKAGE
STRUCTURE TO INDIVIDUAL USERS IN ACCORDANCE WITH
THEIR KNOWLEDGE AND BEHAVIOR

6. BACKGROUND ON ADAPTIVE E-
LEARNING
• THE ADAPTATION OF THE TEACHING AND LEARNING PROCESS
CAN BE DIVIDED IN FOUR ELEMENTS, BASED ON A
HYPOTHETICAL E-LEARNING SYSTEM, AS DESCRIBED BELOW
(MODRITSCHER ET AL., 2004):
ADAPTIVE CONTENT AGGREGATION:
ADAPTIVE PRESENTATION
ADAPTIVE NAVIGATION
ADAPTIVE COLLABORATION SUPPORT

7. • DALL’ACQUA (2009) PROPOSED A MULTIDIMENSIONAL DESIGN
MODEL, DESCRIBING THE SPECIFICATIONS NEEDED FOR AN
EDUCATIONAL ENVIRONMENT AND EXAMINED THE
CONDITIONS THAT MAKES A LEARNING ENVIRONMENT
“ADAPTIVE”.
• DEKSON AND SURESH (2010) CONDUCTED A SURVEY ON
THE VARIOUS MODELS OF ADAPTIVE CONTENT DELIVERY
SYSTEM AND PROPOSED NEWER METHODS OF DELIVERING
ADAPTIVE CONTENT FOR ADAPTIVE E-PORTFOLIO SYSTEM
• MUSTAFA AND SHARIF (2011) PRESENTED AN APPROACH TO
INTEGRATE LEARNING STYLES INTO ADAPTIVE E-LEARNING
HYPERMEDIA SYSTEM AND ASSESSED THE EFFECT OF
ADAPTING EDUCATIONAL MATERIALS INDIVIDUALIZED TO THE
STUDENT‟S LEARNING STYLE.

8. STATEMENT OF THE PROBLEM
• THANKS TO SOPHISTICATED SEARCH ENGINES LIKE GOOGLE,
FINDING INFORMATION ABOUT ANYTHING HAS BECOME AN
EFFORTLESS AND UNCOMPLICATED PROCESS. BUT SOMETIMES
FINDING THE RIGHT TYPE OF INFORMATION IS DIFFICULT, I.E.
INFORMATION THAT IS CREDIBLE AND TECHNICALLY SOUND. BEING
AN IIT STUDENT MYSELF I SOMETIMES FIND IT DIFFICULT TO FIND
EXACTLY WHAT I’M LOOKING FOR.
• ALL THE E-LEARNING SYSTEMS THAT ARE AVAILABLE TODAY ARE
MADE WITH THE ASSUMPTION THAT EVERY STUDENT HAS THE
SAME SKILLS OR HAS THE SAME GRASPING CAPABILITIES
• QUESTIONS
• CAN AN E-LEARNING SYSTEM BE AS GOOD AS OR BETTER THAN A
CONVENTIONAL FACE-TO-FACE CLASS ROOM ENVIRONMENT?
• WHAT ROLE WILL IT AND TECHNOLOGY PLAY IN SHAPING THE
FUTURE IN EDUCATION?

9. CURRENT ON-LINE LEARNING
Course Delivery System
One size fits all
Students / Learners

10. PERSONALIZED
Students / Learners
Adaptive Engine
Course Delivery System
Tailored for each
learner
Data
Individual
Recommendations

11. SMARTE: PROJECT IDEA
• The SMARTE is an web-based learning application that can
be used with any course. It will have a tutoring module that
students would use to
• Learning online lectures,
• A question-bank,
• And a testing module that selects the questions to be
presented to the student.
• In addition, there would be student, teacher and
administrated interfaces. The application would also
process the student responses to provide them with
adapted tutorials.

12. PROJECT AIM AND OBJECTIVES
• THE AIM OF THIS PROJECT IS TO CREATING A E-LEARNING SYSTEM THAT CAN ADAPT
TO THE STUDENTS SKILLS AND KNOWLEDGE.
• SMARTE MUST ADDRESS THE FOLLOWING NEEDS OF ITS USERS:
• SMARTE WILL BE RESPONSIVE
o STUDENTS
 CAN KEEP TRACK OF SCORES OF ALL TESTS WITH THEIR OWN GRADE BOOK
 CAN SEE PROGRESS ANYTIME
 REPORT FOR STUDENTS INCLUDES TEST SCORES AND CLASS RANKINGS
• TEACHERS CAN VIEW THE TEST SCORES OF A PARTICULAR STUDENT OR THE ENTIRE
CLASS
• SMARTE WILL KEEP TRACK OF INDIVIDUAL PROFILES FOR EACH USER
• SMARTE SHOULD BE ABLE TO IDENTIFY IF THE CLASS HAS PROBLEM AREAS IN
COMMON AND
THUS PROVIDE FEEDBACK REPORT TO TEACHER
• SMARTE WILL FOCUS ON WEAK AREAS OF ITS USER.
• SMARTE WILL PROVIDE THE ANNOUNCEMENT FEATURE FOR THE TEACHERS.

13. HOW TO DO
• TOOL THAT WE USE TO DEVELOP THE APPLICATION
- SERVER SIDE(PHP,MYSQL,AJAX)
- CLIENT SIDE(HTML(HTML5),CCS(CSS3),JQUERY,JAVASCRIPT)
• APPROACH/METHODOLOGY TO DEVELOP THE PROMISED
APPLICATION
- BAYESIAN THEOREM

14. • BAYESIAN STATISTICS ARE ABOUT THE REVISION OF BELIEF.
BAYESIAN STATISTICIANS LOOK INTO STATISTICALLY
OPTIMAL WAYS OF COMBINING NEW INFORMATION WITH
OLD BELIEFS.

15. BAYES RULE
Prior probability – personal belief or data. Input.
Likelihood – likelihood of data given hypothesis.
Posterior probability – probability of hypothesis given data.
The probability of event A, given that event B has subsequently occurred, is

16. SPAM
• OFFER IS SECRET
• CLICK SECRET LINK
• SECRET SPORTS LINK
HAM
• PLAY SPORTS TODAY
• WENT PLAY SPORTS
• SECRET SPORTS EVENT
• SPORT IS TODAY
• SPORT COSTS MONEY
• SIZE OF VOCABULARY=12
• P(SPAM)=3/8
• P(HAM)=5/8
• P(“SECRET”/SPAM)=1/3=.333
• P(“SECRET”/HAM)=1/15=.0667
• P(SPAM/”SPORTS”)= 0.1667=3/18

17. Start
Registration of
new
Student/teach
er
Login
Register
Student/teac
her
Determine
Learning
Styles
Course
selected
Pretes
t
fail
Course
allocated
pass
Prerequisite
course
Access chapter
that defined by
system
Take a
Chapter
learning
Chapter
Test
Chapte
r has
been
pass?
Learni
ng is
finishe
d?
End
pass
F
A
I
L
N
O
ye
s
prepared