Transcript USING EXPERT SYSTEMS TECHNOLOGY FOR STUDENT EVALUATION IN A WEB BASED EDUCATIONAL

USING EXPERT SYSTEMS
TECHNOLOGY
FOR STUDENT EVALUATION
IN A WEB BASED EDUCATIONAL
SYSTEM
Ioannis Hatzilygeroudis, Panagiotis Chountis, Christos
Giannoulis and Constantinos Koutsojannis
University of Patras, Dept of Computer Engin. &
Informatics
26500 Patras, Hellas (Greece)
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Outline
1. Introduction
2. System Architecture
3. Domain Knowledge
4. Learning Process
5. Domain Knowledge Management
6. Student Evaluation
7. Helping the Tutor
8. Implementation issues
9. Conclusion
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1. Introduction

Web-based intelligent educational systems (WBIESs)
use Artificial Intelligence (AI) Techniques in order to
adapt mainly to student needs for self-study.
 An important function of such systems is student
evaluation, which provides results for adapting to
both the students and the tutor. There are many
techniques used for student evaluation.
 However, a few of them use an expert system
approach
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Introduction (2)

To help the students and the tutors in our
Department, we constructed an intelligent e-learning
system to assist learning and teaching in the context
of the course of Artificial Intelligence (course 451).
 The system provides means to the tutors for
constructing questions and tests in a structured way.
Also, with the help of an expert system, evaluates
the students and provide useful statistics to the tutor.
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2. System Architecture
Identification
Unit (IU)
Stereotypes
Tutoring
Unit (TU)
Student
Model (SM)
The system consists of five
units:
Expert
System (ES)
Evaluation
Unit (EU)
Virtual
Agent (VA)

the Identification Unit (IU),
 the Student Model (SM),
 Stereotypes for initial
student profile
 the Tutoring Unit (TU),
 the Evaluation Unit (EU)
and
 the Virtual Agent (VA)
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System Architecture(2)
•SM contains all the information about students, like their preferences,
interests, knowledge level, etc
•TU is responsible for the teaching process. A student can select a learning
goal from the domain knowledge tree
•The main goal of EU is to evaluate student's progress due to his/her
interaction with the system. This evaluation is achieved through testing.
•VA has as main goal to motivate the students and improve system's
effectiveness. In our case, VA is an avatar (called Merlin) that provides
information to the students in a natural and recreating way. To achieve this,
VA gets information from the SM and communicates with the student in the
same way that a human tutor would do.
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User Interface
Content
area
Info-bar
Navigation
area
Start – Previous - next
www.mmlab.ceid.upatras.gr/aigroup/undergrad/
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3. Domain Knowledge
The domain knowledge tree, as far as the
subsections level, is displayed in the
navigation area of the user interface (at
the left side of the screen).
Subject
…
Chapter 1
Section 1
Subsection 1
…
Concept 1
Chapter N
…
Section N
Subsection N
…
Concept N
From that tree the student can choose a
learning goal (subsection).
Each subsection corresponds to a learning
page, which is a php page. That is, only
subsections correspond to displayable
material.
The learning page of the selected
subsection is currently presented in the
content area (which resides at the centre
and the right part of the screen).
Each learning page deals with a number of
concepts.
Each concept is linked to the corresponding
concept page. Concept pages constitute
the real teaching material.
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Domain Knowledge(2)

The teaching material, apart from concept pages,
however, includes all the available questions,
which are stored in a database and are used for
the creation of the tests.
 Each learning page is associated with a test.
 Each test consists of a number of multiple choice
type questions that examine the concepts of the
associated learning page.
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4. Learning Process



The learning method (implicitly followed) is based on
the traditional theory-examples-exercises paradigm
(although the user can follow his own method).
That is, for each concept, the theory is first presented.
Then, some examples are given. Finally, the student is
called to make some exercises.
The student is also not forced to follow the system’s way
of teaching, but he can make his own choices for
studying. For example, one can jump to complex
concepts without having had a look at simpler ones. In
many concept pages there are links to other concepts that
are prerequisite to the concept of the page.
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5. Domain Knowledge
Management
The system offers to the tutor the capability of domain knowledge
management. The tutor can deal with management of the domain tree
and the teaching material (learning pages, concept pages, questions
and tests).
The most important part of teaching material management is the
management of the questions. A question is considered as a
structured object that consists of the following parts:

the name of the question

the body of the question,

the concept it concerns (as well as its corresponding path in the
domain tree),

its difficulty level (one of easy, medium and difficult),

its possible answers (1 to 4) each followed by a mark and an
explanation and

a hint (to help the student to answer correctly).
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Screen for Question Creation
(Chapter)
(Section)
(Subsection)
(Concept)
(Difficulty
level)
Answer1-4
Mark1-4
Explanation1-4
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6. Student Evaluation
A student can be evaluated at two levels:
(a) the concept-level and
(b) the topic-level.
The concept-level evaluation deals with the level of
understanding of the concepts of a learning page test,
whereas the topic-level evaluation deals with the level of
understanding of the topic of a learning page, i.e. the test
as a whole.
Student Evaluation implemented via the expert system (ES).
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The structure of the ES
KB
Fact
Base
Rule
Base
JESS
Inference
Engine
The knowledge level of a student, as
far as both a concept and a topic
are concerned, is classified in one
of the following five categories:
(a) excellent (86-100),
(b) very good (71-85),
(c) good (51-70),
(d) average (31-50) and
(e) low (0-30).
Within the parentheses are the
corresponding ranges of the marks
to be achieved
The rules are implemented via the
expert system (ES).
ES is a rule-based expert system
implemented in Jess, which is an
expert system shell.
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Rules for answer marking
Difficulty Level
No of tries
Use of Hint
Answer Status
Mark
easy
1
0
Correct
85
easy
1
1
Correct
70
easy
1
0
Wrong
30
easy
1
1
Wrong
0
easy
>2
medium
1
0
Correct
92
medium
1
1
Correct
77
medium
1
0
Wrong
40
medium
1
1
Wrong
15
medium
2
1
Wrong
15
medium
>2
difficult
1
0
Correct
100
difficult
1
1
Correct
85
difficult
1
0
Wrong
50
difficult
1
1
Wrong
30
difficult
2
0
Correct
70
difficult
2
1
Correct
50
difficult
2
0
Wrong
50
0
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Question weight distribution
Question (s)
weight(s) (/100)
Easy
(E)
Medium
(M)
Difficult
(D)
1
1
1
20 (E), 30 (M), 50 (D)
1
1
0
40 (E), 60 (M)
1
0
1
30 (E), 70 (D)
0
1
1
40 (M), 60 (D)
1
0
0
70 (E)
0
1
0
80 (M)
0
0
1
100 (D)
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7. Helping the tutor
The system can present some statistics about students,
which are the following:
 The percentage of the correct answers to questions
per difficulty level per concept per student.
 The percentage of the correct answers to questions
per difficulty level per test per student.
 The percentage of the correct answers to questions
per difficulty level per concept for all students.
 The percentage of the correct answers to questions
per difficulty level per test for all students.
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8. Implementation issues



The system is web-based, so it works only through the WWW
and cannot be downloaded and function on a student's
computer. It is implemented in .php
The VA (avatar), one of the interesting parts of the system, is
implemented in VBScript. To use the avatar, we first have to
call its object from the Windows Controller. Then we have to
connect to that object and load it. We are then able to call its
functions, show it on the screen and make it interact with the
user.
The ES is implemented in Jess, an ES cell implemented in Java.
After the student has finished a test, a fact for each individual
question of the test is saved in a file. Then the expert system is
called to evaluate the student. The expert system, after having
taken the facts from the file, returns the results in another file.
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9. Conclusions



We concentrate on the knowledge management and
student evaluation aspects of the system. Knowledge
management mainly refers to test questions
construction and management.
Student evaluation refers to the evaluation of the
knowledge level of a student with regards to taught
concepts. A rule-based expert system helps in student
evaluation. A number of statistics provided by the
system give valuable information to the tutor.
The system, as it is now, adapts to the student needs,
but not to the tutor ones. It is only adaptable as far as
the tutor is concerned. So, a further effort is to make
it adaptive to the tutor needs as well.
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Thank you
for your attention !
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USING EXPERT SYSTEMS TECHNOLOGY
FOR STUDENT EVALUATION
IN A WEB BASED EDUCATIONAL SYSTEM
Ioannis Hatzilygeroudis, Panagiotis Chountis, Christos Giannoulis and
Constantinos Koutsojannis
University of Patras, Dept of Computer Engin. & Informatics
26500 Patras, Hellas (Greece)
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