J. Software Engineering & Applications, 2011, 4, 559-570
doi:10.4236/jsea.2011.410065 Published Online October 2011 (http://www.SciRP.org/journal/jsea)
559
An Adaptive Method Based on High-Level Petri
Nets for E-Learning
Fatemeh Omrani1*, Ali Harounabadi2, Vahid Rafe3
1Department of Computer Engineering, Arak Branch, Islamic Azad University, Arak, Iran; 2Department of Computer Engineering,
Central Tehran Branch, Islamic Azad University, Tehran, Iran; 3Department of Computer Engineering, Faculty of Engineering, Arak
University, Arak, Iran.
Received August 31st, 2011; revised September 23rd, 2011; accepted October 15th, 2011.
ABSTRACT
Adaptive learning is a new approach for e-learning systems. In comparison to traditional e-learning systems, which
present same things for all learners, these systems automatically adapt with learner characteristics. In this paper, we
are going to propose a new method for Adaptive learning, and consider adaptation from three viewpoints: 1) learner
learning style; 2) learner’s knowledge level; 3) learner’s score. Due to similarity between learning objects graph and
petri net, and In order to provide adaptive learning, we use an approach based on a high level petri net (HLPN). Also
we propose a method to evaluate performance in this system. We compare our system with a non adaptive system,
through our performance evaluating method. The results show response time for our system is less than non adaptive
system and learners finish course in a relatively shorter period of time. Since our proposed system considers individual
features of learner, we can be sure that learner would not be confused in learning materials.
Keywords: Adaptive Learning, Learning Style, High Level Petri Nets, Response Time
1. Introduction
In e-learning systems, learners are faced with consider-
able amount of information in different format. If all in-
formation is presented to learners, it would result in two
problems: First, learners would be confused with wide
informational world; and the second, the needs of the
user do not satisfy. So these problems result in educa-
tional failure and lack of motivation in obtaining knowl-
edge. So designing adaptive e-learning systems is an im-
portant issue in e-learning field. Adaptive e-learning en-
vironments are environments, which provide learning
materials based on individual learner features. Adaptive
learning environments ensure to provide effective and
efficient learning for learners. Dahbi et al. classified ad-
aptation into two categories [1]: 1) adaptive presentation;
2) adaptive navigation. The former indicates adaption in
content level. In other words, there are some features like
details of presentation, media type, etc. that, in different
situations, affect the presented content and result in hav-
ing different presented content. The latter means adapta-
tion the web links between learning objects for each
learner, is adapted by system automatically. In our pro-
posed method, both techniques are considered. Because
the presented learning material which is available for
learner, not only due to content, is adaptive with user’s
characteristics; but also due to background knowledge
level and his/her score, the path which he/she travels, can
be different. Most methods that consider knowledge level
for adapting course [2,3]; ignore learner’s improvement
in recent course. In proposed method, learner improve-
ment is assessed by checking leaner’s score. Checking
this factor increases learner’s performance and arriving to
his/her learning target in shorter time. For separating
learner from individual attribute we use color token in
color petri net. Also, for evaluating performance, we use
token with time stamp in timed petri net.
Many researchers have conducted researches in adap-
tive learning field and have used different method for
adaptive learning. For example, Semet et al. used an arti-
ficial intelligence approach, called” ant colony optimiza-
tion” [4]. In this system ants are presumed as learners,
and adaptive learning path is made by considering phe-
romone which is released by other learners. De Marcos et
al. proposed a PSO algorithm for presenting adaptive link
to user [5]. In this method, sequencing problem of the
learning object for learner, is resembled to permutCSP
problem and to solve this problem; used an agent which

An Adaptive Method Based on High-Level Petri Nets for E-Learning
560
works based on PSO. Zhu et al. used a learning activity
graph and allocated Boolean expression to every edge of
graph [6]. This expression includes required precondition
and post condition for traveling through this edge. If this
expression is evaluated correctly, corresponding edge is
traversed by learner. Chen et al. used “Item response the-
ory” for providing individual learning path for each
learner [7]. Manouselis et al. used multi-criteria decision
making for automatic presentation of learning path in
personalized environment [8].
Regarding similarity between learning object graph
and petri net, some methods in adaptive learning field use
various types of petri net. A dynamic fuzzy petri net
(DFPN) was used for increasing flexibility of tutoring
agent [9]. Tutoring agent is auxiliary software for helping
individual user. Based on individual user behavior, the
tutoring agent presents a different structure of learning
content. Chang et al. used a Behavioral Browsing model
(B2model) based on High level petri net (HLPN) which
was made for modeling and generating behavioral pattern
of students [10]. Object oriented course modeling based
on High level petri net (HLPN) was proposed by Su et al.
[11]. And one authoring tool was made based on pro-
posed model. Liu et al. proposed an approach based on
petri net for controlling learning path among learning
activities [12]. Different learners learn in different ways.
For example, someone learns by observation, someone
learns by listening, someone learns by doing practices,
someone learns by concentrating on principle, someone
learns by concentrating on application, someone persists
on “memorization” and someone prefers understanding.
To provide adaptive learning some other researches have
focused on learning style [13-15], but these methods are
not formal and don’t evaluate performance by executable
model. In comparison we use learning style factor for ad-
aptation, and propose an executable model by using for-
mal method.
We use three factors for adaptation: 1) knowledge
level; 2) learning style [16]; and 3) score. And we should
add that none of the previous methods based on Petri net,
such as [2,3], use learning style for adaptation; whereas
this is a key factor in learner satisfaction. So we should
say that this is an advantage for our method to consider it.
In addition, in our system, we proposed a method to
evaluate performance and we evaluated our system by it.
Results show, by decreasing response time; performance
increases. In evaluation section of this paper, we demon-
strate in this system, learner with low knowledge level-
by considering recent score by system can improve her/
his performance. Also we assert if system doesn’t use this
method, decrease learner performance at high knowledge
level and this would increase displeasure.
The following sections are: Section 2 which explains
definitions and primary concepts. In Section 3 we express
the proposed method. In Section 4 we describe a case
study. And then, we evaluate proposed method in Section
5. Finally, we finish paper with conclusion and future
work in Section 6.
1.1. Definition and Primary Concepts
Petri net—its primitive idea is presented by Carl Adam
Petri in 60 s—is a graphical and mathematical tool that
these mentioned characteristics make it easy to use and
easy to present [17]. Figure 1 shows a simple example of
petri net.
A petri net is a 5-tuple [10,17]:
0
(;;; ;),PNPTA WM (1)
where
1)


12
,,,
m
Ppp p is a finite set of places. A place
represents a circle, such as, p1, p2 and p3 in Figure 1.
2)


12
,, ,
n
Ttt t is a finite set of transitions. A
transition represents a bar, such as t1 in Figure 1. The in-
tersection of P and T is an empty set, while the union of P
and T is not an empty set, i.e., P ∩ T =

and T ∩ P =

.
3)




A
PTT P is a set of arcs connecting
places and transitions, such as the arrowhead from p1 to t1
depicted in Figure 1.
4)


:1,2,3,WA is a weight function, whose
weight value is positive integers. Arcs, i.e., arrowheads,
are labeled with weights. For example, in Figure 1, the
arrowhead from t1 to p3, which is labeled with 2, is de-
noted as W (p2, t1) = 2. When the weight is unity and/or 1,
the label of arc is usually omitted, e.g., W(p1, t1) = 1 is
omitted in Figure 1.
5)


0:0,1,2,3,MP is the initial marking. If there
are k tokens inside place pi, it is said that pi is marked
with k tokens. For example, in Figure 1(a), p1 is marked
with one token, which is denoted as M(p1) = 1. p2 is
marked with two tokens, which is denoted as M(p2) = 2.
If Figure 1(a) is the initial status, the initial marking is
denoted as M0(p1, p2, p3) = 1, 2, 0.
A transition t is said to be fired if all its input places pi
are marked with at least W(pi, t) tokens, where W(pi, t) is
called the firing condition of transition t. For example, in
Figure 1, the firing conditions of t1 are W(p1, t1) = 1 and
W(p2, t1) = 2. A firing transition t removes W(pi, t) tokens
p
1
p
2
p
3
t
1
2
Before firing
p
2
p
3
t
1
2
After firing
p
1
PlaceTransition Token Arc
(a) (b)
22
Figure 1. A petri net example.

An Adaptive Method Based on High-Level Petri Nets for E-Learning561
from each inpns to each
e following years later, extensions of petri net
su
e so called color is allocated to
Petri Net
any types. In this paper ,we use a
m, there is a user interface
thod
in learning object
itial node and
ch
ut place pi, and adds W(t, pj) toke
output place pj. For instance, since M(p1) = 1 and M(p2) =
2 have satisfied the firing conditions of t1 in Figure 1(a),
t1 is fired. After t1 is fired as Figure 1(b) depicts, t1 has
removed W(p1, t1) = 1 token from input place p1 of t1 and
W(p2, t1) = 2 tokens from input place p2 of t1, respectively,
and then added W(t1, p3)= 2 tokens to output place p3 of
t1.
In th
ch as Colored petri net and timed petri net are made,
which is called High level petri net(HLPN). In following,
we describe these petri nets.
1.1.1. Colored Petri Net
In colored petri net a valu
each token. Transitions use the color of input tokens for
determining the color of output tokens. Relation between
input tokens and output tokens is presented by color
consumption function or arc function that is depicted by
E. for example in Figure 2, E functions are Ef (p1, t1) = a,
Ef(t1, p2) = b. If a transition is fired, colored tokens re-
moved from input places and new color tokens are pro-
duced and placed in output places. In Figure 2(a), place
p1 is marked with two colored tokens a, b from a color set
{a, b, c}, denoted m(p1) = {a, b}. t1 is enabled as its input
arc only requires one colored token a which is available
in place p1. If fired, token a is removed from p1, two new
colored tokens b and c are generated by t1’s output arcs
and deposited in p2 and p3, respectively, as shown in
Figure 2(b).
1.1.2. Timed
Timed Petri nets have m
kind of petri net in which each token is corresponded
with a realvalued clock that is represented to token age
[18,19]. Firing condition of transitions is like regular
petri net. In addition, each arc which connects a transition
to a place is labeled with real number which presents
amount of age growth after transition firing. When a
transition is fired, age of each token which has been
added to output place of transition should be increased.
Figure 3 shows a timed petri net. Each token labeled
with a real number representing its age. Figure 3(a)
shows the age of token in input place p1, that is 2. As
shown in Figure 3(b), after firing t1, age of token which
is added to output place p2, changes to 5.
1.2. Four Learning Style
In a web based tutoring syste
which its properties are similar to teacher’s, who provides
learning materials for learner. These materials may have
different styles such as audio, video and text or combina-
tion of these styles. Drago et al. classify all these styles in
four categories and called them (VARK) styles [16]. So,
the place which is shown to learner should be appropriate
with his/her learning style. By referring to user’s profile,
we can extract the specific style that learner prefers it
more than the other style. These styles are illustrated in
Table 1. In our proposed model, we use colored token for
determining learner’s learning style. To summarize im-
plementation, we declare a 2 member set: “V” as visual
and “RW” as Read/Write style.
2. Proposed Adaptive Me
In our method, each learning object
graph [20], which can be a chapter, a section or an exam-
ple, is mapped to a place in petri net. Colored tokens are
learners, and they travel through learning object via tran-
sitions. Color of token checked by transitions and if con-
dition is true, is allowed to move to the next node. So by
this method, adaptive path is constructed.
At the beginning of the path, we use in
eck the learner’s learning style in this node by guard
function of transition which is connected to this node.
And according to individual value of learning style, we
guide learner to individual path. Then, in each path, ac-
cording to background knowledge level and obtained
score, we decide the next node.
p1a
p2
p3
b
c
p1
p2
p3
t1t1a
b
c
Colored token
(a)Before fires(b)After fires t
1
t
1
Figure 2. Colored petrin ne t.
(a) Beforefires(b) Afterfires
t
1
p
1
2@+3 t
1
p
1
@+3
p
2
p
2
5
t
1
t
1
Figure 3. Timed petri net.
Table 1. The vark style [15].
VARK
Ley absorbed with the use of
Learning
arning wa
Information is best
way
Visual Char Learn by ts, graphs, flow charts,
symbols, arrows, hierarchies seeing
Audio
Lectures, verbal tutorials, tapes, Learn by
Read/write Any text-based input and Learn by
pr
Kinesthetic Experience or pry,
e.g. role playing by doing
group discussion, speaking,
talking things through hearing
output ocessing text
actice activitLearning

An Adaptive Method Based on High-Level Petri Nets for E-Learning
562
In our methoaal-
cuonse timneed to cthe
br
d, we use token with time stmp for c
lating respe. Also, we alculate
owsing time in which a learner remains in a learning
unit, the time that a learner undertakes an assessment and
the time that a student remains in queue. Leaner arrives to
system according to a Poisson process. So the time LT, at
which the next learner will arrive is derived according to
Poisson distribution [21] which is

e,
!
n
tt
LT n


 (2)
where t refers to the time that a st
queue before using system; λ refers t
udent has stayed in
o the rate of entrance,
and since LT is used to determine the time when a learner
arrives, we have n = 1.
The browsing time that a learner stays in a learning unit
is derived according to normal distribution [21], which is
2
1
2
e,
B
AVG
BT






 (3)
2π 
where AVGB refers to the average le
learner stays in a learning unit; α is th
ngth of time that a
e variation of time
spent in a learning unit among learners; and δ represents
the standard deviation of time spent in a learning unit
among learners.
The time that a learner is involved in answering and
completing test question is derived according to normal
distribution [21], which is,
2
1A
AVG
AT
  


2
e,
2π




(4)
where AVGA refers to the average test tim
tion of time spent in a test node among l
e; β is the varia-
earners; and ρ
represents the standard deviation of time spent in a test
node among learners.
The score that a learner has earned from a learning unit
which has test; derived according to normal distribution
[21], which is,
2
1S
AVG  


2
e
Score ,
2π




 (5)
where AVGS refers to the average score o
which has test; and γ represents the standar
so
f the
fa
nse time for each learner and show it in output
te
ion
w
ve time of learner, i.e., the
tim
collector monitor” for computing re-
k box and associ-
model. In other
onse
ciate it with final
tion functions for
f a learning unit
d deviation of
a learning unit which has test; and μ is the variation of
score in a learning unit which has test among learners.
We add an integer number to time stamp of new
learner who enters to the system which represents her/his
arrive time. This integer number is generated with Pois-
n distributed function as we express in Equation (2).
After that, for each learner who browses a learning unit
we add a BT time which is computed via Equation (3). If
a learning unit has test, we add a BT time which is com-
puted via Equation (3) and an AT time which is com-
puted via Equation (4) (AT + BT). At the end of the
course for obtaining response time, we subtract arrive
time (LT) from resent model time for each learner.
In learning units (nodes) which have test we assigned a
Score which is computed via Equation (4) to each learner
who browses that learning unit. This score is one o
ctors which are used for deciding to guide learner to the
next unit.
We compute response time in two viewpoints of white
and black box. In viewpoint of black box, we calculate
only respo
xt file. Whereas, in viewpoint of white box, in addition
to response time, we show traveled path, time spent for
each middle learning unit, earned score in each test,
learning style and knowledge level for each learner.
We need several monitors for computing response time
[22]. A monitor is a mechanism in CPN tool [23,24]
which is used to observe, check and control simulat
ithout changing petri net.
In addition to colors, which show user’s features; we
need several extra colors for computing response time.
These colors are: 1) the arri
e at which the learner arrived in the system; 2) the
total amount of time that learner remains in system which
is initialized to zero; 3) the learner path that represents
unit which learner met them and initialized to null value;
4) the detail time that represents time spent in each unit
of learning path.
2.1. Response Time in Black Box
We use a “Data
sponse time from the viewpoint of blac
ated it with final transition in course
words, the response time for a learner is measured when
the token that is representative of the learner, is added to
the final place of the model. After the learner finishes
conclusion section of course; the observation function for
the monitor, measures the total response time for learner.
When final transition is fired, the total time for the
learner is bound to the one color of token, and arrive time
of token is one of token colors too. So the response time
for learner is subtraction arrive time from total time.
2.2. Response Time in White Box
We use a “write in file monitor” for computing resp
time in viewpoint of white box and asso
transition in course model. The observa
the monitor print of all colors of each token and their
response time in output text file.

An Adaptive Method Based on High-Level Petri Nets for E-Learning
563
f our proposed model.
ourse, we consider a book as depicted
-
pl
3. Case study
There are many tools for implementation of petri nets.
or simulation oWe use CPN tool f
For designing c
in Figure 4. This book includes 2 chapters (content 1,
content 2), 2 sections (content 1.1, content 2.1), 6 exam
es, introduction and conclusion sections. The Learning
objects graph [25], which is corresponded with this book
is shown in Figure 5. We can map this graph to petri net
by method which is expressed in [3]. The generated petri
net is depicted in Figure 10 and Figure 12. Three basic
color sets should be declared for our method in CPN tool:
1) learning style; 2) knowledge level; 3) score. In addi-
tion, we need 4 extra color sets: 1) color set, LT, which is
represented in arrive time; 2) color set, PATH, which is
represented in traveled path by learner; 3) color set, De-
tail Time, which is represented time spent in each middle
unit in the course; 4) Process Time in which the total
amount of time is represented that the learner stays in
system. Since the learner includes all of these features
(colors), we declare a compound color LEARNER which
is the product of all of these colors. Also, we declare a
“list color set”, named “Learners” for maintaining learn-
ers in a FIFO queue when they are entered in the system.
So, declaration of color set in CPN tool, is the one like
Figure 6. As it is said in section 3, Leaner arrives to sys-
tem according to a Poisson process. So, we use the arri-
Introduction…………………………………..
Content 1……………………………………..
Exa mp le1 …… ………… …… ………… …… .
Example 2……………………………………
Content 1.1……………………………...
Content 2…………………………………….
Example 3……………………………………
Example 4……………………………………
Example 5….…………………………………
Example 6…………………………………..
Content 2.1……………………………….
Conclusion…………………………………..
Figure 4. A sample of book structure.
vals subpaers as de-
picted in Figure 7. The time stamp of the token on next
ge to model the arrivals of new learn
place in the Arrivals page determines the time at which
the next learner will arrive. The Poisson function is used
here to generate Poisson distributed in inter-arrival times
with an average inter-arrival time of 120.
Figure 5. Learning objects graph.
Figure 6. Declaration of color set in CPN tool.
Figure 7. Arrival subpage.

An Adaptive Method Based on High-Level Petri Nets for E-Learning
564
When a new learner arrives, the newLearner() function
is used to create a token that represents a new learner.
The declaration of the newLearner() function is depicted
in Figure 8. When called, the newLearner() function re-
turns a value from the LEARNER color set, i.e. it will
return an 8-tuple as described above. The first component
is the kind of the learning style, and using the Learn-
g_Style.ran() function; it is chosen randomly
n
is guided to 2 individual paths. These 2 paths are the same
course, but their content, due to its style, are presented
with different media. For example if learner is a Vstyle
user, the media is flash files, audio and video files. So,
the main page of the model as depicts in Figure 9 have 2
substitute transitions: “RWstyle” and “Vstyle” which are
associated with 2 subpages with same name. TVstyle
uides learner to visual course; and RWstyle in
o
. The sec-transition g
d component is the kind of the knowledge level and
using the Knowledge_Level.ran(); it is chosen randomly.
The third and fourth components are scores in content 1
and 2 of book, respectively. The fifth component is arrive
time and the sixth component is the amount of total time
that learner remains in system. The seventh component is
learning path and eighth component is detail time that
learner remains in each learning unit. The type of queue
is FIFO, so the new learners are added to end of queue by
“learners^^[learner]” arc inscription as depicts in Figure
7 and enter to system from first of queue by “learner::
learners” arc inscription in main page which is the top-
level page in the model as depicts in Figure 9.
The learning style of new learner is checked before
entering to introduction node and based on its type; he/she
he
transition guides learners to text course based on their
learning styles.
fun newLearner()=
(Learning_Style.ran(),
Knowledge_Level.ran(),
0,
0,
IntInf.toInt(time()),
IntInf.toInt(time()),
"",
IntInf.toString(time()));
Figure 8. The declaration of the newlearner() function.
Figure 9. The main page.

An Adaptive Method Based on High-Level Petri Nets for E-Learning565
In both styles all learners (with any knowledge level)
should pass introduction and content 1; based on course
tutor’s suggestion. Since the content 1 is the chapter
which includes the test, the learners should test, after
passing content1 (LO1). After that―based on his/her
score and knowledge level―learner are guided to 4 dif-
ferent paths (because, in first decision making process
based on learning style, after we determine the path, there
are 2 factors which remain, so there are 22 paths). These
stages are illustrated in Figure 10 which is RWstyle
subpage. For determining which paths (from 4 paths) is
appropriate to learner; he/she enters to transition “deci-
sion making” and through this transition enters to the
decision making subpage that is depicted in Figure 11.
Figure 10. The Rstyle subpage. W
Figure 11. Decision making subpage.

An Adaptive Method Based on High-Level Petri Nets for E-Learning
566
As you can see in the Figure 11, in subpage decision
making, first system decides based on knowledge level
and next decides based on score of content 1. After that,
the learner comes back to RWstyle page. In RWstyle
page as depicted in Figure 10, for example if the
learner’s knowledge level is low and her/his score in con-
tent 1 is equal or less than 10, learner should pass exam-
ple1, example 2, content 1.1 (LO1_1), respectively.
Whereas, if the learner’s knowledge level is high and
her/his score in content 1 is more than 10, learner doesn’t
need to pass the example 1 and example 2. After passing
LO1-1, all learners (in 4 types) should pass content 2. For
this purpose, learners go to subpage LO2 by firing transi-
tion LO2. In subpage LO2, at first, based on knowledge
level and score in LO1 by decision making11 subpage,
which is similar to decision making subpage but does not
call any function for assigning score, they divide. Next,
learners go to decision making 2 subpage by firing transi-
tion with the same name as depicted in Figure 12. In
subpage decision making 2—based on their score in LO2
as depicted in Figure 13—learners are guided to 8 indi-
vidual paths. Next they come back to LO2 page and
based on their situation (one of 8 types) pass different
nodes to conclusion node.
Figure 12. ThO2 subpage. e L

An Adaptive Method Based on High-Level Petri Nets for E-Learning567
Figure 13. Decision making 2 subpage.
4. Evaluation
In the proposed system, to evaluate efficiency of the pro-
posed method, as defined in Table 2, we compute re-
sponse time for 3 types of users and next compare them
with response time for learners in non adaptive system. In
non adaptive system, users don’t choose learning unit
with awareness.
The purposes of considering these 3 types of users are:
1) we want to demonstrate if user has high knowledge
level (type 1), does not need to pass some units of the
course, and so she/he arrives to end section in shorter
time, whereas user may pass unnecessary units in regular
system; 2) We want to demonstrate, if user has low know-
ledge level but passes the course tests successfully (type
2), some learning units are ignored in his/her learning
path and learner finishes course in shorter time whereas
user may pass unnecessary units and does not pass nec-
essary unit in regular system; 3) we want to demonstrate
if learner has low knowledge level and does not pass
current course learning tests with success, learner should
pass all course units and can finish course in shorter time,
whereas she/he may skip some units and spend more time
in some units and this increases response time in non
adaptive system.
For computing response time in non adap
we delete guard condition of transitions in adaptive sys-
tems and allow learner choose arbitrary paths without con-
straints.
The simulation output in view of black box for adap-
tive system is depicted in Figure 14.
Each line of Figure 14 represented a learner. In each
line, the first number is response time, the second number
is learner number, the third number is step of simulation
and fourth number is model time. For example in first
line of Figure 14, first learner finished course in time 564
and it occurs in step 79 of simulation and model time has
been 564. Since the Figure 14 is output in viewpoint of
black box, type of learner, passed path and other details
don’t show.
For more detailed information, we use output in white
box. The Figure 15 shows response time in viewpoint of
white box. The information between each 2 lines is rep-
resenting a learner.
Table 2. User types.
User
type
Learning
style
Score in
content 1
Score in
content 2
Type 1H more than 10 more than 10
Type 2L more than 10 more than 10
Type 3L equal or less
than 10 equal or less than 10
tive system,
Figure 14. Output in viewpoint of black box.

An Adaptive Method Based on High-Level Petri Nets for E-Learning
568
The first parameter is learner’s learning style, the sec-
ond parameter is learner’s knowledge level, the third pa-
rameter is learner’s score in content 1, the fourth pa-
rameter is learner score in content 2, the fifth parameter is
learner’s arrive time, the sixth parameter is total time of
waiting in queue and remaining in system, the seventh
parameter is passed path by learner and eighth parameter
is the time spent by learners in each unit. The number
after parenthesis is response time. For example first line
is representing a learner with RWstyle and high knowl-
edge level and his/her score in content 1 is 15 and in
content 2 is 14. The arrive time for this user is 0 and total
time is 564. The passed path by him/her is :
“entersystem/selectRWstyle/Introduction/LO1/LO1
Testing/SelectHigh/selectLO1
Score>10/LO1_1/LO2/LO2 Testing/SelectHigh/LO2
Score>10/Example6/LO2_1/conclusion”
And time spent by his/ her in each unit
/60/5/5/59/66/67/5/5/46/127/30”
nt which is shown in above line, each
number represents the time period of corresponding sec-
tion in passed path. The number 5s represent thinking
times and other number represent time spent in learning
units. For example the number 67 is representing the time
of LO2 testing. Response time for learner which is sub-
traction of arrive time from total time (564-0) is 564. In
addition, total time means 564, equal to summation of
times that are shown in eighth parameter.
We summarize user types (is shown in Figure 14) and
response time for adaptive and non adaptive system in
Table 3.
Table 3. Response time for different users.
User type Response time in
proposed system
Response time in
non adaptive system
Type 1 564 686
is:
“0/5/20/64
In the time spe
Type 2 601 636
Type 3 708 759
Figure 15. Output in viewpoint o f white box.

An Adaptive Method Based on High-Level Petri Nets for E-Learning569
0
200
400
600
800
type1type2
ResponseTime
Learner
t ype3
prop ose dsystem
Re gu l ar system
ring for usFigure 16. Time com paer types.
0
20
40
60
Ti
80
me
100
120
140
introduction
LO1
exa1
exa2
LO1_1
LO2
exa3
exa4
exa5
exa6
LO2_1
type1type2type3
Learner
conclusion
Figure 17. Detail time comparing for user types.
We compare response time for all learners in adaptive
system with non adaptive system in Figure 16. As de-
picted in Figure 16 response time for all user type in
adaptive system is shorter than users in non adaptive sys-
tem.
The Figure 17 is representing spent time in different
course units in adaptive system. Since the time of deci-
sion making in all cases is constant, we don’t show them
in Figure 17. Some of learners, because of their features,
don’t pass some units of course. But all learners pass
main sections of course (Introduction, LO1, LO1_1, LO2,
LO2_1, and conclusion). Some learners such as learner of
type 3 is forced to pass all units of course as depicted in
Figure 17.
5. Conclusions and Future Work
We proposed an adaptive method for e-learning systems
by high level petri net. We use colore
entiating among learners with individual features. We use
tokens with time stamp for computing time of tasks such
as reading a learning unit or time of a test. We implement
method for a sample course and analyze results. From 16
learner types in sample course, all of them have response
time that was shorter than learner in non adaptive system.
So, we can say this method increases performance.
This method has many advantages. For example for-
mal semantic of petri net provides a formal method for
adaptation. Also graphic presentation in petri net, facili-
tates understanding of method; even for unfamiliar peo-
ple with petri net.
As future work, we want to examine proposed method
in real course and analyze user’s satisfaction and success.
Also in many recommending systems such as search en-
gine or electronic shop, in which user’s individual fea-
tures are important, this method can be used for adapta-
d tokens for differ-tion.

An Adaptive Method Based on High-Level Petri Nets for E-Learning
570
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