Journal of Robotics, Networking and Artificial Life, Vol. 3, No. 3 (December 2016), 163-167

Quantitative Evaluation of Flash-based Educational Visualizing Simulator
Kei Takeichi 1, Yoshiro Imai 1, Kazuaki Ando 1, Koji Kagawa 1, Tetsuo Hattori 1*, Yusuke Kawakami 2
1

Faculty of Engineering, KagawaUniversity,
2217-20 Hayashi
Takamatsu City, Kagawa 761-0113, Japan
E-mail: {imai, ando, kagawa, hattori * }@eng.kagawa-u.ac.jp
2

DynaxT Co., Ltd.
2271-6 Hayashi
Takamatsu City, Kagawa 761-0113, Japan
E-mail: riverjp2002@gmail.com

Abstract
A Flash-based simulator of CPU scheduling has been developed and utilized for educational visualization in theclass of university lecture. We have designed and implemented it with Flash-based scripting language in order to
execute it as a stand-alone application as well as in various browsing environment such as Microsoft IE, Chromeand/or FireFox. Based on questionnaire for our simulator, its quantitative evaluation has been carried out by meansof statistical analysis. The report describes Flash-based simulator and the results of the quantitative evaluation.
Keywords: Educational visualization, Questionnaire-based Evaluation, Statistical analysis.

1. Introduction
As you know, computer system consists of hardware
and software, and now main emphasis of education
about computer system may be shifted from hardware tosoftware in almost all educational institute. In higher
education, especially, information processing and/or
computer science, it is very much important for students
to understand structure and behavior of computer
system as well as software system. So information
processing education trends to focus on software systemrather than hardware system. As a matter of course,
software system covers many important areas fromfundamentals to applications. This time, we focus on
Operating System, and particularly CPU schedulingalgorithm. It must be cover several kinds of themes that*students should understand during their school days. A
simple algorithm, namely First Come and First Served
is very natural so that it is one the most fundamental
strategies to decide its priority for users, clients,processes/tasks and so on.
Priority based algorithm is another candidate todetermine the order of execution. It is very significant
idea to choose a suitable item around potentiallyselected targets. It means which is better, or which isoptimal of them. Shortest Processing Time First is to bechosen as one of the priority-based algorithms in thisstudy. In other view point, Round Robin is evaluated asa policy of algorithm to realize equality of opportunity
around the targets. It is a little complicated but very
useful strategy to choose item with equal opportunities.

Dr Hattori has been now a Professor Emeritus of Kagawa University since Apr. of 2015.

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Kei Takeichi, Yoshiro Imai, Kazuaki Ando, Koji Kagawa, Tetsuo Hattori, Yusuke Kawakami

In order to teach students these above algorithms
efficiently, we had better utilize some suitableeducational tool to visualize their behavior and results
for specific conditions 12. A visual simulator is one of
the useful solutions to provide educational tools forstudents who want to understand such theme of
information processing education in trial-and-error,
step-by-step, and demonstrative manners3. In this study,
we have developed some useful educational tools todemonstrate practical CPU scheduling algorithm andprovide visual understanding45 for students in aneffective way.
This paper describes how to design Adobe Flash based
simulator for CPU scheduling algorithm, realize it inorder to execute on the useful environments, and thenperform quantitative and qualitative evaluation throughreal education, namely in the course of information
processing of university. The next section illustrates
system configuration of our visual simulator and its
behavior on the usually executing environments. The
third section demonstrates quantitative evaluation
mainly by means of questionnaire for its users in thereal class and statistical analysis of its results. The lastsection concludes our study on development andevaluation of Adobe Flash based visual simulator for
CPU scheduling algorithm.

Fig. 1. Visualization of Algorithm: FCFS.

2.2. Shortest Processing Time First (SPTF)
SPTF is one of the most famous priority-based
assigning/allocating algorithms. Whenever every event
happens, namely conditions have changed, it must beinvestigated which candidate has the best priority at thattime. So we had better call this algorithm Shortest
Remaining Processing Time First (SRPTF), because wemust consider not the total processing time but"remaining" processing one in order to decide which
candidate has the best priority at that time or later. Our
simulator visualizes sample result based on SPTF
(SRPTF) algorithm in Fig.2.

2. Simulator for CPU Scheduling Algorithm
In a lecture of Operating System of our university, mostimportant algorithms of CPU scheduling are FCFS
(First-Come First-Served), SPTF (Shortest Processing
Time First) and RR (Round Robin), we think. They arevery trivial but sometimes very useful in the real
operating systems. So we have employed these abovealgorithms as fundamental procedures to decide CPUscheduling in our visual simulator. The above three
procedure based on FCFS, SPTF and RR are as follows;
2.1. First-Come First-Served (FCFS)

Fig. 2. Visualization of Algorithm: SPTF.

2.3. Round Robin (RR)
RR is a typical non-priority based algorithm in order toprovide 'equality of opportunity' which can realize
taking turns at it. This algorithm can retrieve candidates
which are waiting for service and select/assign one ofthem who wait for the longest time or longer thanothers. Our simulator visualizes sample result based on
RR algorithm in Fig.3.

FCFS is very much simple, but clearly well-defined
strategy to decide next candidate to be performed, justlike First-In/First-Out (FIFO queue). Our simulator
visualizes below result of FCFS algorithm in Fig.1.
This algorithm is sometimes the target to be comparedwith other algorithm(s). And moreover, a result appliedby this algorithm will be not so worse than other ones
derived from complicated algorithm(s).

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164Fig. 3. Visualization of Algorithm: RR.

Quantitative Evaluation of Flash-based

It is a little complicated for beginners to understanddetails of RR-based procedure and/or develop a kind ofcorresponding programs. And we may sometimes meet
its results with not suitable performance for specificapplications. But 'equality of opportunity' is importantfor several users to receive their necessary services.
3. Quantitative Evaluation
This section presents quantitative evaluation of ourvisualizing simulator6.
3.1. Comparison of Execution TimeAs one of the quantitative evaluation for our CPU
scheduling simulator, at first, we compare the execution
time of simulation of native Flash player with ones on
the below three major browsers. The result issummarized in the following Table 1.
Table 1. Comparison of Execution Time(s)between Different Environments.
Host Application
Flash Player (Ver.10)
Stand alone
MS-Internet Explorer
11.0.9600
Mozilla FireFox
35.0.1
Google Chrome
40.0.2214.11 m

Execution Time
32.25 (sec.)

No.3 of Questionnaire (Q#3)
Do you understand CPU scheduling algorithm moresuitably with this simulator? (yes: 2, neutral:1, no:0)
No.4 of Questionnaire (Q#4)
Are you interesting in CPU scheduling algorithm bymeans of this simulator? (yes: 2, neutral:1, no:0)
No.5 of Questionnaire (Q#5)
Are you interesting in other themes of Operating Systemafter usage of this simulator? (yes: 2, neutral:1, no:0)
No.6 of Questionnaire (Q#6)
Do you need to utilize another type of simulator in orderto learn Operating System? (yes: 2, neutral:1, no:0)
3.3. Results of Questionnaire
Our questionnaire described before can obtain just 20
answers from students of the class because of carryingout on a voluntary basis, although we used to have theclass for Operating System with 40 students or more.
The result of such a questionnaire is summarized inTable 2.

28.95 (sec.)
Table 2. Results of Questionnaire about ourSimulator.

35.19 (sec.)
93.61 (sec.)

3.2. Questionnaire after Using Simulator
As another quantitative evaluation for our simulator,
secondly, we have carried out questionnaire in theclassroom lecture of Operating System in our university
after using our simulator. The questionnaire includes thefollowing six questions.
No.1 of Questionnaire (Q#1)
Is it easy to utilize this simulator? (yes: 2, neutral:1,no:0).
No.2 of Questionnaire (Q#2)
Is it effective to learn CPU scheduling algorithm withthis visual simulator? (yes: 2, neutral:1, no:0)

Student
S01
S02
S03
S04
S05
S06
S07
S08
S09
S10
S11
S12
S13
S14
S15
S16
S17
S18
S19
S20

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Q#1
2212222222221222222
1

Q#222122222222221222222Q#3
21021212221212211222Q#4
11211111111111212212Q#5
11121111111111111212Q#622221222122122222222

Kei Takeichi, Yoshiro Imai, Kazuaki Ando, Koji Kagawa, Tetsuo Hattori, Yusuke Kawakami

Q#1 has 17 numbers of answer "yes" per 20 students(i.e. 85%), Q#2 has 18 numbers of answer "yes" per 20students (i.e. 90%) and Q#3 has 12 numbers of answer"yes" per 20 students (i.e. 60%). From thequestionnaire, many students do feel easy to utilize ourCPU scheduling simulator and consider to be effective
for learning CPU scheduling algorithm by means ofusing our simulator. And majority, namely six out often, of replying students understand CPU schedulingalgorithm more suitably with this simulator. At the sametime, however, Q#4 has just 5 numbers of answer "yes"per 20 students (i.e. 25%) and Q#5 has only 3 numbersof answer "yes" per 20 students (i.e. 15%).
4. Statistical Analysis
In order to perform test of independence among Q#1,Q#2 and Q#3, we will demonstrate to calculate "χ2 testof goodness-of-fit" for relation between results fromQ#1 and Q#2 as well as one for Q#1 and Q#3,
respectively. Relation between results from Q#1 andQ#2 is expressed in the left-hand of Table 3, while
relation for Q#1 and Q#3 is done in the right-hand. Theformer has 2 x 2 table-items and the latter has 2 x 3ones.
Table 3. Relation between Results from Q#1 andQ#2 (left-hand) & from Q#1 and Q#3 (righthand).
Q#2

yes
Q#1

neutralnoyes

neutral162yesneutral

no1

1160

111

1

Based on Table 3, a two-way contingency table for Q#1
and Q#2 can be introduced, which is shown in Table 4,
while another two-way contingency table for Q#1 andQ#3 can be also done, which is shown in Table 5.
Table 4. Two-way Contingency Table for Q#1
and Q#2.
Q#2

Q#1

yes
neutral
SUM

C

2

yes

neutralSUM

16(18*17/20)
2(18*3/20)
18

1(2*17/20)
1(2*3/20)
2

17
320

R2χ 2 = ∑∑{( yij −i =1 j =1sumR * sumC 2 sumR * sumC) /}
Total
Total

= {16 − (18 *17 / 20)}2 /(18 *17 / 20) + (1)+ {1 − ( 2 * 3 / 20)}2 /( 2 * 3 / 20) = 2.135

As described in Eq. (1), degree of freedom for Table 4is ν = (2-1) x (2-1) = 1. So we can have χ2 α=0.05(ν=1) = 3.8415 from the χ2 distribution table. "Statisticalindependence" between results from Q#1 and Q#2 canbe confirmed so that almost users of our simulator notonly consider it to be easy to utilize but also recognize
effectiveness to learn CPU scheduling algorithm with it
respectively and independently.
Table 5. Two-way Contingency Table for Q#1and Q#3.
Q#3

yes
Q#1

yes

SUM

neutralno11(12*17/20) 6(7*17/20) 0(1*17/20)
1(12*3/20)
1(7*3/20) 1(1*3/20)
1271

neutralC

SUM

R17320

Just like the same way, the χ2 (goodness-of-fit statistic)
for Table 4 can be calculated in the following
expression of Eq. (2).
2Q#3
noThe χ2 (goodness-of-fit statistic) for Table 4 can becalculated in the following expression of Eq. (1).

3

χ 2 = ∑∑{( yij −i =1 j =1sumR * sumC 2 sumR * sumC) /}
Total
Total

= {11 − (12 *17 / 20)}2 /(12 *17 / 20) + + {1 − (1 * 3 / 20)}2 /(1 * 3 / 20) = 0.1365(2)

As described in Eq. (2), degree of freedom for Table 5is ν = (2-1) x (3-1) = 2. So we can have χ2 α=0.05(ν=2) = 5.9915 from the χ2 distribution table. "Statisticalindependence" between results from Q#1 and Q#3 canbe also confirmed so that users of our simulator not only
consider it to be easy to utilize but also understand CPU
scheduling algorithm more suitably with our simulator
respectively and independently. Major part of oursimulator’s users recognize that they understand CPU
scheduling algorithm more suitably with the simulator
whether or not such simulator is considered to be easy
to utilize.

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Quantitative Evaluation of Flash-based

In other words, both of results from Q#1 and Q#2 are
not only very good scores, namely 85% of the former's
answers are "yes" and 90% of the latter's answers are"yes", but also the two scores are statisticallyindependent each other, namely there is no reason thatone scores can become good because another scores are
good. And major part (i.e. 60%) of users, whose
answers from Q#3 are "yes", understand CPU
scheduling algorithm more suitably by means of oursimulator independently from its operability.
5. Conclusions
This paper has described an Adobe-Flash based
educational visualizing simulator for students to learn
CPU scheduling algorithm graphically and practically.
Our Flash-based simulator can execute on the major
Web browsers such as Microsoft InternetExplorer,
Mozilla FireFox and Google Chrome and provide
efficient explanation for students of lecture "OperatingSystem".
As quantitative evaluation for our simulator, we havecarried out a questionnaire for the students using thesimulator and have applied statistical analysis for theresults of the questionnaire as shown in the followings.
• 85% of answers from Q#1:"Is it easy to utilize thesimulator?" is "yes", and 90% of answers from
Q#2:"Is it effective to learn CPU schedulingalgorithm with the simulator?" is "yes". Therefore,
it is confirmed that our visual simulator for CPU
scheduling algorithm can provide easy utilizationand effective learning to its users.
• Major part, namely 60%, of learners can understandCPU scheduling algorithm more suitably with thesimulator, although only 25% of users areinteresting in CPU scheduling algorithm by meansof the simulator.
• According to statistical analysis, the results from
Q#1, Q#2 and Q#3 are statistically independent
from one another, and it is confirmed that eachanswer did not have an effect on other answering
behaviors statistically.
Then, we can consider that easy utilization, effective
learning and CPU scheduling algorithm understanding
are statistically independent from one another, and they
are good attributes for our visual simulator for CPU
scheduling algorithm.
As a further study, we would also like to evaluate each
student’s understanding situation in the learning process
more precisely, utilizing some estimation and evaluation

methods adopted in time series analysis and Kansei
Engineering as described in Ref. 7-10.

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