NETWORKED LEARNING PHYSICS OF SEMICONDUCTORS
THROUGH A VIRTUAL LABORATORY ENVIRONMENT
K. Ninos, I. Stavrakas, G. Hloupis, C. Anastasiadis and D. Triantis
Department of Electronics, Technological Educational Institution of Athens, 12210, Greece
Keywords: Virtual laboratory, Networked Teaching, Teaching Physics of Semiconductors, Server - Client Java
Application.
Abstract: Virtual laboratory tools have been introduced long ago. Such tools have been used by students to improve
their performance and stimulate their interest. When the high cost of hardware replacement and maintenance
is contrasted to the flexibility of adding new subjects in a laboratory course, the virtual laboratory tools
render a power tool for educational purposes. Since students statistically do not spend adequate time
preparing for laboratory modules, new methodologies and software have been sought to help students
increase their laboratory skills and understanding. In this work a virtual laboratory environment dealing with
Semiconductor Physics assignments is described, discussed and evaluated. Semiconductor Physics was
chosen since it is a first year module not connected to any background knowledge familiar to the students.
The evaluation was made by providing the software to some of the students and comparing their
performance to that of students having no access to the software. It is concluded that the software tool
provided to the students before taking the laboratory helped to increase their performance. It was also
observed that this tool mainly serves weaker students since, according to the evaluation tests, they are
mainly helped to achieve a pass score.
1 INTRODUCTION
The evolution on the network technologies and the
computing systems has been applied to higher
education Institutions in several fields (Ubell, 2000;
Fox, 2002; Ali et al., 2004). The use of new
technologies in software development has enabled
the combined application of hardware and software
in the teaching procedure of both theoretical and
laboratory oriented courses (Hart 1993; Hudgins et
al., 2002; Cheng et al., 2004; Dede, 2000; DeBord,
2004). There is an increasing interest world wide in
offering more flexible education systems based on
the use of Web resources and remote education.
Specifically, virtual laboratory tools have been
introduced a long time ago. Initially, they were used
to increase the flexibility of controlling hardware
and running experiments (i.e. Labview, Vee, Matlab,
etc). Consequently, they were adopted in the
learning procedure in order to increase the
performance of the students since they constitute a
user-friendly tool and stimulate them to use it in
their study. Additionally, when the high cost of
hardware replacement and maintenance are put in
contrast to the flexibility of adding new subjects in a
laboratory course it can be concluded that the virtual
laboratory environment tools render a power tool for
educational purposes. Such activities and new tools
have been proposed by several researchers (Tsiakas
et al., 2007; Stavrakas et al., 2005; Cheng et al.,
2004; Hart,1993; Hudgins et al., 2002). The
implications of these tools on the performance of the
students are still under investigation (DeBord et al.,
2004; Epstein et al., 2001; Ali et al., 2004). The
effectiveness of the virtual laboratories that are
closed (i.e. taken simultaneously by all students) or
open (i.e. taken at anyplace anytime) are also
investigated as well as other methodologies of using
network enable tools (Soh et al., 2005 and references
therein; Kumar 2003).
The Technological Educational Institution of
Athens in the Framework of Education and Initial
Vocational Training Program titled: “Upgrading of
Undergraduate Curricula of Technological
Educational Institution (T.E.I.) of Athens” has
developed several platforms that constitute novel
tools in the educational processes (Tsiakas et al.,
2005). Specifically, e-examination methodologies
257
Ninos K., Stavrakas I., Hloupis G., Anastasiadis C. and Triantis D. (2010).
NETWORKED LEARNING PHYSICS OF SEMICONDUCTORS THROUGH A VIRTUAL LABORATORY ENVIRONMENT.
In Proceedings of the 2nd International Conference on Computer Supported Education, pages 257-261
DOI: 10.5220/0002790802570261
Copyright
c
 SciTePress

have been adopted and evaluated regarding their
impact on the students’ performance. Additional
tools, that enforce student – teacher interaction, have
also been modified and adapted to the needs of each
academic department (Tsiakas et al., 2005;
Stergiopoulos et al., 2006; Triantis et al., 2004,
Triantis et al., 2009; Ventouras et al., 2010).
Since statistics show that students do not spend
adequate time for an upcoming laboratory module in
order to become familiar with the terms and the
underlying theory, new methodologies and software
have recently been developed and are provided to
the students in order to increase their performance in
laboratory modules. In this work a virtual laboratory
environment that was built is described and
discussed. The students’ performance is studied after
applying the new methodology. Open and close
laboratory methodologies have been tested. In the
present paper the work is organized as follows: A
short initial paragraph describes the motivation of
this research, escorted by a detailed description of
the designed and implemented software that were
based on a case study. In the last section we utilized
two evaluation tests to measure the impact of using
the system on the students’ learning outcome.
Initially, the software was used as a method to
prepare the laboratory module and in the second
evaluation test the software was used as a tool to
conduct the laboratory experiment.
2 DISCUSSING THE NEED
The motivation to measure the influence of virtual
laboratory experiments on the learning outcome of
students was an initial survey (through questions)
about the time the students spent to prepare a
laboratory module. A number of 133 students
participated in this process during the 2007—2008
academic year. The statistical analysis of the
answers is summarized in the pie diagram of
Figure1. In general, statistics indicates that most
students do not spend adequate time on studying a
laboratory module. Figure 1 shows that 45% of the
students actually do not spend any time before a
laboratory module, in order to become familiar with
the terms and the concept of the exercise. The above
results dictate that only 25% have previously spent
a sufficient amount of time in order to get prepared
for the module.
This observation was a motivation in order to
propose a new way of conducting laboratory
experiments in order to attract the students' interest
10%
35%
30%
17%
8%
None
Limited
Fair
Much
Very much
Figure 1: Time spent by students to prepare an upcoming
laboratory module.
and increase their performance. Additionally, for
evaluation purposes, the performance of the students
was compared before and after the use of new tool
for preparation and teaching the module.
3 DESCRIPTION OF THE
SYSTEM
A virtual laboratory environment was built in order
to teach physics of semiconductors. The system has
been developed through a Java programming
environment and consists of a server and a client
application in order to become network enabled.
Initially, the student designs the experimental circuit
through a user-friendly interface. The next step is to
connect the necessary instruments to perform
measurements and to set all the experimental
parameters. Evaluation of the built model comes
next. The final step is the execution of the
experiment. After the experimental procedure is
completed the student may require data plotting and
datasets exporting (which can be done in .xls
format).
An important advantage of this software is the
option provided to the student to approach the
experiment either from a theoretical point of view
(getting this way results as expected according to the
theoretical formalisms) or to introduce to the circuit
the device tolerance factors and transients (in order
to have a more realistic experimental result that
could be met in a laboratory environment).
Several experimental circuits and their
corresponding analysis have been designed and
added to this software. The most important ones
deal with the I-V characteristics of non-linear
electronic devices and the calculation of the energy
gap of Ge crystals.
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Client
(Presentation tier)
Server
(Logic tier)
RDBMS
(Data tier)
User ids, circuit details
User msg, circuit results
User
Control
Figure 2: System architecture.
The basis of this modelling software is a three-tier
client – server architecture. In this architecture, the
user interface, the functional process logic, the data
storage and access are developed and maintained as
independent modules, on separate platforms. Figure
2 shows schematically the architecture of the system.
The client sends the experimental description to the
server and the server responds sending the results
back. In addition, the server is connected to the
database system (RDBMS) for checking the user
accounts.
4 CALCULATING THE ENERGY
GAP (E
G
) OF GE CRYSTALS: A
CASE STUDY
A case study is described here and particularly an
experiment dealing with the calculation of the
energy gap of Ge crystals. For the calculation of Eg
the dependence of the sample resistance on
temperature is monitored by recording the values of
resistance versus increasing temperature. This is
accomplished by using a Ge crystal sample placed
on a heating resistor and by recording the sample
resistance and temperature using an ohmmeter and a
thermocouple respectively. A variable voltage
source feeds the heater to increase the sample
temperature.
Figure 3 depicts a typical Eg measuring circuit.
The program validates the completed circuit design
and the experiment begins. Voltage values are set by
the student in order to achieve a temperature
increase. Then, the student observes the resistance
values to decrease and the temperature values to
increase. If he manipulates the settings so that
temperature increases at a constant slow rate, then,
resistance variation recording will be successful.
The software using the appropriate formalisms
(that are shown in a textbox) will convert the
measured resistance of the sample into conductivity
(σ) using its dimensions and measured in S/m.
During the experimental procedure a plot like the
one of Figure 4 gradually forms and at the end of the
experiment its slope is calculated and presented on
it. Using this slope the software calculates the
energy gap E
g
through the equation:
)k2/E()1000/1(slope
g
⋅−=
where, k is Boltzmann’s constant equal to
15
KeV106.8k
−−
⋅⋅= In this case Eg = 0.65 eV.
Thermocouple
308.5
K
Heater
987.4
Ω
Ge sample
Voltage (V)
0.5
1.0
1.5
.....
Voltage
Supply
Figure 3: Typical circuit with a Ge crystal.
y = -3.79x + 13.77
0.0
1.0
2.0
3.0
4.0
2.5 2.7 2.9 3.1 3.3 3.5
1000
/
Τ
ln σ
Figure 4: Plot of lnσ:1000/Τ for the calculation of the
energy gap of Ge crystals.
5 RESULTS
The impact of this method of conducting laboratory
experiment on the students’ performance is
discussed here.
The software was provided to a number of
students that agreed to use it as a guide in order to
get prepared for the laboratory module (open virtual
laboratory). Preliminary multiple- choice question
NETWORKED LEARNING PHYSICS OF SEMICONDUCTORS THROUGH A VIRTUAL LABORATORY
ENVIRONMENT
259

tests on the module topic were given to the students
just before conducting the laboratory experiment.
Two sets of students participated in the tests; a) 31
students that had no access to the software and had
to prepare for the laboratory in the traditional way
and b) 34 students that had acquired the software
before the experiment and prepared the laboratory
using it.
Figure 5 shows the results of these two sets of
students. The grey bars represent the preliminary test
results achieved by the students that had no access to
the software. The white bars represent the
performance of the students after using the software.
It becomes evident that the use of the software
affects the results since the number of the students
that successfully complete the tests (grade > 5.0) is
larger than that of the ones who never used the
software.
Grades
Number of Students
0%
5%
10%
15%
20%
25%
30%
0-3.9 4-4.9 5-5.9 6-6.9 7-8.4 8.4-10
Figure 5: The impact of software on students preliminary
tests.
The laboratory experiment was conducted using two
methodologies. A set of 40 students selected to use
traditional hardware instruments with manual
controls and a second set of 37 students used the
software to conduct the measurements (close virtual
laboratory). After the experimental procedure each
student handed in an assignment. The grades
achieved by the students of the two sets after
completing the assignments are presented in Figure
6. Grey bars present the performance of the students
that conducted the laboratory experiment in the
traditional way. White bars present the grades of the
students that used the software to conduct the
experiment.
It becomes obvious that the students who
conducted the experiment using the software
achieved higher scores on the assignments. Another
important observation is that the students that
achieve high scores (>8.4) are not significantly
affected in any of the evaluation processes,
preliminary test or final assignment.
This observation manifests that the main target
this software achieves is to stimulate the interest of
students and make them study the experiment
concepts and the underlying theory before taking the
laboratory. The most impressive result is that in both
evaluations (i.e. preliminary test and assignment)
students that could not achieve the pass level (>5),
after using the software, seemed to understand and
digest enough the underlying theory of the
experiment.
Figure 6: The impact of software on students assignments.
6 CONCLUSIONS
A virtual laboratory tool was built and evaluated
using both open and close virtual laboratory
methodologies. Motivation of this work was the lack
of preparation before the laboratory modules from
the students’ side. It is concluded that a software tool
provided to the students before taking the laboratory
can increase their performance since it stimulates
them to prepare better for the upcoming laboratory
module. It is also observed that this tool mainly
serves lower score students since according to the
evaluation tests they are mainly helped to achieve a
pass score.
Finally, it can be stressed that such tools may be
used either before the experimental course as
additional learning tools or they may replace some
laboratory experiments increasing in this way the
interest of students and consequently their
performance. Moreover, new experiments and
extensions of the existing ones can be implemented
in order to keep the topic updated and beneficial for
the students.
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