Laboratory Learning: Hands-on versus Simulated Experiments

Abstract

Many universities and vocational training institutions conduct laboratories as simulated experiments. This is due to the costs and supervision needs to conduct hands-on labs safely. Numerous studies have presented mixed opinions on whether hands-on laboratory work is more conducive to learning than a simulated laboratory. Most of the studies put students from experimental and control groups in significantly different conditions. Therefore, it is hard to reach any definite conclusion regarding the influence of the learning mode onto the learning achievements. PURPOSE This study compares learning outcomes of student laboratory work in an energy storages course conducted in two different modes: first as a practical hands-on exercise and second using computer-based simulations. APPROACH In order to provide reliable insights, this study implements optimized research methodology to avoid any other effect (e.g. learning synchronicity/distance learning/instructions) on the learning outcome rather than the effect of the learning mode itself. The student laboratory experiments were created in a manner that they could be conducted in both modes in the same way and using a single set of instructions. To ensure a comparable group environment for the individual student, the students were arranged into two similar groups based on the student's practical experience. In this crossover study, the groups were taught the same topics by means of interchanging learning modes. RESULTS To evaluate the influence of each mode on student learning, short written tests regarding the previous experiment were conducted at the beginning of the subsequent laboratory session. 102 students have taken part in the study in two years. Overall learning results of hands-on experiments were slightly better than those of simulated laboratories (Cohen's d=0.25), the difference in performance was statistically significant (p<0.02). Through solicited feedback on each laboratory session, in hands-on mode more students expressed they have acquired new insights/comprehensions (76% vs. 66%, Cohen's d=0.23, small effect, p<0.07). CONCLUSIONS Following the strategy not to optimize the lessons individually to the learning mode, other influences on the learning outcome, which were usually mixed, were excluded. The students' subjective opinions show advantages of the hands-on mode. Based on the objective data, a weak, but significant outcome to better knowledge acquisition with hands-on laboratory experiments was achieved. This observation is against the trend of the literature in the last years towards better or equal learning with nontraditional labs. Some of the excluded factors might have a stronger influence on student learning than estimated previously. To get a clear view, the authors recommend isolated research.

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1
Laboratory Learning:
Hands-on versus Simulated Experiments
Fabian Stegera,b, Alexander Nitscheb, Cayler Mileyc,
Hans-Georg Schweigerb, and Iouri Belskia.
School of Engineering, RMIT, Melbourne, Australiaa,
Faculty of Electrical Engineering and Computer Science, THI, Ingolstadt, Germanyb
Faculty of Computer Science and Engineering, UNR, Reno, USAc
Corresponding Author Email: fabian.steger@thi.de
SESSION C1: Integration of theory and practice in the learning and teaching process
CONTEXT Many universities and vocational training institutions conduct laboratories as
simulated experiments. This is due to the costs and supervision needs to conduct hands-on
labs safely. Numerous studies have presented mixed opinions on whether hands-on
laboratory work is more conducive to learning than a simulated laboratory. Most of the
studies put students from experimental and control groups in significantly different conditions.
Therefore, it is hard to reach any definite conclusion regarding the influence of the learning
mode onto the learning achievements.
PURPOSE This study compares learning outcomes of student laboratory work in an energy
storages course conducted in two different modes: first as a practical hands-on exercise and
second using computer-based simulations.
APPROACH In order to provide reliable insights, this study implements optimized research
methodology to avoid any other effect (e.g. learning synchronicity/distance
learning/instructions) on the learning outcome rather than the effect of the learning mode
itself. The student laboratory experiments were created in a manner that they could be
conducted in both modes in the same way and using a single set of instructions. To ensure a
comparable group environment for the individual student, the students were arranged into
two similar groups based on the student's practical experience. In this crossover study, the
groups were taught the same topics by means of interchanging learning modes.
RESULTS To evaluate the influence of each mode on student learning, short written tests
regarding the previous experiment were conducted at the beginning of the subsequent
laboratory session. 102 students have taken part in the study in two years. Overall learning
results of hands-on experiments were slightly better than those of simulated laboratories
(Cohen's d=0.25), the difference in performance was statistically significant (p<0.02).
Through solicited feedback on each laboratory session, in hands-on mode more students
expressed they have acquired new insights/comprehensions (76% vs. 66%, Cohen's d=0.23,
small effect, p<0.07).
CONCLUSIONS Following the strategy not to optimize the lessons individually to the
learning mode, other influences on the learning outcome, which were usually mixed, were
excluded. The students' subjective opinions show advantages of the hands-on mode. Based
on the objective data, a weak, but significant outcome to better knowledge acquisition with
hands-on laboratory experiments was achieved. This observation is against the trend of the
literature in the last years towards better or equal learning with nontraditional labs. Some of
the excluded factors might have a stronger influence on student learning than estimated
previously. To get a clear view, the authors recommend isolated research.
KEYWORDS Hands-on vs. simulated experiment, battery experiment, learning-mode
comparison
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Introduction
Many universities and vocational training institutions conduct laboratories as simulated
experiments instead of using the traditional hands-on way (Ma 2006, Brinson 2015, Heradio
2016). One reason is the cost of required lab equipment to conduct hands-on experiments. In
case of potentially dangerous learning objects – like lithium-ion cells (Dahn 2011) –
additional supervision is necessary to conduct traditional labs safely.
This study compares learning outcomes of student laboratory work conducted in two different
modes: first as a practical hands-on exercise and second using computer-based simulations.
Previous studies have presented conflicting outcomes (Ma 2006, Brinson 2015, Heradio
2016, e.g. Mathiowetz 2016, Sarabando 2016). After comparing the results of such research,
Ma and Nickerson (2006) concluded that many studies did not allow to reach universally
applicable conclusions.
Most of the studies put students from experimental and control groups in significantly
different conditions. This is a result of separately developing and optimizing the teaching
experiment in each mode, as the teachers/researchers do not see the modes as directly
competing solutions for exactly the same objective, they prefer to accommodate different
circumstances with both modes. For example, students conduct hands-on experiments in
groups whilst on campus, but as off-shore students they are engaged in simulated exercises
over the web and individually. Therefore, it is hard to reach a definite conclusion regarding
the influence of the learning mode onto the learning achievements based on this kind of
studies. As the change of modes is linked with other influences on the learning outcome (like
changing learning objectives in the two modes, amount/type of supervision, cooperative
learning effects, distance learning vs. learning in the university, and/or differing instructional
papers) such studies always compare the combination of aspects. If these interfering aspects
have stronger influences, the studies are unable to specifically identify the difference in
learning effectiveness of one aspect (Lindsay 2005). This may explain the inconsistent
outcome of present research (Ma 2006, Brinson 2015). Most of these other influences are
difficult to describe clearly in written form, or just not mentioned in the publications. In order
to provide more reliable insights, the present study follows an optimized research
methodology to avoid many other influences on the learning outcome while comparing the
modes. The student experiments were strictly developed in such a manner that the learning
objectives and experimental procedure were matching in both modes.
Approach
In a crossover study, both groups were taught the same objectives, clustered into four
content areas. All of the content areas were related to lithium-ion cells or battery systems.
The first group learned the first and third area with traditional hands-on experiments while the
second group was taught the same areas by computer-based simulations. The second and
fourth areas were taught using the opposite learning mode in both groups.
In the beginning of the next laboratory session 10-minute long tests were conducted to
evaluate the influence of the mode on student’s knowledge on the content areas of the
previous experiment. To compare which mode has been more successful, the mean results
for each group were evaluated. Moreover, the students were asked to reflect on their
learning during each session in an online survey.
The experiment was conducted three times. Forty students of the study program "Electrical
Engineering and Electric Mobility" at Technische Hochschule Ingolstadt (THI) were enrolled
in the mandatory laboratory subject in summer-semester 2016. Thirty THI students were
enrolled in summer-semester 2017. All students were asked to join the full anonymous study.
Additionally an international summer school was included in the study to collect a broader
database.
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Learning objectives of the laboratory
It was decided to teach the most relevant transferable skills and knowledge that may be
beneficial for the students' future careers. Thus, students should get familiar with the
characteristic behavior and the most important parameters of battery cells. They should also
gain knowledge on how to determine the parameters of battery cells self-sufficiently by
means of appropriate experimental setups.
The recognized learning objectives where clustered into four main content areas: (A) contact
and isolation resistance, (B) open-circuit voltage, (C) internal resistance and power, and (D)
energy of cells. Grouped in these areas, seven laboratory experiments were conducted in
both modes in the same manner:
• Low Resistance Measurements (A1): Students discover that a multimeter is an
inaccurate tool for low ohmic measurements (milliohm range), and why such
measurement is a misuse of this tool. They learn how to use alternative procedures
for low ohmic measurements, including the four-wire measurement in AC and DC.
• Contact Resistance (A2): Students conduct experiments with a variety of typical
electrical connections in battery systems to determine the contact resistance values.
• Isolation Resistance (A3): Students learn to estimate the influence of moisture on the
isolation resistance.
• Voltage Curve (B): Students investigate the voltage of a cell depending on the state of
charge. They use two different types of lithium-ion cells.
• Internal Resistance (C1): Students learn to use AC- and DC-methods to measure
internal resistances, being aware of the temperature dependency of battery cells.
Students learn to approximate temperature changes caused by power loss inside a
cell.
• Power (C2): Students investigate the maximum discharge rate of battery cells.
Students discover the dependency of maximum discharge power from state of
charge, pulse duration, and temperature.
• Energy and Capacity (D): Students determine the capacity of a lithium-ion cell and
learn about the factors influencing it. They learn to calculate the energy efficiency of
charge and discharge cycles.
Each experiment was developed in parallel for both modes. Only a single set of instructions
was created and then used in both laboratory modes, as instructions affect the learning
outcome of an experiment, e.g. Chamberlain 2014 found, that the guidance level can
influence student exploration.
Creating two comparable groups for the crossover study
In a crossover study, differences of the compared groups' average performances may be
equalized by statistics. Nevertheless, with the goal of isolating the influence of the learning
mode in the experiment, the authors had to consider the in-group interaction in laboratories.
The same student may have different experiences in different groups, with consequent
effects on his or her learning (van der Laan Smith 2007, Webb 1989). It was assumed that
students with more practical experience might perform differently than their peers with a
lesser practical background. Therefore the authors tried to create each of the two laboratory
groups from students with a similar mix of practical skills. For the two study semesters of
2016 and 2017, an introductory questionnaire that asked students of their practical
experience was developed. Its results were used for the group allocations. While completing
the questionnaire, each student created a code-word that was used in the study to allocate
the responses and test results to the appropriate individual, while keeping all participants
anonymous. These code words were also used to assign students into laboratory groups.
(Steger 2016)
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Thirty-two students from the international summer school were allocated to groups to ensure
similar distribution of the field of studies, number of study semester, and the nationality of the
students in each group.
Conducting laboratories in content areas A to D
For each of the four main content areas a session with practical hands-on experiments and a
session with computer-based simulations were established. Planned as a crossover study,
each group followed the same sequence of contents, while alternating the learning mode
between sessions. The first group handled the first content area with a computer-based
simulation while the second group conducted it as a hands-on experiment. For the upcoming
session both groups switched between learning modes. For the first content area, a specially
created web tool was used for simulations. For the other areas a black box simulation of the
battery cell and the hands-on equipment (Steger 2017a) was used. The battery simulation
imitates all effects observed in the student experiments and was parameterized to match the
battery cells which are used in the hands-on mode. To avoid any influence from user’s
interface, in both modes the simulation model was controlled by the same graphical user
interface, a program to create the sequences for testing batteries. In both groups students
selected companions to work in small teams of three to five students on the experiments. All
groups and teams remained constant to avoid changing cooperative learning while the
conduction of the semester lasting educational experiment. The laboratories were developed
in a way to be conducted autonomously by the students in a supervised environment. All the
learning targets were addressed through the experiment procedure/instructions, without
essential explanations from the instructor. To pass the subject all learning teams had to
prepare a written laboratory report for each content area.
During international summer school two trimmed content areas were taught on the same
day; the same crossover principle was used as during ordinary semesters in 2016 and 2017.
Online survey after conducting the experiments
All student participants excluding that of the international summer school were asked to
express opinions on their laboratory learning in a short online survey. It was conducted fully
anonymously using the university’s digital learning environment. Responses to the following
survey questions were compared in order to evaluate the laboratory learning in both learning
modes:
(a) "By conducting the experiment I gained new insights/comprehensions today." (German:
"Ich habe heute durch den Versuch neue Erkenntnisse gewonnen.") This was a yes/no
answer and coded 1 or 0.
(b) "At which point in the experiment did you have the biggest problem proceeding with the
experiment?" (German: "An welcher Stelle im Versuch hatten Sie am meisten Probleme
voranzukommen?") This was a free text question, which was not compulsory. For data
evaluation, the information was coded to 1 if any problem was mentioned or to 0 if students
wrote nothing or were expressing they had no problems.
(c) "The procedure of the experiment is quite difficult (1) / feasible (0.5) / easy (0)" (German:
"Die Versuchsdurchführung ist recht schwer / machbar / leicht") The answers were coded in
a three step Likert scale.
(d) "The content of the experiment is also relevant for me outside the university; I can
imagine that it will be beneficial for my future professional life." (German: "Der Inhalt des
Versuchs hat auch außerhalb der TH Relevanz für mich; ich kann mir vorstellen, im
Berufsleben Gewinn aus dieser Versuchsdurchführung zu ziehen.") The answers were coded
in a five step Likert scale: fully agree (1) / somewhat agree (0.75) / maybe (0.50) / somewhat
disagree (0.25) / disagree (0)
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Testing the learning outcome
Three written knowledge tests - each lasting 10 minutes - were used to determine the
influence of the learning mode on student learning outcomes. Knowledge gained by students
in a laboratory was tested just prior to the next laboratory session. A mix of descriptive and
multiple-choice questions, free response, and drawings on the learning objectives of the
previous experiment were used. The results were evaluated using a positive point system.
The achievable number and the distribution of the points were set fixed beforehand.
As the tests were conducted anonymously, the students were distinguished by their self-
created code words that were created by individual students during the Questionnaire on
practical experience.
To exclude time influence on memorizing knowledge the authors aimed for equal time lapses
between two sessions and thereby between experiments and its corresponding tests for both
compared groups. Unfortunately this goal was not achieved regarding content area A in 2016
(Steger 2016). By conducting the sessions for both groups on the same day of the week it
was easier to keep the time gap between the experiments and tests equal for both learning
modes in 2017.
Independent of the learning mode, the same environment was established for both groups
during tests. All students were able to sit at a desk in the same computer laboratory while
working on the knowledge tests.
To find out which mode provides better learning outcome, average results for the test for
each group were compared between hands-on and simulated modes.
Assessment of the influence of the study mode on learning outcomes of students that
participated in the international summer school was similar to that of the students of peers
that conducted laboratories during study semester. Due to logistical constrains, though, tests
on the laboratory knowledge were administered to students as an examination one week
after the experiments.
Results
Learning outcomes based on written tests
In year 2016 with 40 students for three content areas (A to C) a weak effect towards benefits
of the hands-on mode was discovered. Hands-on laboratory sessions led to a better
knowledge acquisition compared to simulated experiments. Content area D showed no
difference between the modes. Overall learning results of hands-on experiments were
slightly better than those of simulated laboratories (weak effect, Cohen's d=0.22), but the
difference in performance was not statistically significant. (Steger 2016)
The second experimental run was conducted with 30 students in summer semester 2017.
The range of individual scores was from 20% to 79% for hands-on, and from 15% to 77% for
the simulated mode. For content areas A (d=0.50) and D (d=0.80, p<0.02) an effect towards
benefits of the hands-on mode was measured. Content area C resulted in a weak effect
(d=0.14) in the same direction, and content area B showed no difference between the
modes. This run was demonstrating hands-on laboratory sessions led to a significant better
knowledge acquisition compared to simulated experiments (d=0.34, p<0.05).
Additionally the learning outcome of 32 participants of the international summer school 2017
in Ingolstadt was evaluated. A short version of content areas B and C1 was taught.
Independent samples T-test showed that content area B had no effect (d=-0.09), and content
area C1 demonstrated a small to medium effect towards better learning in hands-on mode
(d=0.44).
Overall learning results of hands-on experiments were slightly better than those of simulated
laboratories (small effect, Cohen's d=0.25), the difference in performance was statistically
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significant (p<0.02). The correlation between an individual student’s overall performance in
the knowledge tests and learning better with simulations is very weak and insignificant
(Pearson correlation 0.12, p=0.37, N=53).
Table 1: Learning Results Comparison
Research run
Number of
Students
Std. Deviation
of Percentage
Points
Sample
size N
Mean
Value of
Points
Sample
size N
Mean
Value of
Points
effect size /
Cohens d
(pos = adv. of
hands-on)
Student's t
distribution /
p-value one-
tailed
Both modes
Hands-on
Simulated
THI 2016
40
17%
73
47%
75
44%
0.22
0.09
THI 2017
30
20%
58
56%
57
50%
0.34
0.03
THI SS 2017
32
24%
28
60%
28
56%
0.15
0.28
ALL
102
20%
159
53%
160
48%
0.25
0.014
In the 2017 research the pre-questionnaire was extended to ask for more detailed
information. There was no correlation found between average mark in the study program and
individual more effective learning mode (Spearman rho=0.03, N=24). Students who had not
studied directly after school, but made a German VET (Vocational Education and Training,
BMBF 2016), which is a dual (company & school) apprenticeship training and studied later
(1st) have more hands-on experience (Pearson r=0.50, p=0.008, N=27) and (2nd) learn better
with hands-on experiments compared to simulations (Pearson r=0.53, p=0.007, N=25) as
they (3rd) have comparatively bad results with simulated experiments (Pearson r=0.47,
p=0.017, N=25).
Online survey for student feedback
The student feedback results are based on all results of the first and second summer
semester iteration. Student feedback was not collected during the summer school due to
time constraints. Because fewer students responded to the questionnaire in 2016 (37%), an
incentive was offered for completing the questionnaire in 2017 (Steger 2017b). The minimum
passing score of 50% was reduced to 45% for participation in the online survey. As a result
the return rate increased to 70% in 2017.
(a) In hands-on mode more students expressed they have acquired new
insights/comprehensions (76% vs. 66%, Cohen's d=0.23, small effect, p<0.07).
(b) Slightly more students mentioned problems while conducting the hands-on equivalents
(45% vs. 39%, Cohen's d=0.12, not significant, very weak effect).
(c) The engagement in the simulated experiments was stated to be a very small amount (5%
of scale) more difficult. Cohen's d=-0.16 demonstrates a very weak effect, which was not
significant.
(d) In the feedback form on the experiments, students who conducted the experiments in the
hands-on mode rated the execution of the experiments a little more beneficial for their future
professional life (58% hands-on vs. 54% simulation, Cohen's d = 0.20, small effect).
A significant correlation between (a) and (d) was found. It shows that students who claimed
that they gained new insights also tend to believe that the execution of the experiment will
help them in their future professional life (Pearson's r=0.36, p<0.001, Spearman's rho=0.35).
Looking at both modes separately, this correlation in the simulation mode was little stronger
(Pearson's r=0.39, p<0.001; Spearman's rho=0.40; N=86) compared to the hands-on mode
(Pearson's r=0.30, p=0.115; Spearman's rho=0.27; N=92).
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Interesting is a significant weak correlation that exists only in hands-on mode: Students who
(c) stated a hands-on experiment difficult, tend to not (d) consider it beneficial for their future
professional life (Pearson's r=-0.23, p=0.017; Spearman's rho=-0.22; N=86). Regarding
simulated experiments Pearson's r and Spearman's rho is 0.04.
The actual methodology does not allow establishing correlations between individual learning
outcome and student's feedback, as the standard online feedback form did not ask for the
self-created code word that was used in the tests. The authors plan to request this missing
information by updating a questionnaire and use the information for the next iteration.
Conclusions
Learning outcomes based on written tests
Following the strategy not to optimize the lessons individually to the learning mode, other
influences on the learning outcome, which were usually mixed, were excluded. Based on the
existing data, a weak, but significant outcome of better knowledge acquisition with hands-on
laboratory experiments was achieved. This is against the trend of the recent literature that
reported on better or equal learning with nontraditional (virtual/simulated) labs (Heradio 2016,
Brinson 2015). Some of the excluded factors might have a stronger influence on student
learning than estimated previously. To get a clear view, the authors recommend isolated
research. The study on the mode will be continued through 2018 at THI and at more
universities and training institutions with different types of students (e.g. international
students, students enrolled in summer schools).
The average performance in the knowledge tests of a single student is independent from his
better performing learning mode. It is important to note that this is the result of the created
group environment, and may differ if one creates groups of high and low performers.
The difference between students that completed German Vocational Education and Training
before studies and those who enrolled at university directly after school was interesting.
Obviously, the VET-participants have more hands-on experience. This fact was confirmed by
the study. The group creation questionnaire distributed the VET-participants equally to both
groups based on the hands-on experience without using the VET-info. In 2017, 59% of the
participating students in the study were VET-participants. Looking at the correlational data,
this group learns better with hands-on experiments compared to simulations, as they have
significant disadvantages while taught by simulations. Checking the 2017 results of the
knowledge test separately, confirmed this point of view: VET-participants had a significant
better learning outcome with hands-on experiments (Pearson r=0.56, p<0.015, N=60 tests),
while non-VET-participants had no significant difference (N=37 tests).
Online survey for student feedback
The students' opinions show advantages of the hands-on mode. The effect between both
modes in the student's subjective opinion about gained knowledge (a) is very similar to the
objective results tests, even when student’s expressions are less significant. Regarding
mentioned problems (b) and stated difficulty (c) the experiments are considered equivalent.
The slight, insignificant differences may be a result of statistical effects. To clarify this, more
data collection is necessary. Asking the students to describe the laboratory difficulty in a free
text answer may help to gain a deeper insight. Independent from the learning mode, more
than forty percent of the students stated the opinion that they do not benefit in their future
professional life from the experiments (d). There are slight advantages in the hands-on mode
and it is planned to ask in future experiments for missing content students estimate as more
important for their future profession.
The correlation between (a) and (d) suggests that students who believed that the execution
of the experiment will help them in their future professional life also expressed that they
gained new comprehension. The slightly stronger correlation between (a) and (d) in the
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simulations mode suggests that teachers should pay attention to explain the relevance of the
experiment in simulated labs more carefully. Comparing the amount of positive answers in
(a) and (d), one can conclude that the students do not consider all of the gained insights
relevant for their profession. Identifying these insights may be beneficial for the improvement
of the experiments. Future surveys will ask for the greater or lesser beneficial insights gained
and whether these are considered useful outside the university.
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Acknowledgements
This research was approved by the Faculty of Electrical Engineering and Computer Science
of TH Ingolstadt and the College Human Ethics Advisory Network of RMIT, Melbourne.
The project "Academic Education Initiative for E-Mobility Bavaria - Saxony" and the Faculty
of Electrical Engineering and Computer Science at THI provided funding which was used for
the development and the production of the laboratory devices.
This research was not possible without students, generously ready to take part in the study
to improve the learning outcome of future groups.
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