Conference PaperPDF Available

Teaching Battery Basics in Laboratories: Comparing Learning Outcomes of Hands-on Experiments and Computer-based Simulations

Authors:

Abstract

BACKGROUND Understanding the characteristics of rechargeable batteries is essential for a successful career in the field of research and development of hybrid and electric cars. It has been shown that hands-on laboratory work can significantly influence the outcomes of student learning. However, universities and vocational training institutions need proper laboratory equipment to engage students in effective learning of batteries' behaviour. Increased amount of supervision to conduct hand-on labs safely as well as costs of specialised laboratory equipment make hands-on laboratories expensive. Therefore, many universities conduct such laboratories as simulated experiments. PURPOSE The aim of this study was to compare the learning outcomes of laboratory work on lithium-ion battery cells and components of battery systems conducted in two different modes: as a practical hands-on exercise and by means of computer-based simulation. The research had a strong focus on the learning mode of the laboratory experiment, the method was designed to avoid other effects on the result. DESIGN/METHOD The students were split into two comparable groups based on their prior practical experience to ensure a similar background level of the two groups. Each group was taught four content areas: two as practical hands-on experiments and two as computer-based simulations. One group completed the even laboratory sessions as hands-on experiments and the odd ones as computer-based simulations. The other group completed the odd laboratory sessions as hands-on experiments and the even as computer-based simulations. To evaluate the influence of the learning mode onto the student learning, anonymous 10-minute tests on knowledge gained during the previous experiment were conducted at the beginning of the next laboratory session. The average group results between hands-on and simulated mode were compared, to answer the question, which mode was more successful to transfer the knowledge. The method excludes learning synchronicity/distance learning/supervision effects, and is focused on the mode. RESULTS Forty students took part in the study. Three of four content areas showed weak to moderate effect: hands-on laboratory sessions led to a better knowledge acquisition compared to simulated experiments. One content area did not show any effect of study mode. Overall learning results of hands-on experiments were slightly better than that of simulated laboratories (weak effect, Cohen's d = 0.22), but the difference in performance was not statistically significant. CONCLUSIONS This study showed that the described methodology is applicable to focus on the comparison of two learning modes. The slightly better learning results in hands-on mode are not significant. To get statistically significant results, more data collection is necessary. KEYWORDS Hands-on experiment, simulated experiment, student experiment, battery experiment, comparing learning-modes
AAEE 2016 CONFERENCE
Coffs Harbour, Australia
This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by/4.0/
1
Teaching Battery Basics in Laboratories:
Comparing Learning Outcomes of
Hands-on Experiments and Computer-based Simulations
Fabian Stegera,b, Alexander Nitscheb, Hans-Georg Schweigerb, and Iouri Belskia.
School of Engineering, RMIT, Melbourne, Australiaa
Faculty of Electrical Engineering and Computer Science, UAS Ingolstadt, Germanyb
Corresponding Author Email: fabian.steger@thi.de
BACKGROUND
Understanding the characteristics of rechargeable batteries is essential for a successful career in the
field of research and development of hybrid and electric cars. It has been shown that hands-on
laboratory work can significantly influence the outcomes of student learning. However, universities and
vocational training institutions need proper laboratory equipment to engage students in effective
learning of batteries' behaviour. Increased amount of supervision to conduct hand-on labs safely as
well as costs of specialised laboratory equipment make hands-on laboratories expensive. Therefore,
many universities conduct such laboratories as simulated experiments.
PURPOSE
The aim of this study was to compare the learning outcomes of laboratory work on lithium-ion battery
cells and components of battery systems conducted in two different modes: as a practical hands-on
exercise and by means of computer-based simulation. The research had a strong focus on the
learning mode of the laboratory experiment, the method was designed to avoid other effects on the
result.
DESIGN/METHOD
The students were split into two comparable groups based on their prior practical experience to
ensure a similar background level of the two groups. Each group was taught four content areas: two
as practical hands-on experiments and two as computer-based simulations. One group completed the
even laboratory sessions as hands-on experiments and the odd ones as computer-based simulations.
The other group completed the odd laboratory sessions as hands-on experiments and the even as
computer-based simulations. To evaluate the influence of the learning mode onto the student learning,
anonymous 10-minute tests on knowledge gained during the previous experiment were conducted at
the beginning of the next laboratory session. The average group results between hands-on and
simulated mode were compared, to answer the question, which mode was more successful to transfer
the knowledge. The method excludes learning synchronicity/distance learning/supervision effects, and
is focused on the mode.
RESULTS
Forty students took part in the study. Three of four content areas showed weak to moderate effect:
hands-on laboratory sessions led to a better knowledge acquisition compared to simulated
experiments. One content area did not show any effect of study mode. Overall learning results of
hands-on experiments were slightly better than that of simulated laboratories (weak effect, Cohen's d
= 0.22), but the difference in performance was not statistically significant.
CONCLUSIONS
This study showed that the described methodology is applicable to focus on the comparison of two
learning modes. The slightly better learning results in hands-on mode are not significant. To get
statistically significant results, more data collection is necessary.
KEYWORDS
Hands-on experiment, simulated experiment, student experiment, battery experiment, comparing
learning-modes
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 2
Background
Understanding the characteristics of rechargeable batteries is essential for a successful
career in the field of development and maintenance of electric cars (Müller and Goericke,
2012). At the moment more than thirty electric mobility study programs deliver subjects on
electric automotive engineering in Germany (NQuE, 2016). Universities of Applied Sciences
(UAS) are German institutions of higher education that differ from the traditional university in
Germany through their more vocational/practical orientation and wider utilization of
laboratories (Unseld and Reucher, 2010). Magin, Churches, and Reizes (1986) found that
laboratory work could significantly influence the outcomes of student learning. To provide
hands-on learning of batteries’ behaviour, training institutions need adequate laboratory
equipment. But industrial battery test benches are very expensive. Moreover, in order to
study temperature dependent effects of batteries, test benches need to be used together with
bulky temperature cabinets. Also, in order to guarantee students’ safety while handling
dangerous objects like battery cells, an increased amount of supervision is necessary.
Therefore, many universities that are unable to use proper industrial equipment for student
experiments conduct such laboratories as computer-based simulations and/or as remote
experiments (Ma and Nickerson, 2006). In German UASs teaching is based on hands-on lab
experiments, so a hands-on lab course was also chosen here to enhance the employability
of the graduates of this course. Therefore, the university funded the production of small-size
battery test benches for hands-on laboratory practicals, which were developed with funding
of the German Federal Ministry of Education and Research within the scope of the project
"Academic Education Initiative for Electric Mobility Bavaria/Saxony".
The aim of the experiment discussed in this study was to evaluate the influence of this newly
developed hands-on training on student learning. More specifically, the authors planned to
assess whether it led to better understanding compared to an equivalent simulation-based
laboratory work.
Purpose
There are several reasons to compare the effectiveness of learning when laboratory work is
conducted in different modes. Using the more successful mode will lead to an improvement
in student learning outcomes. Better trained engineers will develop superior products, in this
case electrified vehicles. If the effort to create hands-on training laboratory facilities is not
justified by improved learning results, it is possible to save money using cheaper solutions
like computer-based training. In this case, the funding could be allocated to other activities
that improve student learning.
Several researchers were interested in learning effectiveness of laboratory exercises
conducted in different modes, for example Engum, Jeffries, & Fisher (2003), McAteer, Neil,
Barr, Brown, Draper, & Henderson (1996) and Edward (1996). Reflecting on the results of
such studies, Ma and Nickerson (2006) concluded that in many studies the number of
student-participants were too small and did not allow researchers to reach definite
conclusions. Additionally, they found that the relative effectiveness of different kinds of
laboratories was seldom explored. Corter, Nicherson, Esche, Chassapis, Im, & Ma (2007)
compared learning outcomes for traditional hands-on labs, remotely operated labs, and
simulations in a physics engineering course. Learning outcomes of 306 students in two
cantilever beam experiments were assessed and were equal or higher after doing remote or
simulated experiments versus hands-on laboratories. As an outcome of the present
research, the community of engineering educators will gain more information that could help
answering the question whether real hands-on training enhances learning more than
simulated experiments.
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 3
Compared to past studies this research had a strong focus on the learning mode of the
laboratory experiment. Target of the piloted methodology was to compare the knowledge
results of the same laboratory-experiment (executed real or simulated). In literature
researchers e.g. replace and compare a well-tried hands-on experiment with a newly created
simulation. They improve both experiments (hands-on and simulated) independent to the
best solution they find in those modes. For example students learn in groups in the university
(hands-on), but alone at their working place (simulated). As the learning mode is mixed with
other influences (in this case supervision, cooperative learning effects, distance learning,
instructional papers) such research compares the two combinations of aspects. Another
example is the abovementioned study of Corter et al (2007) where in simulation mode the 3D
view was enriched with colour coded stress values, may causing the better results in
simulation mode. Keller et al. (2006) compared two levels of enrichment in a simulation
regarding current. They found no significant differences of conceptual understanding, but the
less enriched was significantly rated more enjoyable and more useful for the learning by the
students.
In the present research the laboratory experiments were developed in a way that every step
in the students experiment was identical, except for the usage of the hardware.
Design/Method
Creation of content areas A to D
The work was based on the identification of the main learning objectives that support the
existing theoretical subject on battery cell behaviors and battery systems design. These
objectives where grouped to four main content areas: A) contact resistance (including four-
conductor measurement); B) open-circuit voltage curve; C) internal resistance and power; D)
capacity and energy.
Based on these four content areas, laboratory experiments were developed in two modes: as
practical hands-on exercise that uses the abovementioned laboratory equipment as well as a
set of computer-based simulations.
A1 “low resistance measurements”: In this laboratory exercise students conduct low
resistance measurements. They are expected to discover that a multimeter is not the
right tool for low ohmic measurements and why. As a result of this exercise students
learn how to use the right alternatives and different devices to conduct a four wire
measurement in AC and DC.
A2 “contact resistance”: Here students discover exemplary values of contact
resistances of different electrical connections used in battery systems. They build up
knowledge in designing a cable lug connection and avoiding the main pitfalls.
A3 “isolation resistance”: This laboratory exercise deals with the usage of the
appropriate measurement equipment. Students learn to estimate the influence of
moisture and measurement period on the isolation resistance.
B “open circuit voltage curve”: In this experiment students investigate the dependency
of the open circuit voltage curve from the state of charge of two different lithium-ion
cell types. They are expected to learn that cells reach a stable state only over an
extended period of time.
C1 “internal resistance”: This exercise is devoted to the importance of the internal
resistance on the efficiency of a battery system. Students learn to use AC- and DC-
methods to measure internal resistances. Being aware of the temperature
dependency, students learn to approximate temperature changes caused by the
power loss in a cell. They also learn to deal with industry standards, select the right
measurement procedure and the effects causing misleading and faulty results.
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 4
C2 “power”: In this laboratory exercise students learn to estimate the maximum
discharge rate of battery cells. They practice to read and understand a cell data sheet
and estimate various cell limits. Students also learn how to calculate the power
density and comprehend the dependency of maximum discharge power from state of
charge, pulse duration and temperature.
D “energy and capacity”: In this experiment students determine the capacity of a
lithium-ion cell and learn about the factors influencing it. They familiarize themselves
with the Peukerts law and the energy efficiency of a cell charge and discharge cycle.
They also learn how to calculate the energy density of a battery cell.
The time required to create these practical experiments was very similar in both modes.
Instructions affect the learning outcome of an experiment, e.g. Chamberlain et al. (2014)
explored using an interactive simulation that the guidance level can strongly influence
student exploration. In this research for each experiment a single set of instructions was
developed, which was used in both laboratory modes. These instructions contained
introductory questions for preparation, guides for the experiments, and suggestions for the
analysis of the collected data and measurement results. Since the study program was
delivered in German, all documents were prepared in German.
Arrangement of students into two groups
Forty students were enrolled in the laboratory subject in summer-semester 2016. As this
study-module was a mandatory subject, the full semester group in the study program
Elektrotechnik und Elektromobilität (B. Eng.)(“Electrical Engineering and Electric Mobility”)
at the UAS in Ingolstadt, Germany was asked to participate in the study.
To conduct the educational experiment as a cross-over study it was necessary to separate
the enrolled students into two comparable groups. It was assumed that students with more
practical experience may perform better in laboratories than their peers with a lesser
practical background. Therefore, in order to assess the level of students’ practical experience
a questionnaire was developed. The questionnaire consisted of 17 statements that focused
on prior hands-on-experience (e.g. "I ever changed the tires of a car"). A four point Likert-
scale from “full yes” to “full no” was deployed. After analysis of student responses, students
were assigned to two laboratory groups in a way to ensure a similar mix of ‘practical’
students in each group. Student names were recorded. Instead, each student created a
code-word that could be used to identify the same individual by the experimenters and at the
same time keep her/him anonymous. Later two lists with code-words were publicized, telling
the students the weekday for the practical laboratory sessions.
During the introductory meeting the research aims and methods were clearly explained to the
students.
Conducting laboratories in content areas A to D
In order to ensure very similar experiences of students from both groups, laboratory
experiments were conducted in accordance to the schedule shown in Figure 1. Each group
completed experiments in four main content areas, two as practical hands-on experiments
and two as computer-based simulations.
One group completed the even laboratory sessions as hands-on experiments and the odd as
computer-based simulations. The other group completed the odd laboratory sessions as
hands-on experiments and the even as computer-based simulations. Both groups attended
hands-on and simulated experiments equally. Topics A1 to A3 were taught in one session.
C1 and C2 were sharing two sessions. Such arrangement of laboratory work allowed to
further reduce the influence of laboratory mode and practical inclinations of participants on
assessment of learning outcomes of hands-on and simulated sessions.
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 5
Figure 1: order of content areas over the semester
For the content area A in simulation-mode a newly created simulation-website was used. For
areas B to D a black box simulation of the hands-on equipment and the battery cell was
accessed through the same graphical user interface as the real hands-on devices. The
intention was to exclude any influences from the interface used by the students to control the
experiments. The simulation model emulates all observed effects of the real battery cell and
the hands-on devices which are used in hands-on mode. The cell simulation model was
parametrized to match the outcome of the hands-on experiments.
The laboratory sessions were conducted at the same time of a day. Each group (Wednesday
n=19, Friday n=21) was split into five smaller learning groups of three to five students. Webb
(1989) found that the same student may have different experiences in different groups, with
consequent effects on his or her learning. The learning groups remained unchanged for all
sessions to exclude any effects on the result caused by changing cooperative learning.
The students worked autonomously in a supervised environment. Each learning group used
a set of hands-on devices or one simulation PC. All groups were asked to prepare a written
laboratory report for each content area before the next session.
Physical actions and the environment may have influences on the learning outcome (Larson
et al, 2015). For the hands-on sessions, the students were standing at tables, whereas for
the simulation sessions a computer lab in sitting position was used.
Data collection/Testing the learning outcome
Anonymous written tests on knowledge gained during the laboratory exercises were
conducted at the beginning of the session that followed the appropriate laboratory session.
These “tests on the content area of the past laboratory sessionlasted ten minutes, and
occupied around four percent of the overall class time. These tests contained a mix of
descriptive and multiple choice questions, free answers and drawings. The questions were
directly related to the learning objectives defined for the content area under test. A positive
point system (similar to tests for giving a mark) was used to evaluate the results.
No names were recorded. Students were coded through the same self-created code-word
that was used in the questionnaire for grouping to keep everything anonymous. This
prepared the analysis of test results of individual students in future. The test papers were not
returned to the students.
The target was to keep time lapses between experiment and the corresponding test equal for
both groups (A 7 days, B 7 days, C 14 days, D 14 days). For organizational reasons, this
was not possible at the first content area A. Nevertheless, although the extended time period
between laboratory session and test in hands-on mode (9 days) the results in this mode were
better.
For the tests, the computer lab was used to provide the same environment while writing the
tests (sitting on a desk, like in usual written exams). An exception was made for the test on
content area A-simulated. This test was written for organizational reasons in the chemistry
lab.
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 6
Anonymity/Research Ethics
Any direct positive or negative effects for individual students regarding the study program
relevant marks had to be precluded. Like mentioned above, both groups attended hands-on
and simulated experiments equally. The result of the laboratory itself is a simple pass/fail,
depending on regular attendance and the abovementioned laboratory reports. The marks of
the accompanying theoretical module were generated according to the students'
performance in a written test created and conducted by an independent lecturer. However,
differences regarding the learning outcomes depending on the mode were expected. But as
both groups attended hands-on and simulated experiments equally, no inequitable results in
the theoretical test were anticipated.
From researcher’s side, it was not possible to identify individual students not taking part in
the research. All students had the free choice not to return any of the documents
(questionnaire for grouping, 10-minute tests on the sessions before).
Analysis
The tests on the individual laboratory sessions were evaluated and rated using a point
system. The average group results between hands-on and simulated mode were compared,
to answer the question, which mode was more successful to transfer the knowledge.
After more data collection, it is planned to answer in future, if students individually benefit
from one mode or the other. This study is going on every year till 2018.
Results
Table 1 shows group-wise results for all handled content areas, Table 2 compares both
learning modes.
Table 1: Results (average reached points) of groups
Group
Content
Area
Return
Rate
Sample
Size
Percentage of
points
Mean Value
Percentage of
points
Std. Deviation
Learning
Wednesday
A
100%
18
38%
17%
Wednesday
B
100%
19
49%
16%
Wednesday
C
100%
15
52%
16%
Wednesday
D
100%
17
45%
17%
Wednesday
all
45%
18%
Friday
A
100%
19
33%
16%
Friday
B
100%
20
54%
16%
Friday
C
100%
20
47%
20%
Friday
D
100%
20
45%
14%
Friday
all
46%
17%
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 7
Grouping
Both groups performed similar in sum over both modes (Table 1), a group bias was not
necessary. The Wednesday group got overall 45 per cent, the Friday group got overall 46
per cent of the maximum points. Standard deviations were also very similar in all tests
(between 14% and 20%). Therefore, it was assumed that the grouping was successful for the
experiment. When enough data is collected, it is planned to investigate the correlation
between individual performance and score in the questionnaire, to check the
abovementioned assumption that practical experienced students perform better in
laboratories.
Group results
A full return rate from present students was reached (Table 1). The authors clearly state that
no data was omitted, except for one filled test that was rejected as the student told he was
not attending the session before. As one question in the test regarding content area B was
verbalized in wrong way (and not answered by the students) is was not taken into account
while evaluation.
Mode results
Range of individual reached points was from 12% to 85% for hands-on, and from 12% to
88% for simulated mode. The Shapiro-Wilk-Test was telling that the distribution of all results
was normal.
With three content areas (A to C) a Cohen’s d around 0.3 (Table 2) was reached. According
to literature (e.g. Rubin, 2013), this was to interpret as weak to moderate effect. It was giving
the hint that hands-on conducted experiments led to a better understanding and knowledge
retention compared to simulated experiments. Content area D was showing no effect.
According to all returned tests the data was showing a weak effect (Cohen’s d=0.22) towards
better results in hands-on mode.
Table 2: Effect hands-on vs. simulated
Content
Area
Percentage of
points
Mean Value
hands-on
Percentage of
points
Mean Value
simulated
Percentage of
points
Std. Deviation
both modes
Effect size (Cohens d)
hands-on led to better
learning outcome
A
38%
33%
16%
0.28
B
54%
49%
16%
0.32
C
52%
47%
18%
0.30
D
45%
45%
15%
0.04
all
47%
44%
17%
0.22
Nevertheless, the 95% confidence interval for the mean of percentage of points was widely
overlapping for both modes (43% to 51% for hands-on, 40% to 48% simulated).
Grouped by learning mode, Levene's test was showing equality of variances
(p=0.741>>0.05). Independent samples T-test showed that although the reached point
percentages after hands-on experiments were higher than after simulations experiments, the
difference in performance was not statistically significant (p=0.215>>0.05).
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 8
Conclusions
This study showed that the described methodology is applicable to focus on the comparison
of two learning modes. With the instructions and learning objectives being identical and
avoiding to change cooperative learning effects, less influence was applied to the learning
outcome generated by the different modes. The modest increase also shows that some of
the excluded factors might have greater impact on student learning than estimated before.
Like this study was focusing on the learning mode with the exclusion of other influences, it is
recommended to investigate the quantitative impact of those other factors with a similar
strong focus on a single one.
The slightly better learning results in hands-on mode are not significant. To get statistically
significant results, more data collection is necessary. This study is going on every year till
2018 at UAS Ingolstadt and the methodology will be tested in different universities all over
the world.
The method used to distribute the students can also be applied in other situations, if
correlations between individual performance and score in the questionnaire will be found.
Than it might be e.g. feasible to provide students the learning mode that is most suiting their
predisposition so that the overall performance can be increased even further.
Proceedings, AAEE 2016 Conference
Coffs Harbour, Australia 9
References
Chamberlain, J. M., Lancaster, K., Parson, R. & Perkins K.K., (2014). How guidance affects student
engagement with an interactive simulation. Chem. Educ. Res. Pract., 15, 628
Corter, J., Nicherson, J., Esche, S., Chassapis, C., Im, S., Ma, J., (2007). Constructing Reality: A
Study of Remote, Hands-on, and Simulated Laboratories, ACM Transactions on Computer-Human
Interaction, Vol. 14, No 2, Article 7
Edward, N. S. (1996). Evaluation of computer based laboratory simulation. Computers & Education
26, 1-3, 123-130
Engum, S. A., Jeffries, P., & Fisher, L. (2003). Intravenous catheter training system: Computer-Based
education versus traditional learning methods. American J. Surgery 186, 1, 67-74
Keller, C. J., Finkelstein, N. D., Perkins, K. K., & Pollock, S. J., (2006). Assessing the Effectiveness of
a Computer Simulation in Introductory Undergraduate Environments. PERC Proceedings, AIP
Conf. Proc. 883, 121
Larson, M. J., Le Cheminant J. D., Hill K., Carbine K., Masterson T., & Christenson E. (2015).
Cognitive and Typing Outcomes Measured Simultaneously with Slow Treadmill Walking or Sitting:
Implications for Treadmill Desks. PLoS ONE 10(4), e0121309
Ma, J., & Nickerson J. V. (2006). Hands-On, Simulated, and Remote Laboratories: A Comparative
Literature Review, ACM Computing Surveys, Vol. 38, No 3, Article 7
Magin, D. J., Churches, A. E., & Reizes, J. A. (1986). Design and experimentation in undergraduate
mechanical engineering. Proceedings of a Conference on Teaching Engineering Designers.
Sydney, Australia. Institution of Engineers, 96-100
McAteer, E., Neil, D., Barr, N., Brown M., Draper, S., & Henderson, F. (1996). Simulation software in a
life sciences practical laboratory. Comput. and Education 26, 1-3, 102-112
Müller, K., & Goericke, D. (2012), Kompetenz-Roadmap, NPE Nationale Plattform Elektromobilität, AG
6 Ausbildung und Qualifizierung, Page 13
NQuE Netzwerk Qualifizierung Elektromobilität. (2016). Database Qualifizierungsangebote im Bereich
akademische Bildung. Retrieved June 18, 2016, from
http://www.nque.de/de/datenbank_akademisch.php
Rubin, A. (2013). Statistics for Evidence-Based Practice and Evaluation, Third Edition. Belmont:
Brooks/Cole. 144-145
Unseld, C., & Reucher, G. (2010). University types: Universities of applied science. Retrieved
September 15, 2015, from http://dw.com/p/Ovf8
Webb, N. M. (1989). Peer interaction and learning in small groups. International Journal of Educational
Research, Volume 13, Issue 1, 21-39
Acknowledgements
This research was approved by the Faculty of Electrical Engineering and Computer Science,
UAS Ingolstadt and the College Human Ethics Advisory Network of RMIT, Melbourne.
A lot of thanks to the lab engineer Sönke Barra giving assistance while planning, preparing
and conducting the experiments. This research was not possible without students,
generously ready to take part in the study to improve the learning outcome of future groups.
As part of the German Federal Government’s Showcase Regions for Electric Mobility, the
Federal Ministry of Education and Research provided funding for the project Akad.
Bildungsinitiative zur Elektromobilität Bayern/Sachsen” (“Academic Education Initiative for
Electric Mobility Bavaria/Saxony) which was used for the development of the prototypes of
the devices. The devices used in the laboratory were built from finance of the Faculty of
Electrical Engineering and Computer Science.
... These code words were also used to assign students into laboratory groups. (Steger 2016) 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. ...
... 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. ...
... 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. ...
Conference Paper
Full-text available
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.
... Each year, after analysis of the student responses, students were assigned to two laboratory groups to ensure a similar mix of students with practical experience in each group. [9] Each student created a code-word that could be used to identify the same individual, while keeping all participants anonymous. Later two lists with code-words were publicized to inform the students which group they were assigned for the laboratory sessions. ...
... A priority while planning both semesters was to keep time gaps between experiment and the corresponding test equal for both groups. For organizational reasons, this was not possible at the first content area A in 2016 [9]. In 2017 the laboratories are on the same weekday morning and afternoon, making it easier to keep the experiment -test time gaps equal for both groups. ...
... Content area D showed no difference between the modes. [9] 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 (p=0.09>0.05). ...
Conference Paper
Full-text available
pp121-128 This cross-over study compares student laboratory work conducted in two different learning modes: first as a practical hands-on exercise and second using computer-based simulations. The research methodology was optimized to avoid other effects on the learning outcome. To evaluate the influence of the mode, short tests on knowledge gained during the previous experiment were conducted at the beginning of the next laboratory session. In 2016, forty students have taken part. Overall learning results of hands-on experiments were slightly better than those of simulated laboratories, but the difference in performance was not statistically significant. The study is continuing in 2017 with 30 participants. In addition to the knowledge tests, after each laboratory session the students were asked for their opinion in an online survey. A similar percentage of the students stated the execution of the experiments is beneficial for their future professional life. In the hands-on learning mode more students expressed they have acquired new knowledge. Although more students assessed the simulated laboratories as more challenging compared to hands-on experiments, more students mentioned obstacles while conducting the hands-on equivalents.
... Laboratory experiments covered four content areas: (A) contact and isolation resistance; (B) open-circuit voltage; (C) internal resistance and power; and (D) energy of cells [25]. ...
... Participants of the study runs held in German (R1 and R2) took the tests one or two weeks after the respective laboratory exercises were conducted, prior to the next laboratory session. In order to equalize the influence of time on the ability to remember, equal periods of time were targeted between experimentation and the associated tests for both groups [25], [41]. Students that participated in the study runs conducted in English were handed a single test between one and two weeks after completion of the laboratory work. ...
Article
Full-text available
Contribution: Prior studies comparing the effectiveness of different laboratory learning modes do not allow one to draw a universally valid conclusion, as other influences are mixed with the learning modes. In order to contribute to the existing body of work and to add another piece to the puzzle, this article demonstrates an improved methodology to evaluate the effectiveness of computer-simulated laboratories in comparison to hands-on exercises using a battery basics practical course as a case study. Background: Computer-simulated experiments are becoming increasingly popular for conducting laboratory exercises in higher education and vocational training institutions. To ensure the consistent quality of laboratory learning, an accurate comparison between the results of simulated experiments and practical hands-on experiments is required. Intended Outcomes: In this article, the achievement of the following learning objectives were compared between the two laboratory modes: 1) comprehension of the most important parameters of battery cells and 2) knowledge on how these parameters can be determined using adequate experimental procedures. Application Design: To avoid interference of factors other than laboratory mode on the learning, laboratory instructions and experimental interfaces ensured identical execution of the experiments in the compared modes. Using a counterbalanced methodology, the two laboratory modes alternated by the session, while the experimental procedures remained constant regardless of the respective modes. Findings: Tests taken by the participants after conducting the laboratory experiments revealed that hands-on laboratories resulted in statistically significantly better student performance than simulated laboratories. This difference was even more pronounced for the participants that finished a vocational education and training program before the university studies.
... Es wurde angenommen, dass sich Studierende mit mehr praktischer Erfahrung hinsichtlich des Forschungsziels im Labor abweichend zu ihren Kommilitonen mit weniger praktischer Erfahrung verhalten. Daher versuchten die Autoren, mit einem Fragebogen jeweils zwei Vergleichsgruppen mit einem ähnlichen Mix an praktischen Fähigkeiten zusammenzustellen [7,9] . ...
... [11] Ergebnisse Objektive Lernergebnisse auf der Grundlage der schriftlichen Tests Im Jahr 2016 waren die Lernergebnisse der praktischen Durchführungen etwas besser als die der simulierten (Cohen's d = 0,22; p (2-seitig) <0,2), aber der Unterschied in der Leistung war statistisch nicht signifikant genug, um alleine darauf basierend Schlüsse zu ziehen. [7] Auch die Durchführung im Sommersemester 2017 deutete darauf hin, dass praktische Experimente zu einem besseren Wissenserwerb führten (Cohen's d = 0,34; p (2-seitig) <0,1). [9] Um spezifischere Aussagen treffen zu können, wurden inzwischen -jeweils leicht der jeweiligen Situation angepasste -weitere (auch internationaler besetzte) Durchführungen abgeschlossen: Summer School Ingolstadt [11] , Gastvorlesung in Chemnitz, einleitendes Praktikum eines Studiengangs an der Fakultät Maschinenbau. ...
Article
Full-text available
Viele Universitäten und Berufsbildungseinrichtungen führen simulierte Experimente in Praktika durch, um Kosten für Laborausstattung zu sparen. Diese können vor allem bei potentiell gefährlichen Lernobjekten wie z.B. Lithium-Ionen-Zellen sehr hoch sein. Die Effektivität simulierter Experimente im Vergleich zu praktischen Übungen wird in diversen Studien betrachtet. Da die meisten dieser Untersuchungen Studierende aus Versuchs- und Kontrollgruppen signifikant unterschiedlichen Bedingungen (angepasste Lernziele, Betreuungsumfang und -art, Fernlernen vs. Lernen an der Universität, unterschiedliche Lehrmaterialien) aussetzen, sind meist keine allgemein gültigen Schlussfolgerungen bezüglich des Erfolgs der Lernmodi möglich. Die hier beschriebene Studie vergleicht deshalb die Lernergebnisse aus praktischen Experimenten mit Versuchen auf Basis computergestützter Simulation unter Anwendung einer Methodik, bei der sowohl die Lernziele als auch das experimentelle Vorgehen der Studierenden strikt übereinstimmen.
... The biggest challenge while creating the experiments was the limited time for a single student's laboratory. [6] III. IMPEDANCE SPECTROSCOPY UPGRADE The laboratory was piloted in summer 2016 [6] and repeated after improvements in 2017 [7]. ...
... [6] III. IMPEDANCE SPECTROSCOPY UPGRADE The laboratory was piloted in summer 2016 [6] and repeated after improvements in 2017 [7]. In these runs the DC measurements were conducted with the developed equipment, while all AC resistance measurements depended on three additional sets of Hioki Battery Hitester 3554. ...
Conference Paper
Full-text available
Knowledge in this field of energy storage systems is essential for the development of electric vehicles. To allow practice for future employment through practical laboratory training, a battery test system for student laboratories was developed. A new laboratory was piloted in summer 2016 and has been conducted with several student groups. To remove the needs for additional devices to measure AC resistances, the firmware of the student battery cell test system was enhanced to allow frequency dependent injection and measurement. The upgraded device was showing acceptable results for student education and will be used in future.
... Funding by the Faculty of Electrical Engineering and Computer Science allowed a complete teaching laboratory at THI to be equipped with 13 battery test systems. The hardware and the newly created laboratory experiments were tested in various study programs on electric mobility [6]. The students have evaluated practical experiments that utilized the new system more favourably then similar experiments conducted using computer simulations [7]. ...
Conference Paper
Full-text available
Energy storage systems are vital for success of electric mobility. Thus, knowledge of energy storage systems behaviours is essential for the development of electric vehicles. Universities and vocational training institutions are urgently required to educate specialists in electrical storage systems. A safe and easy-manageable battery test system was developed to improve the outcomes of student learning of battery storage systems and to allow practice for future employment through hands-on laboratory training. This test system supports temperature-dependent experiments with different cell types including lithium-ion cells, and incorporates a redundant safety shut-off module that protects students from being injured. Based on this system, a new energy storage laboratory course was developed. This course covers four main content areas of battery energy storage: (1) contact & isolation resistance, (2) open-circuit voltage, (3) internal resistance & power, and (4) energy of cells. This new laboratory course was introduced in summer 2016 and has already been conducted with several full-time and part-time student groups.
Thesis
Full-text available
Background: Engineering courses often complement lectures with laboratory classes to optimise student learning outcomes and further develop valuable skills for future employment. Computer simulated experiments for conducting laboratory exercises have become increasingly popular in higher education and vocational training institutions to replace traditional hands-on laboratories. Reasons for this include for example, cost efficiency and repeatability. Research question: There has been a wide array of discussion on the efficacy of the two laboratory modes in teaching, both in general and for students in engineering fields (for example, chemical engineering or electrical engineering). However, many previous studies on this question did not reach a universally valid conclusion. The used methodologies mixed other influences with the impact of the investigated learning modes. These influences include for example accompanying lectures, experimental instructions, teachers, learning objectives, tests, working teams, and many more. Thus, the differences in results of these studies cannot be attributed to the laboratory mode only. The study conducted for this thesis investigated differences in learning outcomes of students in higher education when comparing two laboratory modes in the local domain: 1. In-person hands-on laboratories allow students to directly interact with the subject at hand, although this interaction might be mediated through technology or a user interface. 2. In-person simulated laboratories moderate all student interactions through a user interface. The properties of the investigated effect are simulated by computer software. The students work in a classroom equipped with computers on which the simulations are running. Since this study was focused solely on comparing different learning modes, all other aspects were held as constant as possible. Improvements that were theoretically possible in only one of the teaching methods (e.g. time-lapse in simulations) were not implemented in order to keep the surrounding conditions as equal as possible. Thus, the aim of the research was not to determine which of the investigated laboratory modes would be best for teaching a specific topic, but rather to investigate whether or not there are discernible differences in teaching success when conducting the same experiment in hands-on and simulated laboratories. The ultimate goal was to establish more reliable and generalisable insights into the influence of a particular laboratory mode on learning. The study did not include a remote laboratory condition; the comparison was only made between in-person laboratory teaching with proper laboratory equipment and simulations conducted in the local domain. An important note on demographics: a third of students at universities of applied sciences have completed apprenticeships in the German vocational education and training programs (VET) before enrolment. These VET programs mainly consist of practical on-the-job learning and aim to directly prepare apprentices for entering the job market. Due to the large size of this demographic and their previous experiences mostly with hands-on learning, it was of additional interest to see if VET-participants’ results differed significantly from those of their peers when confronted with the two laboratory modes. It was also of interest to see if the perception of the learning modes influenced the outcome. Methodology: This study was conducted in two consecutive phases on the example of a practical course teaching the basics of batteries (not related to physical manipulation of the batteries). A counterbalanced within-subject methodology was employed with German and international participants in nine study runs. The laboratory modes alternated, while the learning objectives and the experimental approach of laboratory exercises remained practically identical. In the first phase, the objective was to compare students’ learning success when working with hands-on laboratories and with overt computer simulations, respectively. The second phase was conceptualised to give insight into possible subjective influences of students’ perception of the two laboratory modes. In this phase, the simulation condition was hidden. Participants used hands-on equipment in both conditions. In the first condition, real measurements were shown; in the second condition, hands-on devices displayed simulated battery behaviour to investigate the influence of students’ perception. The participants were not aware of the differences in data sources. Besides the comparison of knowledge test results, questionnaires were employed to correlate prior, specifically technical, practical experience and previous apprenticeship training with the success of the knowledge transfer in both of the compared modes. Well-known personality tests were also employed in order to provide further insight into the subjects. The study collected subjective opinions regarding the laboratory modes in two ways: 1. Participants of the main study were asked to provide feedback after conducting a laboratory experiment. This method allowed for the indirect gathering of information about the difference in perception towards the two modes. 2. Persons who had either not yet started the laboratory or weren’t participating in the laboratory were asked to fill out a general questionnaire distributed amongst different universities in different countries. This method asked directly for subjective opinions regarding the learning modes. Finally, the THI university database was analysed to extract objective information about students with and without vocational training degree to gain broad background information about the compared groups. Outcomes: In the first phase, it was found that there were statistically significant differences in learning outcomes favouring the hands-on mode. When the simulation condition was overt, students with a background in vocational training before enrolment showed statistically significant trends towards better learning with hands-on experiments. Students in the international runs and Germans without a VET background performed similarly in both modes. In the second phase, when students were not aware that they were using simulations, both modes showed similar student learning across all student groups. Generally, simulations were reported as less relevant and their authenticity was called into question. A VET background seems to determine whether or not students had different levels of success in hands-on and simulated laboratories. As hidden differences in the simulations could be excluded from having been the reason for inferior learning results, psychological effects needed to be considered to comprehend the different laboratory modes’ effectiveness. The study outcomes lead to the conclusion that students’ personal perception of the laboratory modes, particular simulations, can have a significant impact on laboratory learning.
Article
Das Schlagwort Digitalisierung wird im Kontext der Hochschullehre häufig mit der Notwendigkeit assoziiert, digitale Tools einzusetzen. Jedoch umfasst der durch die Digitalisierung bedingte Transformations-prozess an Hochschulen deutlich weitreichendere Veränderungen. So entwickelt etwa die Hochschulleitung der Technischen Hochschule Nürnberg derzeit eine umfassende Digitalisierungsstrategie für die Hochschule. Unter Einbindung aller Betroffenen entsteht ein Gesamt-ansatz, um aktuellen und zukünftigen Entwicklungen angemessen zu begegnen. Bereits seit vielen Jahren bestehende Initiativen und Projekte werden in diese Gesamtstrategie integriert und weiterentwickelt, wie zum Beispiel die Online-Studienberatung (https://www.th-nuernberg.de/studium-karriere/studienorientierung-und-studienwahl/studienberatungsportal/), das Learning Lab (https://www.th-nuernberg.de/llab), Blended Learning (https://www.th-nuernberg.de/bl) oder Online-Self-Assesments (https://www.studiengangstest.de/portal/), um nur einige zu nennen. Die Technische Hochschule verfügt also über einiges an Erfahrungen in der Entwicklung und Anwendung digitaler Unterstützungsangebote in der Lehre.
Conference Paper
Full-text available
zum Beitrag: https://www.diz-bayern.de/images/documents/400/DiZ_FdL_2018_Tagungsband.pdf#page=60
Article
Full-text available
Purpose: This study compared cognitive (attention, learning, and memory) and typing outcomes during slow treadmill walking or sitting. Seventy-five healthy individuals were randomly assigned to a treadmill walking group (n=37; 23 female) or sitting group (n=38; 17 female). Methods: The treadmill walking group completed a series of tests while walking at 1.5 mph. The sitting group performed the same tests while sitting at a standard desk. Tests performed by both groups included: the Rey Auditory Verbal Learning Test and a modified version of the Paced Auditory Serial Attention Test. In addition, typing performance was evaluated. Results: Participants in the treadmill walking group performed worse on the Rey Auditory Verbal Learning Test for total learning than the sitting group; the main effect was significant (F(1,73)=4.75, p=0.03, ηp2=0.06); however, short- and long-delay recall performance did not differ between groups (p>0.05). For the Paced Auditory Serial Attention Test, total number of correct responses was lower in the treadmill walking group relative to the sitting group; the main effect was significant (F(1,73)=4.97, p=0.03, ηp2=0.06). The performance of both groups followed the same learning slope (Group x Trial interactions were not significant) for the Rey Auditory Verbal Learning Test and Paced Auditory Serial Attention Test. Individuals in the treadmill walking group performed significantly worse for all measures of typing (p<0.05). Conclusion: Walking on a treadmill desk may result in a modest difference in total learning and typing outcomes relative to sitting, but those declines may not outweigh the benefit of the physical activity gains from walking on a treadmill.
Article
Full-text available
Laboratory-based courses play a critical role in scientific education. Automation is changing the nature of these laboratories, and there is a long-running debate about the value of hands-on versus simulated laboratories. In addition, the introduction of remote laboratories adds a third category to the debate. Through a review of the literature related to these labs in education, the authors draw several conclusions about the state of current research. The debate over different technologies is confounded by the use of different educational objectives as criteria for judging the laboratories: Hands-on advocates emphasize design skills, while remote lab advocates focus on conceptual understanding. We observe that the boundaries among the three labs are blurred in the sense that most laboratories are mediated by computers, and that the psychology of presence may be as important as technology. We also discuss areas for future research.
Article
Full-text available
Incl. bibl., index
Article
We studied how students engaged with an interactive simulation in a classroom setting and how that engagement was affected by the design of a guiding activity. Students (n = 210) completed a written activity using an interactive simulation in second semester undergraduate general chemistry recitations. The same simulation – PhET Interactive Simulations' Acid–Base Solutions – was used with three written activities, designated as Heavy Guidance (HG), Moderate Guidance (MG), or Light Guidance (LG). We collected mouse click data and classroom field notes to assess student engagement with each type of activity. Simulation features were characterized as “prompted” or “exploratory” based on the presence or absence of explicit guidance in the written activity to use that feature. While students in every condition were engaged with the simulation and their activity, student interaction with “exploratory” features decreased significantly when more guidance was provided (LG = 85%, MG = 68%, HG = 9%, p < 0.0005). Lighter guidance groups explored more and attended to their simulation interactions, indicated by a redraw task in the week after use. These results indicate that activity design – in terms of guidance level – can strongly influence student exploration with an interactive simulation. We discuss the implications of these results for the design of activities to accompany simulations, including how to increase student practice in scientific inquiry.
Article
A significant number of HND/BSc engineering students work offshore. This results in their missing blocks of the course. Measures to assist them are being developed but an alternative to laboratory experimentation is problematic. Multi-media packages which include a computer-based simulation (CBS) are being developed. One of these has been piloted with full time students. Among the aspects evaluated was the degree to which the desired learning outcomes had been achieved. It was found that the gain in technical knowledge as gauged from assessments and the students' own perception was at least as good with the simulation as with the lab. Paradoxically the students rated their gain in practical ability highly but were rather disparaging in verbal comments about the CBS. Both groups rated their gain in understanding of the relationship between theory and practice highly. The objective of evaluation was to develop and enhance the package. The relevant literature was also surveyed and the paper includes pointers gained from that source. It was concluded that the approach, while not the equivalent of labs in all respects, is the optimum which can be produced. Its success, however, was dependent on the combination of text-based, video and computer components in the package.
Article
Describes experiences at the University of Glasgow (Scotland) with the use of simulation software in life sciences laboratories and discusses the results of evaluation procedures that included confidence logs, questionnaires, interviews, and grades. A table of statistical results relevant to confidence log comparisons and the postcourse questionnaire are appended. (LRW)
Article
This chapter discusses the kinds of peer interaction that influence learning in small groups and describes the characteristics of students, groups and tasks that predict different patterns of peer interaction. Based on previous empirical research, critical features of peer interaction include the level of elaboration of help given and received, and the appropriateness of responses to requests for help. Predictors of peer interaction in small groups include student ability, gender, and personality, and group composition on ability and gender. Hypotheses about important, but neglected, aspects of peer interaction that may predict learning are discussed.
Article
Background: Virtual reality simulators allow trainees to practice techniques without consequences, reduce potential risk associated with training, minimize animal use, and help to develop standards and optimize procedures. Current intravenous (IV) catheter placement training methods utilize plastic arms, however, the lack of variability can diminish the educational stimulus for the student. This study compares the effectiveness of an interactive, multimedia, virtual reality computer IV catheter simulator with a traditional laboratory experience of teaching IV venipuncture skills to both nursing and medical students. Methods: A randomized, pretest-posttest experimental design was employed. A total of 163 participants, 70 baccalaureate nursing students and 93 third-year medical students beginning their fundamental skills training were recruited. The students ranged in age from 20 to 55 years (mean 25). Fifty-eight percent were female and 68% percent perceived themselves as having average computer skills (25% declaring excellence). The methods of IV catheter education compared included a traditional method of instruction involving a scripted self-study module which involved a 10-minute videotape, instructor demonstration, and hands-on-experience using plastic mannequin arms. The second method involved an interactive multimedia, commercially made computer catheter simulator program utilizing virtual reality (CathSim). Results: The pretest scores were similar between the computer and the traditional laboratory group. There was a significant improvement in cognitive gains, student satisfaction, and documentation of the procedure with the traditional laboratory group compared with the computer catheter simulator group. Both groups were similar in their ability to demonstrate the skill correctly. CONCLUSIONS; This evaluation and assessment was an initial effort to assess new teaching methodologies related to intravenous catheter placement and their effects on student learning outcomes and behaviors. Technology alone is not a solution for stand alone IV catheter placement education. A traditional learning method was preferred by students. The combination of these two methods of education may further enhance the trainee's satisfaction and skill acquisition level.
Constructing Reality: A Study of Remote, Hands-on, and Simulated Laboratories
  • J Corter
  • J Nicherson
  • S Esche
  • C Chassapis
  • S Im
  • J Ma
Corter, J., Nicherson, J., Esche, S., Chassapis, C., Im, S., Ma, J., (2007). Constructing Reality: A Study of Remote, Hands-on, and Simulated Laboratories, ACM Transactions on Computer-Human Interaction, Vol. 14, No 2, Article 7
Assessing the Effectiveness of a Computer Simulation in Introductory Undergraduate Environments
  • C J Keller
  • N D Finkelstein
  • K K Perkins
  • S J Pollock
Keller, C. J., Finkelstein, N. D., Perkins, K. K., & Pollock, S. J., (2006). Assessing the Effectiveness of a Computer Simulation in Introductory Undergraduate Environments. PERC Proceedings, AIP Conf. Proc. 883, 121