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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.
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
Hands-on Experiments vs. Computer-based Simulations
in Energy Storage Laboratories
F. Steger1
a,b
Research Associate/Lecturer
E-mail: fabian.steger@thi.de
H.-G. Schweiger a
Professor
E-mail: hans-georg.schweiger@thi.de
a TH Ingolstadt, Germany
A. Nitsche
a
Research Associate
E-mail: alexander.nitsche@thi.de
I. Belski b
Professor
E-mail: iouri.belski@rmit.edu.au
b RMIT Melbourne, Australia
ABSTRACT
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.
Conference Key Areas: Open and Online Engineering Education, Engineering
Education Research, Sustainability and Engineering Education
Keywords: Hands-on experiment, Simulated experiment, Battery experiment,
Learning-modes comparison
1 Corresponding Author: F. Steger, fabian.steger@thi.de
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
INTRODUCTION
It has been shown that hands-on student laboratory work can significantly influence
the outcomes of student learning [1]. Nevertheless, many universities and vocational
training institutions conduct laboratories as simulated experiments instead [2]. This is
due to the costs of proper laboratory equipment to engage students in effective
learning. Another reason is the increased amount of supervision to conduct hands-on
labs safely, especially when the object of the experiment is potentially dangerous, such
as lithium-ion battery cells [3].
1 RATIONALE
Numerous studies have presented mixed opinions on whether hands-on laboratory
work is more conducive to learning than the simulated laboratory [4, 5, 6].
Reflecting on the results of such studies, Ma and Nickerson concluded that many
studies did not allow the researchers to reach definite conclusions [2].
Making clear conclusion on learning effectiveness of different modes of laboratory
exercises is hampered by design of the abovementioned studies. For example,
students conduct hands-on experiments in groups whilst on campus, but are engaged
in simulated exercises over the web and individually [6]. As the learning mode is mixed
with other influences (in this case supervision, cooperative learning effects, distance
learning, instructional papers, synchronicity, etc.), such research compares the
combination of all aspects and is unable to specifically identify the difference in learning
effectiveness of hands-on and simulated experiments.
The present 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. In order to provide more reliable insights, this study
optimized research methodology to avoid other effects on the learning outcome during
laboratory work. The student experiments were strictly designed in such a way that the
content and procedure were identical in each mode.
2 APPROACH
Each group was taught the same four content areas related to lithium-ion battery cells:
two as practical hands-on experiments and two as computer-based simulations. The
first group completed the even laboratory sessions as hands-on experiments and the
odd ones as computer-based simulations. Planned as a cross-over study, the second
group of students were taught the same topics by means of the opposite learning
mode. To evaluate the influence of the mode on student learning, short tests on
knowledge gained during the previous experiment were conducted at the beginning of
the following laboratory session. The mean group results were compared, to answer
the question: which mode has been more successful? Additionally, the students were
asked for their opinions on each laboratory session in an online survey.
2.1 Learning objectives of the laboratory
Electrochemical cells are a wide and rapidly evolving field. It was therefore decided to
limit the new laboratory to teach the most relevant transferable skills and knowledge
that are beneficial for the students’ future careers. First, they should strengthen their
understanding of the characteristic behaviour of battery cells. Second, there is a focus
on gaining practical knowledge of the most important parameters of cells and how to
determine these parameters self-sufficiently by means of appropriate experimental
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
setups. With these competencies students are enabled to design experiments with
energy storage systems.
The identified learning objectives where grouped to 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 designed in such a way that they could be 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, in the range, and why such
measurement is a misuse of the multimeter. They learn how to use alternative
procedures for low ohmic measurements, including a four-wire measurement in
AC and DC.
Contact Resistance (A2): Students conduct experiments with a variety of
contact resistance values of typical electrical connections in battery systems.
Isolation Resistance (A3): Students learn to estimate the influence of moisture
on the isolation resistance.
Open Circuit Voltage Curve (B): Students investigate the dependency of the
open circuit voltage of a cell 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 a
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 both charge and discharge cycles.
Instructions affect the learning outcome of an experiment, e.g. the guidance level can
strongly influence student exploration [7]. Thus, for each experiment only a single set
of instructions was created and then used in both laboratory modes.
2.2 Creating two comparable groups for the cross-over study
Over the last two years, students enrolled in the energy storages course, were split
into two comparable groups based on their prior practical experience to ensure a
similar group environment for the individual students.
Forty students were enrolled in the laboratory subject in summer-semester 2016. Thirty
students were enrolled in summer-semester 2017. The full semester group in the study
program "Electrical Engineering and Electric Mobility" at Technische Hochshule
Ingolstadt (THI) is participating in the mandatory laboratory. All students were asked
to join the study.
To conduct this educational research as a cross-over study, it was essential to
separate the enrolled students into two comparable groups. In this type of study,
differences of the compared groups average performances are detected and
equalized by statistics. With the goal of isolating the learning mode in the experiment,
we have to consider the in-group interaction in laboratories. Webb found that the same
student may have different experiences in different groups, with consequent effects on
his or her learning [8].
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
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 preliminary questionnaire was
developed. 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.
2.3 Conducting laboratories in content areas A to D
Each group completed experiments in the four main content areas, two as practical
hands-on experiments and two as computer-based simulations. The first group
completed the even experiments as hands-on experiments and the odd ones as
computer-based simulations. Planned as cross-over study, the second group of
students were taught the same topics by means of the opposite learning mode.
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 [10] and the
battery cell was used. To minimize influences from the user interface to control the
experiments, the simulation was accessed through the same graphical user interface
as the real hands-on devices. The simulation model emulates all observed effects of
the real battery cell and the hands-on devices. The cell simulation model was
parametrized to match the outcome of the hands-on experiments.
Each group was split into smaller learning groups of three to five students. The
laboratory groups and the learning groups remained unchanged to limit any effects on
the result caused by changing cooperative learning.
The students worked autonomously in a supervised environment. All groups were
asked to prepare a written laboratory report for each content area before the following
session.
2.4 Online survey after conducting the experiments
The students where asked for their thoughts on each laboratory session in a short and
fully anonymous online survey. This survey is conducted in both laboratories of the
department (chemistry and energy storages) to improve the laboratories. Adequate
questions were evaluated to compare the learning modes:
(1) “By conducting the experiment I gained new insights today.” (Original: “Ich habe
heute durch den Versuch neue Erkenntnisse gewonnen.“) This was a yes/no answer
and coded 1 or 0.
(2) “At which point in the experiment did you have the biggest problem proceeding with
the experiment?” (Original: „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.
(3) “The procedure of the experiment is quite difficult (1) / feasible (0.5) / easy (0)
(Original: „Die Versuchsdurchführung ist recht schwer (1) / machbar (0.5) / leicht (0)“)
The answers were coded in a three step Likert scale.
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
(4) “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.” (Original: „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)
2.5 Testing the learning outcome
To evaluate the influence of the mode on the student learning, written tests on
knowledge gained during the previous experiment were conducted at the beginning of
the next laboratory session.
These tests lasted ten minutes and contained a mix of descriptive and multiple choice
questions, free answers, and drawings. A positive point system (similar to tests for
giving a mark) was used to evaluate the results.
The tests were conducted anonymously. Students were coded through the same self-
created code-word used in the questionnaire for grouping.
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.
For the tests, the goal was to always use the computer lab to provide the same
environment sitting at a desk while completing the tests. The tests were evaluated and
rated using a point system. The average group results between the hands-on and
simulated modes were compared, to answer the question: which mode was more
successful to transfer the knowledge of the experiment?
3 FINDINGS
3.1 Learning outcome (ten minute tests)
The second run of the experiment is conducted with 30 students in summer semester
2017. In year 2016 with 40 students the range of individual scores was from 12% to
85% for hands-on, and from 12% to 88% for the simulated mode. The distribution of
all results was normal. As seen in Table 1, for three content areas (A to C) a weak
effect towards benefits of the hands-on mode was measured. There was a slight trend
demonstrating hands-on laboratory sessions led to a better knowledge acquisition
compared to simulated experiments. 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).
3.2 Student feedback
The student feedback results are based on all results of the first iteration and the results
of content area A of the current ongoing laboratory in 2017.
(1) In hands-on mode more students expressed they gained new insights (81% vs.
55%, Cohen's d = 0.56, medium effect). Pearson’s correlation between mode and new
insights was 0.28 and significant (p=0.005).
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
(2) A similarly large share of the students mentioned problems while conducting the
hands-on experiments or their simulation equivalents (42% hands-on vs. 43% in
simulation); Cohen's d = -0.02 is showing that there was no effect.
(3) The engagement in the simulated experiments was stated to be a small amount
(8% of scale) more difficult (0.38 hands-on vs. 0.46 simulation). Cohen's d = -0.33
demonstrates a weak effect.
Table 1. Results of 2016 [9], Effect hands-on vs. simulated
Content
Area Sample
Size
Percentage
of points
Mean Value
hands-on
Percentage
of points
Mean Value
simulated
Percentage of
points
Std.
Deviation
both modes
(Cohen's d)
"hands-
better learning
A
37
38%
33%
16%
B
39
54%
49%
16%
C
35
52%
47%
18%
D
37
45%
45%
15%
all
148
47%
44%
17%
(4) 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 (0.58 hands-
on vs. 0.55 simulation, Cohen's d = 0.19, very weak effect).
No significant correlations between these items were found except between (1) and
(4), where Pearson’s correlation was 0.41 (p<0.001) and Spearman’s rho was 0.38.
Looking at both modes separately, the correlation in the simulation mode was stronger
(Pearson's r 0.52, p<0,001; Spearman's rho 0.52; N=49) compared to the hands-on
mode (Pearson's r 0.23, p=0,115; Spearman's rho 0.14; N=48).
Due to a low response rate for this questionnaire (31%) in the first year (2016), in the
second year (2017) the students were recompensed for their time to fill out a
questionnaire. Additional 5% of points were added to the grading of the laboratory
protocol, which makes it easier to reach the minimum level to pass the subject. In
experiment A, which had already been conducted at the writing of this publication, the
return rate increased to 77%.
4 CONCLUSIONS
4.1 Learning outcome
This study showed that the described methodology is applicable to focus on the
comparison of two learning modes. By excluding many other influences on the
comparison of the learning outcome, the results show only a small difference. The
slightly better learning results of the hands-on mode are not significant. Some of the
excluded factors might have greater impact on student learning than estimated
previously. To get statistically significant results more data collection is necessary. This
study is going on through 2018 at THI and the methodology will be tested at more
universities with different types of students (e.g. international students, summer
schools).
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
4.2 Online survey
The contrast between both modes in the student’s subjective opinion about gained
knowledge (1) is much more significant than in the objective results of the ten minute
tests. The students’ opinions show advantages of the hands-on mode: although one
cannot determine the students understood more in this mode, one can conclude they
gain more confidence in performing similar tasks. In a limited capacity, self-confidence
can be considered beneficial for the students' further development by increasing their
intrinsic motivation.
The correlation between (1) and (4) 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. The weaker correlation between (1) and (4) in the hands-
on mode suggests that even when the students think they gained additional insights,
the students do not consider all of the insights relevant for their profession. Identifying
these insights may be beneficial for the improvement of the experiments. Therefore,
future surveys will ask for the most beneficial insights gained and whether they
consider them useful for their future work outside the university.
Regardless of the learning mode, more than forty percent of the students think they do
not benefit in their professional life (4) from the experiments. This is quite disappointing
and leads to the possibility of asking for missing content students estimate as important
for their future profession.
On one hand less students mention problems with the hands-on experiments (2), while
on the other hand they feel the simulated variants to be more difficult (3). When taking
a deeper look at the answers from the students, it becomes clear that these results are
not correlated. A common mentioned problem was the lack of available measurement
devices. Also problems tied to the use of the software could be considered as neutral,
as the same software was used in both modes. The difference between the described
levels of difficulty of the learning modes (3) raises the question for these reasons. At
the time of writing we do not have a final conclusion on this weak effect. For the future
evaluation of the study we will ask the students to describe the difficulty in a free text
answer.
The actual methodology does not allow one to find correlations between individual
learning outcome and student’s feedback, as the standard online feedback form is not
asking for the self-created code-word that was used in the tests. We plan to implement
the new questions and this missing information to a new questionnaire in paper form
and use it for the next iteration.
5 ACKNOWLEDGMENTS
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.
Many thanks go to the laboratory engineer Sönke Barra for his assistance while
planning, preparing, and conducting the experiments. The devices used in the
laboratory were built from finance of the Faculty of Electrical Engineering and
Computer Science at THI.
This research was not possible without students, generously ready to take part in the
study to improve the learning outcome of future groups.
45th SEFI Conference, 18-21 September 2017, Azores, Portugal
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F. Steger, A. Nitsche, H.-G. Schweiger and I. Belski, Teaching Battery Basics
in Laboratories: Comparing Learning Outcomes of Hands-on Experiments and
Computer-based Simulations, Proc. of the 27th Austral
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Engineering Education (AAEE) Annual Conference, Coffs Harbour, 2016.
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... The participants were given 10 min to complete each test. Since the environment influences test results [38], tests took place in the same environment regardless of the experimental condition [41]. 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. ...
... 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. ...
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... 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. ...
... The described battery test system allows students to easily conduct the hands-on experiments [7]. The laboratory sessions and devices may give other educators a base to start battery laboratories. ...
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... 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. ...
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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.
... 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]. ...
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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
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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.
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The present study compares the relative merits of virtual and real educational laboratories in science and engineering education, in terms of their educational effectiveness and if they were the most appropriate for learning. The age of the students was also investigates as a possible factor affecting the outcome. The authors of the present paper started by identifying 67 recent and mutually compatible research papers (articles, doctoral theses, and reviews) and reviewed their content performing a meta-study to discover their findings about the most effective laboratory type. Web-based tools were used, such as e-journals, databases, thematic guides, and portals, catalogues of other libraries offered by a variety of universities. A critical analysis followed to compare findings and reach decisions. In a corollary section of the study, the authors conducted some semi-structured discussions with 25 experienced science teachers of secondary, primary, and tertiary education, for verification purposes. Discussions followed, all participants being arranged in 5 different groups, focus-ing on still open topics in need of further clarification. The present two-prong analysis resulted in a number of interesting results, presented herein, on the relative effectiveness of virtual and real laboratories as a factor of student age.
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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.
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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
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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.
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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.
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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.
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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)
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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.
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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.
Design and experimentation in undergraduate mechanical engineering
  • D J Magin
  • A E Churches
  • J A Reizes
Magin, D. J., Churches, A. E., and Reizes, J. A. (1986), Design and experimentation in undergraduate mechanical engineering, Proc. of the Conference on Teaching Engineering Designers, Institution of Engineers, Sydney, pp. 96-100.
Lithium-Ion Batteries, Linden's Handbook of Batteries 4th Edition
  • Jeff Dahn
  • Grant M Ehrlich
Dahn, Jeff and Ehrlich, Grant M. (2011), Lithium-Ion Batteries, Linden's Handbook of Batteries 4th Edition, McGraw-Hill, Town, pp. 26.1-26.79.