Gamified Hands-on-Training in Business Information Systems:

An Educational Design Experiment

Anke Schüll

, Laura Brocksieper and Julian Rössel

Department of Business Information Systems, University of Siegen, Kohlbettstr. 15, Siegen, Germany

Keywords: Self-paced e-Learning, Gamification, ERP-training.

Abstract: The Covid-19-pandemic confronted lecturers worldwide with the sudden necessity to develop concepts

suitable for distance education. Students’ motivation became a crucial aspect of prolonged e-learning

situations. This paper reports on an educational design experiment to change a hands-on-training on SAP

ERP-Systems into a gamified self-paced e-learning environment. This training accompanies a lecture on

Business Information Systems for first-semester students. Allowing mistakes to happen kept the attention

high and made achievements within the learning environment more rewarding. An anonymous online survey

confirmed the relevance of self-paced learning for learning efficiency. Even though a positive impact of

“mistakes” on learning efficiency was not confirmed, comments and statements of the participants pointed

towards an effect on learning, worth further research. We contribute to the body of knowledge by providing

lessons learned on gamified self-paced e-learning within university courses. It could be verified that business

process-related hands-on-training within an ERP-System could be implemented in a gamified self-paced e-

learning environment without compromises regarding scope or scale of the content.

1 INTRODUCTION

The Covid-19-pandemic changed the working and the

learning situation globally. Work motivation and

behavior, health, well-being, job and career attitude

changed (Spurk and Straub, 2020). Schools and

universities closed. The impact of social isolation on

students and their attitude towards using learning-

management systems became a topic of research

(Raza et al., 2021). Lecturers worldwide were

confronted with the sudden necessity to develop

concepts suitable for distance learning. Keeping

students motivated became a crucial aspect of

prolonged e-learning.

Gamification describes the use of playful

elements in non-game activities, e.g. to increase

engagement within an otherwise less enjoyable

context (Bakker and Demerouti, 2007). Elements of

gamification are e.g. rewards, badges, and high scores

(Turk and Goren, 2017). Using gamification to

support the learning process can strengthen students'

perseverance and resilience (Aguilera and Martínez,

2017), arouse their interest, and increase their

motivation.

https://orcid.org/0000-0001-9423-3769

More fun, a more intense flow experience (Herzig

et al., 2012) and better learning outcomes than with

conventional training methods (Alcivar and Abad,

2016) were reported on gamified ERP-system

training. In a comparative study, gamification was

found to increase motivation and interest, even

though it was perceived to be more time-consuming

(Barata et al., 2013).

This let to an Educational Design Experiment: the

transformation of the hands-on-training within the

SAP enterprise resource planning-system (ERP-

System) towards a gamified self-paced e-learning

environment. The training accompanies a lecture on

Business Information Systems (BIS) for first

semester students of the University of Siegen. Most

of these students had no preliminary knowledge of

the topic.

This paper contributes to the body of knowledge

by providing lessons learned on gamified self-paced

e-learning within university courses. Taking wrong

turns, making mistakes and failure are part of the

challenge that make achievements more rewarding.

Within e-learning environments, learning from

mistakes is a still underdeveloped field of research.

Schüll, A., Brocksieper, L. and Rössel, J.

Gamiﬁed Hands-on-Training in Business Information Systems: An Educational Design Experiment.

DOI: 10.5220/0010618401650171

In Proceedings of the 18th International Conference on e-Business (ICE-B 2021), pages 165-171

ISBN: 978-989-758-527-2

165

The educational design experiment presented in this

paper reports on the transformation of a hands-on-

training towards an e-learning environment with

elements of gamification, self-paced learning and

learning from mistakes.

2 LITERATURE REVIEW AND

FORMULATION OF

HYPOTHESES

Educational design research is an experimental

approach on the iterative development of solutions to

practical and complex educational problems

(McKenney and Reeves 2021, 2014). Self-regulated

learning and a constructive handling of mistakes

became two requirements that shaped the design of

the e-learning environment. Design decisions were

made to give the students a maximum of control over

their learning process, and to allow mistakes to

happen as part of the learning progress.

The implementation allows an evaluation of

student’s perception of these concepts build on

personal experience. To assess students’ perception,

two independent variables are evaluated, whose

impact is assumed to influence learning efficiency:

self-paced learning and learning from mistakes.

Within a university, students have to learn

autonomously in a self-regulated manner (Anurugwo

2020). Self-regulated learning platforms encourage

students to actively get involved in the learning

process (Anthonysamy et al., 2020) and allow them

to learn and understand the content at their individual

speed (Turk and Goren, 2017; Anurugwo, 2020).

Self-paced learning can improve students’ academic

achievements and prepares for life-long-learning

(Bautista, 2015). Self-paced learning environments

can support students in building cognitive and

metacognitive knowledge based on hands-on

experiences (Bautista, 2015). Self-paced learning

tools support learning effectively (Marshman et al.,

2020).

Therefore, we postulate:

H1: Self-paced Learning Has a Positive Effect on

Learning Efficiency.

Hands-on-training allow students to get actively

involved. The possibility to make mistakes during the

learning process in combination with feedback,

promotes the learning success (Tulis et al., 2016).

Learning speed, understanding of the underlying

concepts and a transfer to new tasks were improved

by a constructive handling of mistakes during the

learning process. Similar findings were confirmed in

a study by Metcalfe (2017). Confronted with critical

situations, those subjects who were exposed to errors

and mistakes during their learning process reacted

better and could adapt more flexibly than those who

were spared the confrontation with errors during the

learning process: learning perseverance improves.

This is consistent with literature on resilience, a trait

that is acquired at least in part through learning

(Coutu, 2002). Thus, the second hypothesis results in:

H2: Mistakes Have a Positive Impact on Learning

Efficiency.

During this experiment, student teachers supported

two groups of up to 30 students each. They took

notice of student reactions and observed their

progress within the ERP-System. To avoid

influencing the behaviour of the participating

students, all interactions remained undocumented. To

assess the perception of the requirements

implementation, an anonymous online survey was

included into the e-learning environment at the end.

3 EDUCATIONAL DESIGN

EXPERIMENT

Educational design experiments can be used to solve

a problem (here: taking up the sudden challenge of

prolonged and distant e-learning), to put knowledge

to innovative use (here: longterm experiences with

hands-on ERP-training and research literature led to

a new concept for gamified self-paced learning),

and/or to increase robustness and systematic nature of

design practices (McKenney and Reeves, 2021).

Herzig et al., (2012) and Alcivar and Abad (2016)

reported on the implementation of hands-on ERP-

training within an e-learning environment. In this

educational design experiment, aspects of

gamifications are included. The e-learning

environment covers the full content of the course in a

playful way. No compromises were made regarding

scope or scale of the content. A game board visualizes

the context (Figure 1). The spokes in the background

of the layout are related to the topic: the production

of bicycles. Even though the learning management

system Moodle is available at our university, this

game board was placed outside Moodle on a website,

an environment unrelated to learning. To raise

students’ interest was the intentions and once they

started the learning process to keep them motivated

all the way through. A variety of media was mixed to

avoid monotony. Suiting to the topic, the shooting of

some videos was located in the landscape on local

bicycle routes. Associations with leisure activities

were expected to bring more ease into the learning

ICE-B 2021 - 18th International Conference on e-Business

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process.

Learning objects of the hands-on-training in the

SAP ERP-system are logistical processes, material

and information flows. Business processes

(procurement, manufacturing, stock management,

sales and support) and the supportive use of ERP-

Systems are at the core of this course. All student

learners are responsible for their own materials. A

multi-layered bill-of-material (BOM) is used,

containing raw materials, semi-finished and finished

products (here: a bicycle). The processes are

integrated and complexity increases with the

progress: sales orders trigger manufacturing and

manufacturing can only be executed, if raw materials

are in stock. If not, procurement of the raw materials

is required. The interconnectedness between the

many processes leads to increased complexity.

Figure 1: Layout.

The elements of the game board are interactive.

Explanations of the contents of the case studies and

screencasts on the functionality of the ERP-system

are part of the learning environment. The pace is self-

regulated. The graphic elements of the game design

are largely self-explanatory to enable intuitive use:

 Each plate corresponds to a mission (a scenario or

a subprocess),

 Arrows guide through the roadmap, marked in

different colors. Steps back to preceding sub-

processes or scenarios are marked in red.

 Each scenario is described by three elements:

- A process diagram (BPMN 2.0). Explanatory

videos on the process are embedded within these

diagrams.

- Data sheets.

- Screencasts.

 QR codes link to mini-quizzes.

 Flashing blue lights direct to a trouble-shooting

file that empowers students to "self-help”, if

mistakes occur.

 Feedback option/evaluation.

Goals and process steps are explained at the

beginning of each scenario via process diagrams

according to BPMN 2.0 (Business Process Model and

Notation). Videos add explanations to the process

diagrams. Scenarios are the “missions” to be

completed within the ERP-system. The pace is

controlled by the students themselves. The

completion of a mission is rewarded with a badge in

Moodle. There is no best list, but how many other

students have achieved this badge, is visible. Badges

can have a positive effect on user activities (Hamari,

2017). Badges show the achievements of the

participating students and trigger competitive

behavior.

Each scenario is a mission. The repair of a bicycle

previously delivered to the customer, is one process

instance students have to deal with. The repair of this

bicycle involves the replacement of a defective

gearshift, one of the components according to the

BOM of this bike. If the activities were carried out

correctly within the ERP-system, this gearshift

should not be available in the warehouse: students

need to start another instance of the procurement

process, before the repair of the bicycle can be carried

out and the service notification closed. As a service to

the customer, costs are settled via an internal cost

center.

Mistakes can happen at any point in the scenarios,

but are most frequent in master data management.

Master data management and the importance of data

quality are important aspects of the scenarios. In

preparation for real-life-environments, flaws in data

quality should have an impact. Preventing students’

mistakes would be counterproductive to the learning

outcome. They were allowed to happen and if they do,

they have an impact on the process.

Three aspects are important for effective “learning

from mistakes” (Metcalfe, 2017): mistakes were

made by the student learners themselves, they receive

corrective feedback and this corrective feedback

leads to a correct answer, a correction of the mistake

or problem solving. Corrective feedback should

remind learners of the context in which the mistake

was made (Metcalfe, 2017) and to assist in

uncovering the cause, fixing it and reflecting on it.

Reflection and the search for explanations improve

the understanding. Problem-solving paths become

visible, which can empower learners to correct errors

themselves. As mistakes can be taken emotionally

(Kartika, 2018), corrective feedback needs to be

careful and constructive.

The flickering blue light links to a trouble-

shooting file with typical error messages and

explanations of their causes (corrective feedback).

This empowers the student learners to cope with

mistakes without assistance. The sense of control thus

Gamiﬁed Hands-on-Training in Business Information Systems: An Educational Design Experiment

167

remains, even when mistakes occur. As a second

escalation level, students are assisted by tutors who

provide corrective feedback.

4 QUANTITATIVE ANALYSIS

The participants of the hands-on-training on

integrated business processes in the SAP ERP-

System were invited to participate in an online

survey. There were no incentives, the survey was

anonymous, participation voluntary. The

questionnaire followed the research model, using a

five-point Likert scale to measure the items and some

free-text fields for students’ comments.

231 students passed the course in the autumn

semester 2020/21, 72 (31%) filled out the

questionnaire. 25 data sets were incomplete and

dismissed from further analysis. Even though with 47

remaining data sets, the sample size is poor (Comrey

and Lee, 2016), the data sets covered 20% of the

participants and their analysis was expected to

provide valuable insights.

SmartPLS version 3.3.3 (Ringle et al., 2015) was

used to analyse the data. SmartPLS is a software for

SEM-PLS (Henseler, 2017) frequently used in

literature on information systems (e.g. in

Kijsanayotin et al. 2009, Celik 2016, Gunawan 2018,

Raza et al. 2021). The partial least square structural

equation modeling (PLS-SEM) was performed in two

stages: Stage one involved the evaluation of the

measurement model (reliability and validity of

constructs). The second stage involved the evaluation

of the structural model (inner loadings). Within the

first stage the validity of the items was examined

(Table 1).

Table 1: Mean and Standard deviation (SD).

Item Mean SD

LE1: I feel like I have learned more about

the ERP system.

3.6 1.23

LE2: My understanding of business

processes has improved.

3.51 1.16

LE3: I learn about business application

systems more effectively than through

lectures.

3.7 1.08

M1: Mistakes helped me to understand the

contexts better.

3.49 1.28

M2: Learning from mistakes can promote a

positive attitude in the process.

3.66 1.27

SL1: I like being able to determine the

learning speed by myself.

3.94 1.45

SL2: I like being able to learn the material at

any time.

4.11 1.51

SL3: I like being able to learn anywhere. 3.96 1.47

SL4: Time passes quickly in this

environment.

3.87 1.17

SL5: Noise doesn't distract me when I'm

studying.

3.47 1.47

SL6: It makes me feel good. 3.72 1.04

Cronbach’s Alpha was calculated to assess the

scale’s reliability (Tabachnick and Fidell 2014), those

underneath 0.55 were dismissed from further

analysis. After validity of the items was confirmed,

reliability of the constructs was assessed.

Table 2: Reliability (CR = Composite Reliability, AVE =

Average Variance Extracted).

Cronbach's Alpha CR AVE

LE 0.880 0.879 0.709

M 0.816 0.821 0.697

SL 0.942 0.941 0.728

Composite Reliability (CR) is higher than 0.7 for

all constructs (Table 2). Cronbach’s Alpha was

calculated to assess the scale’s reliability (Tabachnick

and Fidell, 2014). The values are above 0.8 for all

constructs, which is very good (Streiner, 2003).

Average Variance Extracted (AVE) is greater than

0.5 for all constructs, thus satisfying the nominal

value given by Fornell and Larcker (1981). With

these three criteria fulfilled for all constructs,

reliability was confirmed.

Table 3: Fornell-Larcker-Criterion.

LE M SL

LE 0.842

M 0.603 0.835

SL 0.768 0.780 0.853

Discriminant validity has been measured (Table

3). The square root of AVE is higher than the

correlation of these constructs, thus meeting the

criterion of Fornell and Larcker (1981). Cross

loadings of the items on their relevant construct

(Table 4) are higher than on the other constructs.

Table 4: Cross-Loadings.

LE M SL

LE1 0.760 0.352 0.593

LE2 0.864 0.551 0.662

LE3 0.895 0.601 0.683

M1 0.465 0.771 0.530

M2 0.539 0.894 0.760

SL1 0.530 0.540 0.689

SL2 0.716 0.761 0.932

SL3 0.717 0.661 0.934

SL4 0.631 0.579 0.821

SL5 0.625 0.769 0.814

SL6 0.694 0.675 0.903

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Within the structural model analysis, inner

loadings were calculated. The PLS bootstrap was

used to test the significance of item loadings. Whilst

the first hypothesis (SL-> LE) was significant, the

second hypothesis (M-> LE) had to be dismissed

(Figure 2).

Figure 2: Path Coefficients (t-statistics for inner and outer

loadings; LE = Learning Efficiency, SL = Self-paced

Learning, M = Learning from Mistakes).

5 DISCUSSION

In recent literature no clear indication could be found

on whether the effectiveness of corrective feedback is

higher when it is immediate or delayed. But literature

suggests that long delays can have a negative impact

on the learning process and can overwhelm learners

who need support (Mathan and Koedinger, 2005).

This can explain the lower loading of “learning from

mistakes” on learning efficiency that was investigated

in H2. As the frustration level varies from student to

student, the perception of mistakes as beneficial for

the learning process and the learning outcomes varies

as well. One student commented within the

questionnaire on frustration resulting from mistakes:

“The work was sometimes really fun, but when errors

appeared and it took time until they could be fixed or

succeeding errors occurred, it was sometimes really

annoying. That also spoiled the fun a bit.”

(Participant 9). Another student perceived this as less

problematic: “In general, the videos, data sheets, etc.

make working with SAP very easy. However, it

becomes frustrating when you receive error messages

that do not appear in the videos and often leave you

sitting in front of the computer at a loss. With a little

help from the tutor, however, this is also feasible and

is therefore not a big problem.” (Participant 28).

Another student expressed a preference to increase

the self-control and to even intensify the possibility to

make mistakes within the scenarios: “In my opinion,

watching the videos made it a little too easy to work

on the project. At one point or another, I would have

liked to take more control to manoeuvre through the

process in the system, so that I could make my own

mistakes and learn from them.” (Participant 45).

The positive correlation of self-paced learning

with learning efficiency which was assumed in H1 is

in line with previous literature (Marshman et al.,

2020; Bautista, 2015) and received further

confirmation by the comments given within the

questionnaire (Table 5).

Table 5: Some sample comments on learning efficiency of

the participants.

“It helped me to understand what an ERP system is and most

importantly, it brought practice to my studies, making them

easier to understand.”

“You learn parallel to the normal lecture (Business

Information Systems), how certain processes run in the system,

[…] which was a lot of fun for me and helped a lot with

learning!”

“Working within the environment was a lot of fun! […] I would

have liked a slightly higher level of difficulty and a little more

control. I learned a lot from this project and would always

recommend and wish for more of this kind of learning in my

tudies.”

“Very interesting and good way to design a course. The

learning material is practically applied in a corresponding

software environment. There was no dull memorization […] but

rather the material was "understood". Interactive or practical

design of lectures should be used more often.”

Among the comments given, one student asked

for more tasks, to improve understanding, e.g.,

another sales order requiring manufacturing of parts.

There were some sound issues in the videos that

required improvement and one student asked for more

colour, and the possibility to visualize the progress in

the roadmap (Participant 41). As suggestions for

improvement are indicators for a positive attitude

towards the learning environment, all of them were

written down, to improve the environment for the

semesters to come.

6 CONCLUSIONS

This paper contributes to the body of knowledge by

providing an educational design experiment within an

academic context on the complex topic of business

processes and their support through ERP-systems. It

could be verified that a hands-on-training within a

SAP ERP-System could be taught via an e-learning

SL2

SL3

SL4

SL5

SL6

LE1

LE2

LE3

SL1

0.313

3.665

12.640

68.171

12.847

53.360

17.008

15.769

26.678

25.587

13.411

21.093

25.587

Gamiﬁed Hands-on-Training in Business Information Systems: An Educational Design Experiment

169

environment without making compromises regarding

scope or scale of the content. The student feedback

gives encouragement to continue on this path.

Iterative development will improve functionality,

sound quality and user interaction.

Within this educational design experiment the

task of providing a self-controlled e-learning

environment to gain an in-depth understanding in

distance learning environments has been addressed.

The design was gamified, the layout showed a self-

explanatory roadmap that students could follow at

their own pace, completing missions and being

rewarded with badges as they proceeded. Making

mistakes was possible within all scenarios. If they

occurred, they had an impact on the processes.

Reflection was necessary to understand the causes

and to identify ways for their correction. “Learning

from mistakes” was supported via corrective

feedback.

Within this design experiment, students could

give feedback via an anonymous online survey. SEM-

PLS was used for data analysis. A positive correlation

of self-paced learning with learning efficiency was

confirmed, while a positive correlation of learning

from mistakes with learning efficiency was not

supported. As the size of the data set is rather small,

further research is necessary. Within e-learning

environments, literature on learning from mistakes is

spare. Future studies could elaborate on this. As only

students taking the course qualify for participation in

the survey, options for broadening the survey are

limited. Further design research could broaden and

confirm the results.

The invitation to the survey was linked into the

learning environment at the very end. As only

students who successfully completed the course came

so far, the results might be biased. But the positive

attitude of those students gave encouragement to

continue on the path in the aftermath of the pandemic:

“It was a little hard for me to get into at first, but

now I don't want to get out. It's a pity it's over, it

was really fun.” (Participant 6)

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