The Repercussions of Business Process Modeling Notations on Mental Load and Mental Effort [original]

The Repercussions of Business Process Modeling

Notations on Mental Load and Mental Effort

Michael Zimoch1, R¨udiger Pryss1, Thomas Probst2, Winfried Schlee3, and

Manfred Reichert1

1Institute of Databases and Information Systems, Ulm University, Germany

2Dep for Psychotherapy and Biopsycho Health, Danube University Krems, Austria

3Dep of Psychiatry and Psychotherapy, Regensburg University, Germany

{michael.zimoch, ruediger.pryss, manfred.reichert}@uni-ulm.de,

[email protected], winfried.schlee@googlemail.com

Abstract. Over the last decade, plenty business process modeling nota-

tions emerged for the documentation of business processes in enterprises.

During the learning of a modeling notation, an individual is confronted

with a cognitive load that has an impact on the comprehension of a

notation with its underlying formalisms and concepts. To address the

cognitive load, this paper presents the results from an exploratory study,

in which a sample of 94 participants, divided into novices, intermedi-

ates, and experts, needed to assess process models expressed in terms

of eight different process modeling notations, i.e., BPMN 2.0, Declara-

tive Process Modeling, eGantt Charts, EPCs, Flow Charts, IDEF3, Petri

Nets, and UML Activity Diagrams. The study focus was set on the sub-

jective comprehensibility and accessibility of process models reflecting

participant’s cognitive load (i.e., mental load and mental effort). Based

on the cognitive load, a factor reflecting the mental difficulty for com-

prehending process models in different modeling notations was derived.

The results indicate that established modeling notations from industry

(e.g., BPMN) should be the first choice for enterprises when striving for

process management. Moreover, study insights may be used to deter-

mine which modeling notations should be taught for an introduction in

process modeling or which notation is useful to teach and train process

modelers or analysts.

Keywords: Business Process Modeling Notations, Cognitive Load, Men-

tal Load, Mental Effort, Human-centered Design

1 Introduction

Business process models specify in terms of textual or graphical artifacts the

business processes in an enterprise [1]. In this context, insights on the compre-

hension of process models demonstrate that process model comprehension plays

an important role when analyzing and optimizing processes [2, 3]. As a result,

enterprises are confronted with an influx of process modeling notations (e.g.,

Business Process Model and Notation (BPMN) 2.0 [4], Event-driven Process

2 Zimoch et al.

Chains (EPCs) [5], or Flow Charts [6]) for the documentation of their business

processes within process models. However, for an effective use of process models,

the latter must ensure that the processes of an enterprise are comprehended

correctly by all involved stakeholders.

In prior research, we investigated process model comprehension in order to re-

veal factors fostering or thwarting respective comprehension [7, 8]. Furthermore,

focusing on cognitive neuroscience and psychology, we proposed valuable lessons

learned on how to optimize empirical studies for a deeper investigation on pro-

cess model comprehension [9].

To enhance our previous work on process model comprehension, this work pre-

sents the results obtained from an exploratory process model comprehension

study. In detail, a sample consisting of n= 38 novices, n= 21 intermediates,

and n= 35 experts in the domain of process modeling are confronted with pro-

cess models expressed in terms of eight different process modeling notations. The

objective of the study was to evaluate the perceived cognitive load (i.e., effort

being used in the working memory) of participants caused when comprehending

respective modeling notations. Based on the results we obtained, we derived for

each process modeling notation amental difficulty level.

This work contributes to the field of process model comprehension in two ways.

First, in research, we want to learn more about the cognitive load and adverse

effects when comprehending process models in terms of different modeling no-

tations [10]. The obtained insights can foster related empirical investigations in

this context. Second, in practice, enterprises can be supported in making decision

about the adoption of a particular process modeling notation or which modeling

tool should be used when adopting process-oriented thinking.

The remainder of the paper is structured as follows: Section 2 introduces theo-

retical backgrounds. Study setting and operation are explained in Section 3. In

Section 4, the obtained results are described empirically and discussed. Finally,

Section 5 discusses related work, while Section 6 summarizes the paper and gives

an outlook on future work.

2 Theoretical Background

The cognitive load can be defined as a multidimensional construct representing

an individuals cognitive capacity used to work on or to solve a task as well as

to address a problem [11]. Thereby, cognitive load has a causal dimension re-

flecting the interaction between task- (e.g., inherent difficulty of the task) and

subject-specific characteristics (e.g., knowledge about a topic). Particularly, cog-

nitive load is comprised of the assessment dimensions describing the measurable

aspects mental load and mental effort [12, 13]. The mental load relates to a task,

which indicates the cognitive capacity needed to cope with the complexity of a

task. Juxtaposing mental load, the mental effort is subject-specific and refers to

the invested cognitive capacity of an individual while working on a task [14].

In many fields (e.g., psychology, education), the observation as well as measure-

ment of the cognitive load has become crucial. Reasons for this are that a reduced

Repercussions of Modeling Notations on Mental Load and Effort 3

cognitive load has a positive impact on the working memory, thus promoting in-

formation processing and assuring a greater success in learning processes [15].

In turn, a high cognitive load (i.e., overloading the working memory) inhibits

information processing leading to confusion and a higher risk of making mista-

kes. As a consequence, cognitive load should be kept at an appropriate level [16].

Therefore, an appropriate level of cognitive load can be ensured from the ideal

interplay of mental load and mental effort. Particularly, by designing tasks and

presenting information in such way that an individual is not confronted with

challenges, demanding more capacity in the working memory [17].

3 Study Setting

Any process modeling notation has its own strengths and weaknesses regarding,

for example, model conformance checking or expressibility [23]. Considering this

fact, enterprises are confronted with the important decision about which process

modeling notation fulfills their requirements and covers all their needs. Some

of the process modeling notations are offering an extensive syntax to express

business processes in a fine-grained level. On the other, some notations provide

only a limited set, which is, however, sufficient for correctness verification of

process models [24]. For an effective use of process modeling notations, their

formalisms and methodologies must be comprehended correctly. Thereby, the

acquisition of knowledge about a process modeling notation represents a cognitive

task. In this context, several aspects of a modeling notation are learned more

easily and quickly, while, on the other, some aspects are difficult to learn, having

different impact on the cognitive load of an individual. However, this effect cannot

be generalized and, hence, is completely different between individuals. Especially

Notation Brief Description Spec.

BPMN 2.0 [4] BPMN is a graphical notation for documenting business processes

based on flowcharting techniques. Nowadays, BPMN is an establis-

hed standard for process modeling.

Declarative [18] Declarative Process Modeling is an approach specifying in an impli-

cit manner through the use of constrains the order of execution for

a process model.

eGantt [19] eGantt Chart is an extension of the Gantt Chart with an emphasis

to capture time characteristics of a process in a process model.

EPC [5] EPC is a flow chart type mainly used for the implementation and

configuration of enterprise resource planning.

Flow [6] Flow Chart is a diagram for the graphical modeling of processes.

Flow Charts are widely used and enable the creation of easy-to-

understand process models.

IDEF3 [20] IDEF3 is a method used for modeling of processes with a focus

on the process flow as well as the state of objects and respective

conditions.

Petri Net [21] Petri Net is a notation mainly used for the description of distributed

systems and the only notation with an exact mathematical theory.

UML Act. [22] UML Activity Diagram documents activities as well as the control

flow in a flowchart manner and are often used for process modeling.

Table 1: Process Modeling Notations used in the Study

4 Zimoch et al.

novices without any knowledge in process modeling are often confronted with

difficulties how to properly comprehend process modeling notations. To address

this issue, we conduct an exploratory comprehension study in which novices,

intermediates, and experts from the domain Thereby, we want to investigate

the impact of modeling notations on the cognitive load (i.e., mental load and

mental effort) of individuals. In detail, we agree on using the following notations

as described in Table 1. Aside well-known modeling notations (e.g., BPMN ),

we chose notations that are rarely seen in the process repositories of enterprises

(e.g., IDEF3). Table 1 contains an additional column specific (i.e., Spec.), stating

whether the use of a modeling notation is more focused on particular aspects.

3.1 Study Planning

Participants. All participants have an academic background. In detail, students

and research associates as well as professionals, who take a distance e-learning

course, are invited for the study at Ulm University. There are no prerequisites

for participating in the study and all participants are recruited on a voluntary

basis. Further, all participants have given their consent.

Object. Participants need to assess eight different process models (cf. Table 1)

regarding their subjective comprehensibility and accessibility. With this asses-

sment, we want to draw conclusions about the perceived mental load and mental

effort of the participants. Thereby, the process models reflect three different le-

vels of complexity (i.e., easy,medium, and hard). To be more precise, the easy

process model contains only basic modeling elements. With rising level of com-

plexity, the total number of elements is increased and new elements, previously

not contained in the process model, are added. For each level of complexity, pro-

cess models are created using the mentioned eight process modeling notations

respectively (cf. Table 1). As the semantic description of the process models is

not relevant in this context, we use abstract labels (i.e., alphabetic letters) for

the single modeling elements.1Furthermore, it is ensured that all process mo-

dels are comparable within a specific level of complexity. Therefore, experts and

novices in the domain of process modeling, who were not participating in the

study, ranked and compared the used process models.

Instrumentation. There are three different questionnaires in the study. First,

apre-study questionnaire is used gathering demographic data (e.g., age, gender)

and asking about prior knowledge on process modeling notations. In addition,

participants were asked about their familiarity with specific modeling notations.

Second, a mid-study questionnaire is used providing four items regarding the

mental load (i.e., comprehensibility and accessibility of the process models): the

process model is comprehensible, the 2

process model is accessible, the 3



used modeling constructs (e.g., split and join) are comprehensible, and the 4



the process flow is understood properly. All items are rated on a 7-point Likert

scale, ranging from 0 (i.e., strongly disagree) to 6 (i.e., strongly agree).

Third, a post-study questionnaire is used with four items capturing the mental

1Material: https://www.dropbox.com/sh/4shyxdy2p4xsf71/AAAfY1EMfwrluYA2BQ-g4z18a?dl=0

Repercussions of Modeling Notations on Mental Load and Effort 5

effort of participants: the 1

mental demand for performing the task is high,

the 2

task is complex, the 3

overall performance during the task, and the 4



effort level for performing the task. All items are rated on a 7-point Likert scale,

ranging from 0 (i.e., strongly disagree) to 6 (i.e., strongly agree).

In addition, in the post-questionnaire participants need to categorize the assessed

process models according their subjective preferences regarding the comprehen-

sibility as well as accessibility, beginning from the simplest to the most difficult

process model. Finally, participants are able to leave qualitative feedback.

3.2 Study Design and Procedure

The design and procedure of the study is based on the guidelines set out by

[25] on how to systematically plan, conduct, and evaluate studies. Precedent, a

pilot study with four students and four research associates was conducted for

the improvement of the process models and to ensure their comparability with

each other. Moreover, the pilot study was used to eliminate potential ambigui-

ties as well as misunderstandings. Further, the chosen language in the study is

German. Within a period of two weeks, several sessions for conducting the study

are offered to the participants. Each session took about 20 minutes and ran as

follows: An introduction is given to the participants, in which the procedure of

the study is explained and the study materials (i.e., process models, questionnai-

res) are handed out. Afterwards, the participants need to answer the pre-study

questionnaire. Following this, they are asked to read and assess eight randomly

selected process models. Thereby, it is ensured that participants need to assess

each modeling notation in a study run. For each assessed process model, the

participants have to answer statements regarding subjective comprehensibility

and accessibility (i.e., mental load). After completing this step, participants ans-

wer the post-study questionnaire, providing information about perceived mental

effort and they need to categorize assessed process models in a ranking system.

The study procedure is illustrated in Figure 1.

Introduction Pre-Study

Questionnaire

Exploratory Study

Mid-Study

Questionnaire

Process Model

Mid-Study

Questionnaire

Post-Study

Questionnaire Feedback

Capture demographic

information

Explain the overall

procedure of the study Gather mental effort

Categorize process

models regarding

comprehensibility and

accessibility

Measure mental load

Fig. 1: Study Design

6 Zimoch et al.

4 Data Analysis and Interpretation

In total, data from 94 participants were collected. A median split is performed to

categorize the participants into samples of novices, intermediates, and experts.

Therefore, we determine the median for the number of process models a partici-

pant has analyzed and created during the last 12 months. Consequently, n= 38

novices, n= 21 intermediates, and n= 35 experts participate in the study. Each

participant assesses eight randomly selected process models regarding subjective

comprehensibility and accessibility (i.e., mental load), resulting in n= 752 asses-

sed process models. Table 2 summarizes the detailed distribution of the assessed

process modeling notations for each level of complexity.

Process Modeling Notations

BPMN Declarative eGantt EPC Flow IDEF3 Petri UML Act.

Level

Easy 30 31 33 31 36 28 34 35

Medium 29 32 33 36 28 35 33 31

Hard 36 31 28 27 30 31 27 27

Table 2: Number of Assessed Process Models

4.1 Descriptive Statistics

The obtained data regarding the perceived mental load for each process modeling

notations are presented in Table 3 as means for the entire sample size as well as

each sample respectively (i.e., novices, intermediates, and experts). Higher values

indicate less mental load. As described in Section 3.1, mental load is determined

with four aggregated items. As a prerequisite, all response variables must show

a high reliability [26]. For this purpose, Cronbach’s α(i.e., several items are an

accurate estimate of an accumulated item) is calculated.2For mental load, a

Cronbach with α= 0.79 was calculated.

BPMN Declarative eGantt EPC Flow IDEF3 Petri UML Act.

All

Easy 5.77 4.70 5.34 5.60 5.61 4.62 5.66 5.54

Medium 4.61 3.61 3.70 5.13 4.86 4.34 4.51 4.68

Hard 3.60 3.27 3.01 4.99 3.99 3.64 3.90 4.46

Nov.

Easy 5.76 5.12 5.03 5.18 5.43 4.47 5.87 5.60

Medium 4.47 3.45 3.28 4.54 5.14 3.67 4.83 4.69

Hard 3.22 3.20 2.98 4.63 2.72 2.42 4.10 4.35

Int.

Easy 5.84 4.21 5.43 5.78 5.63 4.88 5.33 5.70

Medium 4.66 3.66 4.13 5.32 5.17 4.78 4.88 4.50

Hard 4.53 3.47 3.45 5.55 5.29 4.80 3.95 4.92

Exp.

Easy 5.70 4.77 5.56 5.84 5.78 4.50 5.79 5.33

Medium 4.71 3.71 3.68 5.54 4.26 4.56 3.81 4.84

Hard 3.05 3.00 2.58 4.79 3.97 3.69 3.66 4.11

Table 3: Mental Load

Figures 2 - 4 depict the two-dimensional data (i.e., process modeling notati-

ons,level of complexity) regarding the mental load for the entire sample and each

sample respectively (i.e., novices, intermediates, and experts) as radar charts.

Thereby, higher values stand for less mental load on the used scale.

2According to [26], α > 0.6 acceptable reliability; 0.7 < α < 0.9 good reliability

Repercussions of Modeling Notations on Mental Load and Effort 7

0.00

1.00

2.00

3.00

4.00

5.00

6.00

BPMN

Declararative

eGantts

EPCs

Flow Charts

IDEF3

Petri Nets

UML Activity

Easy Medium Hard

Fig. 2: Mental Load - All

0.00

1.00

2.00

3.00

4.00

5.00

6.00

BPMN

Declararative

eGantts

EPCs

Flow Charts

IDEF3

Petri Nets

UML Activity

Easy Medium Hard

Fig. 3: Mental Load - Novices

0.00

1.00

2.00

3.00

4.00

5.00

6.00

BPMN

Declararative

eGantts

EPCs

Flow Charts

IDEF3

Petri Nets

UML Activity

Easy Medium Hard

Fig. 4: Mental Load - Intermediates

0.00

1.00

2.00

3.00

4.00

5.00

6.00

BPMN

Declararative

eGantts

EPCs

Flow Charts

IDEF3

Petri Nets

UML Activity

Easy Medium Hard

Fig. 5: Mental Load - Experts

Regarding the easy level of complexity (cf. Figures 2 - 4), several process

modeling notations (e.g., BPMN,Petri Nets, and UML Activity Diagrams) are

close to the maximum of the scale (i.e., 6 - reflecting less mental effort). Ho-

wever, comparing the values obtained from each sample, there is an indication

that easy process models expressed in Declarative or IDEF3 appear to be more

challenging to comprehend and, consequently, demand a higher mental load from

the participants. In general, with rising level of complexity, an erratic decrease

in the mental load is observable (cf. Table 2). In detail, instead of an uniform

decrease of the values for the mental load, for several modeling notations, an ab-

rupt decline can be seen in the samples. For example, there is a significant drop

in the mental load between the easy and medium BPMN process models in each

sample. For novices and intermediates, there is only a little decline in the mental

load between the medium and hard BPMN model. However, experts show again

a significant drop in the mental load for BPMN process models. Another example

8 Zimoch et al.

concerns the process models expressed in terms of EPCs. There are only slight

decreases in the mental load, which indicates that the comprehension of EPC

process models appears to be feasible throughout the levels of complexity.

Table 4 shows the mental effort (ME) for the entire sample as well as each

sample respectively (i.e., novices, intermediates, and experts). Mental effort is

determined by four aggravated items (cf. Section 3.1), here, Cronbach resulted

in α= 0.83. Higher values stand for less mental effort. The study task demands

a moderate mental effort from all samples. Further, there are only minimal dif-

ferences in the mental effort between novices, intermediates, and experts.

All Novices Inter. Experts

ME 2.84 2.76 2.89 2.94

Table 4: Mental Effort

4.2 Discussion

It is obvious that with rising level of complexity the mental load regarding the

process modeling notations is decreasing (cf. Figure 2). Despite the compara-

bility of the process models, however, it is interesting to see how the mental

load is decreasing in a different manner between novices, intermediates, and ex-

perts. For example, the mental load from intermediates regarding the medium

and hard process model is about the same. In turn, for novices and experts, the

differences between these two levels of complexity are clearly discernible. The

same can be observed regarding the IDEF3 process models. Further, the results

provide a good indication about the mental load when comprehending process

models in particular modeling notations. More complex process models expressed

in Declarative, IDEF3, or eGantt appear to be more challenging to comprehend.

Reason might be that these notations are not as widespread in practice as other

notations. Often, amongst others, tertiary educational institutions are teaching

more common notations widely used in practice such as BPMN and EPCs.

While there are different characteristics for the mental load, however, the men-

tal effort needed for process model comprehension is approximately the same

between novices, intermediates, and experts. Although the mental effort for

comprehending a process model is on a moderate level, however, process mo-

del comprehension is a complex matter that needs to be taken into account.

Based on the study results, Figure 6 presents the derived mental difficulty for

each process modeling notation and respective level of complexity. Therefore, for

each process model, the percentage proportion is calculated based on the cate-

gorization the participants indicated in the post-study questionnaire (cf. Section

3.1). Therefore, we considered respective position of each process model as well

as their level of complexity. The calculated percentage proportion serves as a

factor used for multiplying the aggravated constructs mental load and mental

effort to derive the mental difficulty [27].

As shown in Figure 6, regarding an easy level of complexity, almost all values,

except for Declarative and IDEF3, are still in the green range. This means that

Repercussions of Modeling Notations on Mental Load and Effort 9

BPMN

Declarative

eGantt

EPC

Flow

IDEF3

Petri

UML Act.

Easy Medium Hard

Process Modeling Notations

Mental Difficulty

Fig. 6: Mental Difficulty

the comprehension of such process models should not be a challenge. With rising

level of complexity, the values for the comprehension of process models tend

towards the orange or red range respectively. However, none of the values is

located completely in the red range and, consequently, it seems to be that process

models can be comprehended independently of the modeling notation and level

of complexity. In addition, the results confirm the further use of the widespread

and established modeling notations such as BPMN and EPCs.

4.3 Threats to Validity

Study results can only be accurately interpreted when limitations are also dis-

cussed. First, the comparison of the different process modeling notations can also

be seen as a comparison like the idiom apples and oranges. Particular modeling

notations are usually not used for the direct modeling of business processes, but

for other purposes (e.g., Petri Nets are mainly used for the verification of process

models) and, hence, a direct comparison is difficult. Second, the classification of

participants into the samples of novices, intermediates, and experts solely based

on a median split by considering the number of process models a participant has

analyzed and created during the last 12 months may be oversimplified. Hence,

results might differ significantly with experts in process modeling with several

years of experience. Third, the size of the process models might not be repre-

sentative. In general, real world process models are usually much larger than

the models we used in the study. Fourth, in addition, to ensure comparability

between the process models, the latter are modeled only with basic modeling

elements because some modeling notations don’t have the expressiveness for the

documentation of complex processes. Fifth, the respective level of complexity

reflected by the process models constitutes another threat. The models might

be considerably unbalanced between the level of complexity and, hence, working

10 Zimoch et al.

memory capacity of participants, especially for novices, may be exceeded. Se-

venth, specific process modeling notations (e.g., BPMN ) may be considered as

easy to comprehend since these notations are more familiar than others (e.g.,

IDEF3), thus resulting in a positive impact on mental load and mental effort.

5 Related Work

A review of existing business process modeling notations in literature is presented

in [23]. Further, the authors present a framework for the classification of mo-

deling notations according their purpose. [28] discusses an evaluation of modeling

notations and proposes a meta-model to capture the concepts of the evaluated

notations. In turn, [29] compares and discusses different process modeling met-

hods based on aspects like process model representation and tool support.

Concerning research on process model comprehension, [30] gives insights into

subject-specific characteristics (e.g., theoretical knowledge) influencing process

model comprehension. In turn, [31] evaluates different process modeling notati-

ons with respect to their comprehensibility. A discussion about factors having

an influence on the comprehension of process models is presented in [32].

Regarding cognitive aspects, a measure to determine the cognitive complexity for

process models is proposed in [33]. Further, [34] shows an approach to reduce the

cognitive load when comprehending process models by applying patterns for im-

proving the model comprehensibility. The empirical assessment of participants’

mental effort, while creating or comprehending process models, is demonstrated

in [35]. The mental effort needed between inexperienced and experienced mo-

delers to create process models is investigated in [36].

Altogether, there are several works regarding the comparison of process modeling

notations. However, to the best of our knowledge, none of the discussed works

deal with such a comparison of process modeling notations, while taking the

cognitive load (i.e., mental load and mental effort) of participants into account.

6 Summary and Outlook

This paper presented the impact of different process modeling notations on the

cognitive load (i.e., mental load and mental effort) of n= 38 novices, n= 21

intermediates, and n= 35 experts in the domain of process modeling. Therefore,

study participants needed to comprehend and assess process models of different

levels of complexity (i.e., easy,medium,hard) in terms of eight different mo-

deling notations, i.e., BPMN 2.0,Declarative Process Modeling,eGantt Charts,

EPCs,Flow Charts,IDEF3,Petri Nets, and UML Activity Diagrams. We gat-

hered information related to the mental effort and mental load (i.e., cognitive

load) of participants while performing the study task. Based on the cognitive

load, we derived a factor representing the mental difficulty for each process mo-

deling notation. The high availability of process modeling notations resulted in

a strong demand from enterprises to compare and evaluate these notations in

Repercussions of Modeling Notations on Mental Load and Effort 11

order to find an appropriate one covering the needs of an enterprise. The presen-

ted mental difficulty may support enterprises in making this decision considering

the perceived cognitive load of an individual, while comprehending process mo-

dels expressed in terms of different modeling notations. In addition, the results

from this paper may help in answering questions about which modeling nota-

tions should be supported with a greater emphasis in modeling tools. Further,

the results show that the use of established modeling notations (e.g., BPMN ) is

recommendable for enterprises and future process modelers as well as analysts.

Future research is needed in order to determine more precise indications about

an individuals cognitive load while comprehending process models. Besides an

in-depth statistical analysis of the results, for example, instead of using abstract

labels the process model elements could be described with concrete labels. Furt-

her, participants should be confronted with process models from real projects

expressed in terms of the assessed modeling notations. Finally, cognitive load is

only one factor having an effect on the comprehension of process models expres-

sed in different modeling notations and, therefore, the consideration of additional

factors (e.g., expressiveness,level of automation) will be subject of future studies.

References

1. Nowak, A., et al.: Flexible Information Design for Business Process Visualizations.

In: 5th Int’l Conf on Service-Oriented Comp and App (SOCA’12). (2012) 1–8

2. Bandara, W., Indulska, M., Chong, S., Sadiq, S.: Major Issues in Business Process

Management: An Expert Perspective. (2007) 1240–1251

3. Gulla, J.A., Brasethvik, T.: On the Challenges of Business Modeling in Large-Scale

Reengineering Projects. In: Int’l Conf on Requirements Engineering. (2000) 17–26

4. OMG: Business Process Management & Notation 2.0 (2018) www.bpmn.org, last

visited on 2018/02/27.

5. van der Aalst, W.M.P.: Formalization and Verification of Event-driven Process

Chains. In: Inf & Soft Tech. (1999) 639–650

6. Schultheiss, L.A., Heiliger, E.: Techniques of Flow-Charting. In: Proc 1963 Clinic

on Library Applications of Data Processeing. (1963) 62–78

7. Zimoch, M., et al.: Cognitive Insights into Business Process Model Comprehen-

sion: Preliminary Results for Experienced and Inexperienced Individuals. In: Proc

BPMDS’17. (2017) 137–152

8. Zimoch, M., et al.: Using Insights from Cognitive Neuroscience to Investigate the

Effects of Event-Driven Process Chains on Process Model Comprehension. In: Proc

1st Int’l Conf on Cognitive Business Process Management. (2017) 446–459

9. Zimoch, M., et al.: Eye Tracking Experiments on Process Model Comprehension:

Lessons Learned. In: Proc BPMDS’17. (2017) 153–168

10. Zugal, S., Pinggera, J., Weber, B.: Assessing Process Models with Cognitive Psy-

chology. In: EMISA. Volume 190. (2011) 177–182

11. Paas, F., et al.: Cognitive Load Measurement as a Means to Advance Cognitive

Load Theory. In: Educational Psychologist. Volume 38. (2003) 63–71

12. Wickens, C.D.: Multiple Resources and Mental Workload. In: Human Factors.

Volume 50. (2008) 449–455

13. Paas, F.G., Van Merri¨enboer, J.J.: The Efficiency of Instructional Conditions:

An Approach to Combine Mental Effort and Performance Measures. In: Human

Factors. Volume 35. (1993) 737–743

12 Zimoch et al.

14. Krell, M.: Evaluating an Instrument to Measure Mental Load and Mental Ef-

fort Considering Different Sources of Validity Evidence. In: Cogent Education.

Volume 4. (2017)

15. Sweller, J.: Cognitive Load During Problem Solving: Effects on Learning. In:

Cognitive Science. Volume 12. (1988) 257–285

16. Ayres, P.: Impact of Reducing Intrinsic Cognitive Load on Learning in a Mathe-

matical Domain. In: Applied Cognitive Psychology. Volume 20. (2006) 287–298

17. Baddeley, A.: Working Memory. In: Science. Volume 255. (1992) 556–559

18. van Der Aalst, W.M., et al.: Declarative Workflows: Balancing Between Flexibility

and Support. In: Comp Sci-Res and Dev. Volume 23. (2009) 99–113

19. Lanz, A., Kolb, J., Reichert, M.: Enabling Personalized Process Schedules with

Time-Aware Process Views. In: International Conference on Advanced Information

Systems Engineering. (2013) 205–216

20. Kim, C.H., Yim, D.S., Weston, R.: An Integrated Use of IDEFO, IDEF3 and Petri

Net Methods in Support of Business Process Modelling. In: Proc Instof Mech Eng,

Part E: J of Process Mech Eng. (2001) 317–329

21. Murata, T.: Petri Nets: Properties, Analysis and Applications. In: Proceedings of

the IEEE. Volume 77. (1989) 541–580

22. Dumas, M., Ter Hofstede, A.H.: UML Activity Diagrams as a Workflow Specifi-

cation Language. In: Int’l Conf on the UML. (2001) 76–90

23. Aguilar-Saven, R.S.: Business Process Modelling: Review and Framework. In:

International Journal of Production Economics. Volume 90. (2004) 129–149

24. White, S.A.: Process Modeling Notations and Workflow Patterns. In: Workflow

Handbook. (2004) 265–294

25. Wohlin, C., Runeson, P., H¨ost, M., Ohlsson, M.C., Regnell, B., Wesslen, A.: Ex-

perimentation in Software Engineering - An Introduction. Kluwer (2000)

26. Kline, P.: Handbook of Psychological Testing. Volume 2. Routledge (1999)

27. Sweller, J.: Cognitive Load Theory, Learning Difficulty, and Instructional Design.

In: Learning and Instruction. Volume 4. (1994) 295–312

28. List, B., Korherr, B.: An Evaluation of Conceptual Business Process Modelling

Languages. In: Proc Symp on Applied Computing (SAC’06). (2006) 1532–1539

29. Wang, W., Ding, H., Dong, J., Ren, C.: A Comparison of Business Process Mo-

deling Methods. In: Int’l Conf on SOLI’06. (2006) 1136–1141

30. Mendling, J., Recker, J., Reijers, H.A., Leopold, H.: An Empirical Review of

the Connection Between Model Viewer Characteristics and the Comprehension of

Conceptual Process Models. In: Information Systems Frontiers. (2018) 1–25

31. Kiepuszewski, B., Hofstede, A.H.M.T., Bussler, C.: On Structured Workflow Mo-

delling. In: Proc 12th Int’l Conf on Adv Inf Systems Engineering. (2000) 431–445

32. Mendling, J., Strembeck, M., Recker, J.: Factors of Process Model Comprehension-

Findings from a Series of Experiments. In: Decis. Support Syst. (2012) 195–206

33. Gruhn, V., Laue, R.: Adopting the Cognitive Complexity Measure for Business

Process Models. In: 5th Int’l Conf on Cog Inf (ICCI’06). (2006) 236–241

34. Gruhn, V., Laue, R.: Reducing the Cognitive Complexity of Business Process

Models. In: 8th Int’l Conf on Cognitive Informatics (ICCI’09). (2009) 339–345

35. Zugal, S., Pinggera, J., Reijers, H., Reichert, M., Weber, B.: Making the Case for

Measuring Mental Effort. In: EESSMod’12. (2012)

36. Martini, M., et al.: The Impact of Working Memory and the Process of Process

Modelling on Model Quality: Investigating Experienced versus Inexperienced Mo-

dellers. In: Scientific Reports. Volume 6. (2016)