In the Flow: A Case Study on Self-paced Digital Distance Learning on

Business Information Systems

Anke Schüll

and Laura Brocksieper

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

Keywords: Self-paced e-Learning, Flow, Business Information Systems.

Abstract: This paper investigates the acceptance of a self-paced digital distance learning environment on courses about

Business Information Systems and Management & Control of IT at a university. The aim of the environment

was to avoid monotony and to actively involve the students into their learning process. The course content

was split into small units arranged onto an online roadmap. Different design elements were used along the

progress on the roadmap, each adding to the content, contributing to clarification, understanding, repetition

or memorization. Students could proceed at their own pace, but there was a timetable for discussing the

content in accompanying videoconferences and corresponding deadlines for the tasks to be completed. The

concept was evaluated in a real-life learning situation following the Unified Theory of Acceptance and Use

of Technology (UTAUT), slightly modified to the context. The case study contributes to the body of

knowledge by providing a selection of design elements that can be combined to enrich students’ learning

experiences. The outcomes of the evaluation underline the importance of “flow” for the acceptance of e-

learning environments.

1 INTRODUCTION

In 1999 Weiser and Wilson propagated video

streaming on the internet as a way to provide course

content for students geographically isolated from

educational and academic institutions. Within their

case study, they described the distance learning

programs of their time as poor cousins to traditional

campus-based programs, stigmatized as a necessary

evil, yet unequal to traditional courses (Weiser and

Wilson 1999).

In 2020, Anthonysamy et al. characterized digital

technology as a “catalyst for transformation in

education in this twenty-first century”. That was even

before the pandemic took hold of academic

institutions worldwide and forced them into a rapid

learning curve on digitally-supported distance

learning.

During the pandemic, many universities closed,

separating students from their academic institutions.

Distance learning programs were initiated to make up

for the lack of personal contact, relying heavily on

digital learning materials. And even though massive

advances in digital technology now would allow the

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

“creation of true student-centred learning models”

(Weiser and Wilson 1999), the stigma as an inferior

learning method and a compromise to circumstances

still sticks. The case study presented in this paper

aims to extricate or diminish the stigma by providing

a case study on self-paced digital distance learning,

using a selection of design elements suitable for

enriching the learning experience for student learners

in an academic context.

Digital learning can be described as any

instructional practice relying on digital technology

that effectively supports the learning experience

(Anthonysamy et al. 2020). Active engagement in the

learning process, instead of passive transmission, can

be supported by digital means.

Liu et al. (2005) underline that e-learning

providers should recognise their users not only as

users of a system, but also as learners. They point out

that, in mixed-media e-learning environments, the

design philosophy should emphasize presentations

suitable for building up user’s concentration. Self-

paced e-learning tools can assist in learning content,

preparing lessons or exams as well as in improving

personal skills like problem-solving or meta-

cognitive skills (Marshman et al. 2020).

332

Schüll, A. and Brocksieper, L.

In the Flow: A Case Study on Self-paced Digital Distance Learning on Business Information Systems.

DOI: 10.5220/0011068000003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 332-339

ISBN: 978-989-758-569-2; ISSN: 2184-4992

Figure 1: Roadmap of the course on Business Information Systems.

Students have access to the material, anytime and

anywhere, thereby enabling them to learn

individually, at their own pace (Bautista 2015). Self-

paced learning provides availability of the necessary

resources for learning and encourages students to

organize their learning process autonomously,

independently from the lecturer (Anurugwo 2020),

thus preparing for lifelong learning.

2 ROADMAP OF THE COURSE

The self-paced e-learning environment presented in

this paper was developed in reaction to the pandemic.

The sudden necessity to switch from traditional

teaching/learning situations to digital distance

learning was regarded as a chance to reconsider what

the courses are about and to adapt the means to the

content. The audience of the course on Business

Information Systems are students of BA Business

Administration, BA Economics, and BA Business

Law. At the end of the course, students should be

capable of modeling business processes as a starting

point for the development or evaluation and

customizing of business information systems. They

should be aware of the penetration of businesses with

information systems and should be able to engage in

digital transformation within selected fields of

application.

That successful digitalization goes beyond a mere

electrification of existing processes towards a radical

rethinking of this process, was a frequently repeated

mantra of the course. When considering the challenge

of digital distance learning/teaching, a good sip of our

own medicine seemed overdue. A thorough look at

the learning goals and the content led to the

development of an asynchronous, self-paced e-

learning platform to cover the course content. The

roadmap (figure 1) was accessible on a website. All

materials were online at the beginning of the

semester.

All courses were held online, due to the covid-19-

pandemic. With students spending most of their time

in the learning management system of our university,

a “non-pedagogical” appearance was decided on to

create a more relaxing environment, unrelated to

“learning”. Asynchronous learning allows students to

access training content anywhere and anytime

(Wilson and Weiser 2001). We opted for a rich media

approach, as previous studies revealed that the

acceptance rate of mixed media-based e-learning

content is higher because it generates a high user

concentration (Liu et al. 2005). Thus, different media

and tools were combined to relieve the monotony.

Mini quizzes and learning cards were implemented in

ARSnova (ARSnova 2017), an audience response

system, to trigger students’ engagement (Gröblinger

et al. 2016). As the attention span in digital media is

limited, videos last no longer than 20 minutes. For

variety, internal videos were enriched by external

In the Flow: A Case Study on Self-paced Digital Distance Learning on Business Information Systems

333

videos. URLs were included, linking examples,

software applications and case studies into the

curriculum, without infringement of copyrights.

Even though the learning environment covered

the relevant content, it was accompanied by weekly

video conferences to discuss the content and the

results of the assignments. Even though limited to the

chat function of the video conferencing tool, students

made use of these communication channels to speak

out, ask questions, or to provide additional examples

or recommendations for other students.

The students were encouraged to work

asynchronously and at their own speed. The course’s

roadmap, in combination with a timetable for

discussing the topics gave the students the flexibility

to proceed at will, while keeping a certain pace,

encouraging continuity, and allowing them to connect

with their lecturer and their co-students.

The roadmap’s design elements are largely self-

explanatory (table 1). In the summer semester 2020,

another course was transformed into a similar

concept: Management & Control of IT. Content,

Table 1: Design Elements of the Roadmap.

Design

Element

Function

Each panel corresponds to a chapter of the

course, thus providing visual clues of the

content structure.

Arrows lead the way along the roadmap,

from start to finish.

This icon refers to mini videos on the content

of the course. Videos with external content

(e.g., examples or case studies) are marked

in blue.

URLs link to examples or other practical

clarifications of the content.

This icon is linked with a pdf document

containing tasks and assignments, to deepen

the knowledge or to research specific

content.

The set of slides for each chapter is linked to

this icon as a pdf document.

QR codes link to mini-quizzes or small

evaluations. The icon is linked with a URL,

so that access is granted to the quiz, even

without scannin

the QR code.

This icon can be found at the bottom right of

each panel. It is linked to electronic learning

cards for each chapter.

This icon leads to additional

recommendation of selected literature.

The link to the online survey was included

behind this icon.

layout and design of the roadmap were different but,

except for one, the design elements used for a

digitalization of the course were the same: Additional

literature recommendations were included that went

beyond those already included into the slides.

3 RESEARCH MODEL

The design elements and the students’ perception of

the self-learning environment could be explored in a

natural, real-life context, providing the preliminaries

for case study research (Crowe et al. 2011). This case

could be linked to hypotheses (Flyvbjerg 2016),

followed by a quantitative evaluation. The evaluation

of the acceptance of the learning environment was

based on the unified theory of acceptance and use of

technology (UTAUT) (Venkatesh et al. 2003). The

model aims to explain the use of a type of technology

by the individual perception of four core constructs:

performance expectancy, effort expectancy, social

influence, and facilitating conditions (Yang et al.

2019). This model extends the expressive power of

the Technology Acceptance Model (TAM) (Davis

1989). With UTAUT2 (Venkatesh et al. 2012) this

model was extended to take the specific factors into

account that influence customers’ use of technology:

hedonic motivation, price value and habit (Ain et al.

2016). Within the learning context, several studies are

based on TAM (e.g., Saadé and Bahli 2005; Al-

Azawei et al. 2015; Ibrahim et al. 2018), UTAUT

(e.g., Chao 2019; Almaiah et al. 2019; Salloum and

Shaalan 2019; Persada et al. 2019; Raza et al. 2021)

or UTAUT2 (e.g., Ain et al. 2016; Raman and Don

2013; Arain et al. 2019; Kang et al. 2015. This study

relies mainly on UTAUT and TAM, slightly adopted

to the context.

Perceived usefulness (PU) describes the degree to

which an individual believes that using a certain type

of technology will help to enhance his or her job

performance (Venkatesh and Davis 2000). The job

for students, or rather the assigned tasks within an

academic context, is to accomplish the learning goals.

Adopted to this context, PU can be defined as the

degree of perceived usefulness for accomplishing the

assigned learning goals. E-learning can support

learning activities and uplift educational skills and

performance (Salloum and Shaalan 2019). Therefore,

it is posited that performance expectancy (PE) has a

significant and positive influence on PU (H

Facilitating conditions (FC) refer to the technical and

organizational infrastructure supporting the use of the

e-learning system (Salloum and Shaalan 2019). As

these factors ease the accessibility of the content

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required to achieve the learning goals, it is posited

that facilitating conditions have a positive impact on

perceived usefulness of e-learning environments

). As the platform works within a web page

requiring almost no support, the focus was shifted

onto accessibility and availability of the learning

content. Increasing maturity of e-learning

environments will improve the user friendliness and

will ease the effort to use them (Salloum and Shaalan

2019). This led to the hypothesis that the degree of

ease related to the use of an e-learning environment

has a positive effect on its usage to achieve the

assigned learning goals (H

). The perceived degree of

ease is measured by effort expectancy (EE)

(Venkatesh et al. 2003).

Liu et al. (2005) underline that, within e-learning

environments, users should be recognized as learners,

and the design philosophy should be dedicated to

building up users’ concentration. Csikszentmihalyi

(1990) coined the term “flow” to describe the

psychological state when an individual becomes so

totally absorbed by their activity that they lose their

sense of time or their awareness for their

surroundings. When students reach a state of “flow”,

they concentrate entirely on their learning activity,

which can play a major role in online learning

behaviour (Liu et al. 2005). A study by Saadé and

Bahli (2005), revealed a significant positive effect of

cognitive absorption on PU. Therefore, we posit (H

)

that flow (F) has a positive influence on

accomplishing the assigned learning goals (PU).

PU is a strong predictor of the intention to use a

certain technology (Venkatesh et al. 2003), thus H

posits that PU has a positive effect on the behavioral

intention (BI) to use the digital learning environment.

Image and social influence describe an effect of

compliance to subjective norms by performing a

specific behavior that a person perceives to be

expected by their social surroundings (Venkatesh and

Davis 2000). During the pandemic, students suffered

from social isolation, diminishing the impact of social

influence. Within this rather exceptional situation,

any acceptance or dismissal of a hypothesis on the

impact of social influence on perceived usefulness of

the e-learning platform discussed in this paper would

be biased by the circumstances and incomparable to

previous or following studies. Taking this into

account, social influence was omitted from the

research model with some regret.

An anonymous online survey was conducted

among the students enrolled in both courses that are

following the same design principles: a course on

Management and Control of IT (3

semester

students, summer 2020) and another on Business

Information Systems (1

semester, autumn 2020/21).

As the evaluation of students with higher grades

preceded the evaluation of the course for minors, the

student groups didn’t overlap. The students were

invited to participate in the survey. The participation

was voluntary, there were no incentives, neither gifts,

nor credit points. No personally identifiable

information was gathered. Analysis of the data was

restricted to gaining insights for further development

of the learning environments and for research

purposes. As all students are undergraduate and of

about the same age, and a moderating effect of gender

not the interest of our research, demographic items

were omitted from the questionnaire. A five-point

Likert scale was applied to measure the items (table

2). Some items were inverted. Overall, 361 students

participated in the survey. 139 data sets were

incomplete and had to be dismissed, leaving 222 data

sets for further analysis.

Table 2: Items (PE - Performance Expectancy, FC –

Facilitating Conditions, EE – Effort Expectancy, F – Flow,

PU – Perceived Usefulness, BI – Behavioral Intention).

Code Mean SD Code Mean SD

PE1 3.410 1.106 EE1 4.086 1.335

PE2 3.973 1.301 EE2 4.050 1.299

PE3 4.284 1.324 EE3 4.036 1.252

PE4 4.104 1.357 EE4 4.135 1.273

FC1 3.649 1.140 PU1 3.757 1.172

FC2 4.068 1.325 PU2 3.770 1.149

FC3 3.721 1.246 PU3 3.604 1.247

FC4 3.671 1.327 PU4 3.536 1.176

F1 3.387 1.050 PU5 3.586 1.139

F2 3.189 1.212 BI1 4.018 1.298

F3 3.482 0.985 BI2 3.806 1.298

F4 3.667 1.064 BI3 3.959 1.275

F5 3.554 1.067 BI4 3.383 1.363

4 DATA ANALYSIS

The research model was evaluated using partial least

square (PLS) modeling, as this approach is widely

used in IS research. Using Smart-PLS (v3.3.3)

(Ringle et al. 2015), we first evaluate reliability and

validity of the measurement model, followed by an

evaluation of the structural model.

Cronbach’s alpha was calculated to ensure

internal consistency among the items (table 3). The

value is greater than 0.7, thus fulfilling the criteria

(Tabachnick and Fidell 2014). With a Cronbach’s

alpha > 0.9 on EE and PE, the value is almost too high

for these constructs. The composite reliability (CR)

should be above 0.7 to indicate a reliability of the

In the Flow: A Case Study on Self-paced Digital Distance Learning on Business Information Systems

335

results (Hair et al. 2006), which is true for all

constructs. The Average Variance Extracted (AVE).

indicates the convergence validity of the constructs

and ranges from 0.661 to 0.952, all well above the

threshold of 0.5 (Fornell and Larcker 1981).

Table 3: Scale Reliability (PE - Performance Expectancy,

FC – Facilitating Conditions, EE – Effort Expectancy,

F – Flow, PU – Perceived Usefulness, BI – Behavioral

Intention).

Cronbach's

Alpha

Composite

Reliabilit

Average Variance

Extracted (AVE)

BI 0.888 0.923 0.751

FC 0.871 0.913 0.725

EE 0.975 0.983 0.952

F 0.868 0.906 0.661

PU 0.933 0.949 0.788

PE 0.890 0.925 0.756

Cross Loadings confirm that the loading of each

item on its own constructs is higher than on the others,

and the item loadings on the construct are all above

0.7, thus confirming individual reliability (table 4).

Table 4: Cross-Loadings (PE - Performance Expectancy,

FC – Facilitating Conditions, EE – Effort Expectancy,

F – Flow, PU – Perceived Usefulness, BI – Behavioral

Intention).

BI FC F EE PE PU

BI1 0.857 0.686 0.628 0.677 0.731 0.705

BI2 0.931 0.743 0.730 0.612 0.780 0.784

BI3 0.907 0.783 0.661 0.714 0.784 0.756

BI4 0.763 0.511 0.581 0.341 0.610 0.589

FC1 0.687 0.883 0.619 0.617 0.692 0.675

FC2 0.736 0.885 0.568 0.812 0.788 0.693

FC3 0.714 0.904 0.612 0.670 0.684 0.677

FC4 0.549 0.721 0.464 0.430 0.490 0.507

F1 0.561 0.515 0.786 0.365 0.538 0.568

F2 0.346 0.292 0.625 0.206 0.306 0.386

F4 0.692 0.633 0.877 0.450 0.599 0.638

F5 0.700 0.638 0.882 0.593 0.631 0.645

F6 0.685 0.565 0.866 0.492 0.576 0.626

EE1 0.658 0.734 0.509 0.982 0.727 0.659

EE2 0.688 0.755 0.555 0.970 0.752 0.679

EE4 0.665 0.720 0.505 0.975 0.719 0.663

PE1 0.653 0.587 0.618 0.402 0.750 0.605

PE2 0.711 0.692 0.549 0.682 0.902 0.665

PE3 0.803 0.759 0.600 0.799 0.925 0.741

PE4 0.751 0.692 0.561 0.693 0.890 0.696

PU1 0.745 0.706 0.625 0.676 0.725 0.885

PU2 0.741 0.738 0.662 0.670 0.746 0.884

PU3 0.727 0.599 0.638 0.576 0.656 0.881

PU4 0.706 0.602 0.617 0.498 0.659 0.901

PU5 0.725 0.688 0.632 0.597 0.670 0.887

Discriminant validity can be confirmed by the

Fornell-Larcker Criterion (table 5). The AVE square

root is presented as bold values in the upper values of

each column. These values should be above 0.5 and

higher than the squared correlation of the other

constructs (Fornell and Larcker 1981). Table 5 shows

that the Fornell-Larcker Criteron is satisfied, thus

confirming discriminant validity.

Table 5: Fornell-Larcker Scale (PE - Performance

Expectancy, FC – Facilitating Conditions, EE – Effort

Expectancy, F – Flow, PU – Perceived Usefulness,

BI – Behavioral Intention).

BI FC EE F PU PE

BI 0.867

FC 0.794 0.852

EE 0.687 0.755 0.976

F 0.752 0.668 0.536 0.813

PU 0.822 0.755 0.684 0.716 0.888

PE 0.842 0.788 0.751 0.668 0.781 0.869

To test the hypotheses, a bootstrap procedure was

applied with 1,000 subsamples and a significance

level of 0.05. The path coefficients and the t-values

support all hypotheses (table 6).

In this context PU is defined as the degree of

perceived usefulness for accomplishing the assigned

learning goals. With H

it was postulated that

performance expectancy has a significant and

positive influence on perceived usefulness (PU),

which was supported by this data set (β = 0.332; t =

4.237 and p < 0.05).

Table 6: Path coefficients (PE - Performance Expectancy,

FC – Facilitating Conditions, EE – Effort Expectancy, F –

Flow, PU – Perceived Usefulness, BI – Behavioral

Intention).

Hypothesis

Path

Coefficients

T Statistics

O/STDEV

Values

: PE ->PU 0.332 4.237 0.000

: FC -> PU 0.206 2.765 0.006

: EE -> PU 0.123 1.999 0.046

: F -> PU 0.290 5.253 0.000

: PU -> BI 0.822 32.754 0.000

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This is consistent with previous research in the e-

learning context (e.g. Mahande and Malago 2019).

Several students used the free text form to elaborate

on this. Some sample statements: “What I liked most

was that I could decide myself, if and how much I like

to learn in a week.”

“Better than most, because you could decide on

the pace yourself, relisten without hurry and do some

research on the internet without missing parts of the

lecture.”

“I prefer working on something on my own,

therefore I liked the self-paced environment a lot. I

think that I could remember the content better than in

other courses.”

“I enjoyed the course a lot. The topics don’t drag

too long and to every topic there are examples from

reality. The practical examples make everything

understandable and anchors the knowledge in the

brain.”

Students’ learning preferences as well as their

perception of the learning setting differ, and the

learning environment didn’t work well for all of them.

Some statements reveal a more critical perception.

Difficulties were voiced in relating the content to the

assigned learning goals and in prioritizing the

content: “Not bad, but in some articles and videos it

is hard to figure out what to take out of it. They were

not uninteresting, but you didn’t really learn a lot. I

have no clue what I should have learned out of all

these parts.” Others underlined their preference for

traditional lectures: “[…] I would have preferred

learning a bit more dynamically – directly from the

lecturer and at the university. Nonetheless, I like

having the option to do everything from home.”

That facilitating conditions have a positive impact

on perceived usefulness of e-learning environments

) was also supported (β = 0.206; t = 2.765 and p <

0.05). This is in line with, e.g., Mahande and Malago

(2019). Within the items, the accessibility of the

content of the learning platform was the dominant

aspect. Several students commented on a low voice

quality in the explanatory videos. These require

massive improvement, and some found the layout

confusing. With H

it was postulated that the degree

of ease, related to the use of an e-learning

environment, has a positive effect on its usage to

achieve the assigned learning goals. The hypothesis

was supported by this data set (β = 0.123; t = 1.999

and p < 0.05). This is in accordance with literature

(e.g., Mahande and Malago 2019), but it is necessary

to point out that this hypothesis would not have been

accepted at another level of significance.

That flow has a significant influence on perceived

usefulness (H

) was also supported by the data of this

data set (β = 0.290; t = 5.253 and p < 0.05). Flow can

come with a high concentration on the learning

activity. Therefore, it can be an important factor in

online learning behaviour (Liu et al. 2005). Several

students wrote about “fun” in their comments, e.g.,

“It’s really fun to learn like this, and some facts are

easier to understand.” Another student mentioned

becoming carried away: “I finished more than half of

the self-learning environment within three days; not

because I would like to finish the course, but because

it is difficult to stop, once you started.

”Some students commented on curiosity: “The

playful roadmap arouses my curiosity”. At the

beginning, I thought Business Information Systems

would not interest me at all, but my curiosity grew

with every panel.” This is in line with previous

literature on learning management systems, in which

a significant positive effect of cognitive absorption

and pleasure on PU became evident (Saadé and Bahli

2005).

Avoiding monotony and activating the students

was an important aspect of this learning environment.

One student wrote that “the many short videos are

more motivating to proceed than a […] script,

because of the variation, and it is more interesting.”

The data set and the comments indicate that the

concept worked for many but not for all students: “I

can’t complain, but it takes a lot of discipline to work

with the learning environment.”

posits that PU has a positive effect on the

behavioral intention (BI) to use the digital learning

environment. This hypothesis was also supported (β

= 0.822; t = 32.754 and p < 0.05), which is in line with

previous studies (e.g. Liu et al. 2005).

5 CONCLUSIONS

During the pandemic, lecturers worldwide struggled

to find new ways of distance teaching. They

developed new skills in media production, video

editing, video conferencing and social media usage.

Over the months, they fought their way along a steep

learning curve and some innovative learning/teaching

concepts evolved. However, even though

“digitalization” is the magic bullet transforming

business processes worldwide, when it comes to

courses, the stigma of digitalization as being an

inferior learning method to traditional courses sticks.

The case study presented in this paper aims to

extricate the stigma. This paper contributes to the

body of knowledge on self-paced digital distance

learning, by providing a selection of design elements

suitable for enriching the learning experience for

In the Flow: A Case Study on Self-paced Digital Distance Learning on Business Information Systems

337

student learners in an academic context and by

validating the concept in a real-time learning

situation. Students appreciate having access to

explanatory videos anywhere and anytime. They

appreciate the flexibility to choose when and where

to learn, thus taking responsibility for their own

learning process. The evaluation underlines the

importance of “flow” for the acceptance of e-learning

environments and shows examples of design

elements that can be combined to enrich students’

learning experiences.

There are several limitations to take into

consideration: The survey was embedded into the

learning environment at the very end. Not all students

came that far. Students who skipped the course did

not participate in the survey, therefore the results will

be biased.

Restricting the analysis on the acceptance of this

specific platform, allows an evaluation more pointed

towards the learning goal, yet with the price of losing

the necessary number of participants to calculate

statistically reliable numbers. The self-paced digital

distance learning environment presented here, and the

evaluation on its acceptance may not generalize well,

but the students participating in the survey are the

target group. Their impression, their feedback and

their hints to further improvements are highly

relevant for the next iteration of self-learning

environments developed for the next courses in the

semesters to come. Evaluating the results on a broader

scale would be a suggestion for further research.

Due to the urgency of the situation, the concept

had to prove itself in a real-life learning situation.

Thus, there is no control group for comparing the

results. Another aspect to consider is that, due to the

pandemic-circumstances, the platform wasn’t used

voluntarily. If students would choose these platforms

at will, this would be worth further exploration. As

the situation was exceptional, an evaluation of this

platform in a standardized situation would be

recommended.

Early research on asynchronous learning already

raised the concern of lacking interaction between

students and faculty, and the fear of some faculties

that e-learning would make instructors obsolete

(Wilson and Weiser 2001). Within their pilot study,

Wilson and Weiser brought up two research

questions: Will students quit attending classes when

an asynchronous mode of learning is available? Will

they use the available technology to assist their

learning process or to support their laziness? (Wilson

and Weiser 2001). Twenty years later, these questions

still need to be answered.

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