A Flow Measurement Instrument to Test the Students’ Motivation in

a Computer Science Course

Nour El Mawas and Jean Heutte

CIREL (EA 4354), University of Lille, Lille, France

Keywords: Learning, Students’ Motivation, Computer Science, Flow, Optimal Experience.

Abstract: Motivate students is a top research aspect for many research communities, schools, universities, and

institutions. In this context, motivation has an important role in the leaning process and particularly in the

students’ success and the drop-out avoidance. This paper proposes a flow measurement instrument in order

to test the students’ motivation in a Computer Science course. The experimental study involved 33 students

that answer a same questionnaire twice in a period of one week. The temporal stability, internal consistency

and convergent validity of the first English version of the Flow in education scale (EduFlow) were examined.

The results show that autotelic experience (well-being provided by the activity itself) is significantly

positively correlated with academic achievement. This research work is dedicated to Education and Computer

Science active communities and more specifically to directors of learning centres / pedagogy departments,

and the service of information technology and communication for education (pedagogical engineers) who

meet difficulties in evaluate students’ motivation in a specific course.

1 INTRODUCTION

Nowadays, education is facing big changes based on

concepts, theories, principles, and methods.

Motivation is one of the most important factor that

universities/institutions/teachers need to target in

order to improve students' learning (Bhoje 2015).

Palmer (2007) reviews the student motivation as an

essential element that is necessary for the education

quality.

According to Weiner (1992), motivation is the

study of the determinants of thought and action—it

addresses why behaviour is initiated, persists, and

stops, as well as what choices are made. In general,

most teachers are aware of the importance of keeping

students motivated. According to Goleman (1996),

“The extent to which emotional upsets can interfere

with mental life is no news to teachers. Students who

are anxious, angry, or depressed don’t learn; people

who are caught in these states do not take in

information efficiently or deal with it well.”

The efforts of teachers to motivate their students

are not always successful, probably due to lack of

training and deep understanding of all the issues

involved in the class. The motivation behind this

work is the teachers’ difficulty to evaluate students’

motivation in instructional situations.

Optimal experience (or Flow) “is a gratifying

state of deep involvement and absorption that

individuals report when facing a challenging activity

and they perceive adequate abilities to cope with it”

(EFRN 2014). The phenomenon is described by

Mihaly Csikszentmihalyi in 1975 in order to explain

why people perform activities for the activity itself,

without extrinsic rewards. During flow state, people

are deeply motivated to persist in their activities and

to perform such activities again (Csikszentmihalyi,

1975). The experience is triggered by a balance

between a person’s skills in an activity and the

challenges afforded by the lifelong learning

environment. Flow has been shown to promote

learning and personal development because deep and

total concentration experiences are intrinsically

rewarding, and they motivate students to repeat an

activity at progressively higher levels of challenge

(Csikszentmihalyi et al., 2005). Potentially due to its

positive consequences, flow research is further

growing in the new millennium, and there is a

plethora of empirical articles dedicated to this

phenomenon.

In this paper, we are focusing on a flow

measurement instrument in order to test the students’

motivation in a Computer Science course. We choose

the Research in Computing course in a Master

El Mawas, N. and Heutte, J.

A Flow Measurement Instrument to Test the Students’ Motivation in a Computer Science Course.

DOI: 10.5220/0007771504950505

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 495-505

ISBN: 978-989-758-367-4

495

programme entitled Cloud Computing because

students in this field do not see the immediate benefit

to have this course in their curriculum. Note that, in

our context, ‘instrument’ is a novel psychometric

scale to assess optimal experience in educational

situations.

The paper is organized as follows. Section 2

presents some research work done in the area of flow

and motivation to learn, focusing on flow in

education. Section 3 describes our flow measurement

instrument. Section 4 highlights our case study in an

engineering school in Dublin. Section 5 presents the

results of the case study. Section 6 summarizes the

conclusion of this paper and presents its perspectives.

2 RELATED WORK

2.1 Flow and Motivation to Learn

As Csikszentmihalyi and LeFevre (1989) noted,

“when both challenges and skill are high, the person

is not only enjoying the moment, but is also stretching

his or her capabilities with the likelihood of learning

new skills and of increasing self-esteem and personal

complexity”. In this manner, the concept of flow is

inherently relevant to learning and particularly

important within educational settings.

According to (Culbertson et al., 2015), research

findings regarding flow within learning contexts have

demonstrated that flow is associated with heightened

creativity, persistence in studies (Nakamura 1988),

and overall learning and academic performance

(Csikszentmihalyi et al., 1993). In addition, there is

evidence that flow is related to teaching effectiveness

and that flow within the classroom can crossover

from one individual to others (e.g., from teacher to

students);.

Optimal motivation and learning occur when

perceived challenge and perceived skill are balanced

and high. An imbalance between perceived challenge

and skill can lead to decreased motivation, such as

boredom when skills exceed challenge, or anxiety

when challenge is higher than skill (Csikszentmihalyi

2014). Optimal motivation promote the most positive

psychological / developmental / behavioural

outcomes and psychological well-being (Deci and

Ryan, 2002).

High perceived skill is especially advantageous

when perceived difficulty is high. Learners who

perceive that their skills are high and matched with

the level of challenge report higher enjoyment,

interest, and positive affect (Shernoff et al., 2003),

and are more likely to persist or wish to continue with

a task (Csikszentmihalyi et al., 1993).

The main topic of flow in education was the link

of flow with motivational indicators. Beside

motivation, some studies related flow to (a)

engagement (Mesurado et al., 2016); (b) goal

orientation (Oertig et al., 2014), (c) achievement

motives (Engeser and Rheinberg, 2008), (d) interest

(Bachen et al., 2016). This is not surprising because

many authors consider flow experience as a state of

the optimal motivation (Deci and Ryan, 2002; Heutte

2017).

The topic of flow in education has often been

studied in combination with other theories. Many

previous studies have examined the connection

between flow and intrinsic motivation (Keller et al.,

2011). Intrinsic motivation involves doing a

behaviour because the activity itself is interesting and

spontaneously satisfying (Deci and Ryan, 2002).

However, some authors introduce confusion by

considering flow as intrinsic motivation. Indeed, it is

quite possible to have an optimal experience during

an activity that has not been chosen for intrinsic

reasons (which is not a free choice). This is often the

case like the first time a student performs a task to

respond to a teacher's request without any intrinsic

motivation. It is thus possible to see that sometimes it

can be the challenge (complexity or task

requirements) imposed by a prescribed task that goes

in a completely unexpected way (for the student) to

induce the state of flow, as if the flow literally fell on

him without his expectation. In this case, it is during

the activity that the student will find himself or herself

gradually absorbed by the task and it is this

absorption, combined with the fact of realizing that he

or she is progressing (sometimes beyond what he or

she thought he or she was capable of), that will bring

pleasure to the work. Of course, it is this phenomenon

that may induce the desire to re-engage in the task,

this time for intrinsic reasons. Therefore, it would be

more accurate to say that intrinsic motivation can be

a consequence of flow (because the opposite is not

always true). Thus, even if obviously all forms of

autonomous motivation can promote flow, the fact

remains that the confusion between intrinsic

motivation and flow is indeed a conceptual error

(Heutte 2017).

Other studies on motivation and flow are more

linked to self-efficacy (Bandura 1997). Results

highlight that self-efficacy is linked to flow

frequency, higher levels of challenge, and skills.

These results also show that self-efficacy predicts

flow over time (Heutte et al, 2016). High efficacy

beliefs levels have a positive impact on flow

CSEDU 2019 - 11th International Conference on Computer Supported Education

496

Table 1: Some instruments examples used to study flow in an educational context.

Scales

Authors

Items

Dim

Flow Questionnaire* (Flow Q)

Csikszentmihalyi and Csikszentmihalyi (1988)

n.a.

Flow in Human-Computer Interaction

Ghani and Deshpande (1994)

Flow in Online Environments

Novak et al (2000)

Flow State Scale-2 (FSS-2)

Jackson and Eklund (2002)

Flow-Kurzskala (FKS)

Rheinberg et al (2003)

EGameFlow

Fu et al (2009)

Échelle de mesure du flow en éducation

(EduFlow)

Heutte et al (2014)

Échelle de mesure du flow en éducation-2

(EduFlow-2)

Heutte et al (2016)

Note. * only for qualitative study

experiences in academic settings (Heutte et al., 2016;

Salanova et al., 2006). Various aspects of Bandura's

(1986) self-regulation learning model were shown to

exert a significant and positive effect on flow state

(Chen and Sun, 2016). Higher congruence between

one’s implicit motives and self-attributed motives is

associated with better self-regulation, goal

attainment, and flow (Rheinberg and Engeser, 2012).

Some studies highlight collective (or social)

motivational conditions of flow: collective efficacy

beliefs predict collective flow over time (Salanova et

al., 2014).

In any case, most studies show that the relation

between flow and learning is complex because the

learning process is not simple. Flow predicts

motivational outcomes (intrinsic motivation, interest,

self-efficacy, self-regulation, persistence, etc.), but

not always task performance (Durik and Matarazzo,

2009).

2.2 EduFlow

Various instruments have long been used to study

flow in educational contexts (Table 1). However,

according to Csikszentmihalyi, before the

development of the Flow in education model

(EduFlow) (Heutte et al., 2014), there was no short

multidimensional scale designed and dedicated

specifically for education (some generic scales were

applied in education without being initially designed

for this domain). “The understanding of flow within

educational settings, however, is limited by the

methodological approaches to date. For example,

most research on the correlates of flow has been

cross-sectional and therefore incapable of

establishing the causal nature of the relationships

between the potential antecedents and consequences

of flow. In addition, cross-sectional studies must rely

on measures assessing recalled flow as opposed to

direct measures of flow at the times of the activities”

(Culbertson et al., 2015). Note that in table 1, nb

refers to number and dim to dimension.

In order to carry out a convergent validity test

with this first English version of the Flow in

Education Scale (EduFlow), we have selected two

standardized measurement instruments whose main

characteristics are: (1) short scales and (2) scales very

regularly used in international scientific work. In our

context, we use the Flow Short Scale (Rheinberg et

al., 2003) and the General Self-Efficacy Scale

(Schwarzer, and Jerusalem, 1995).

3 OUR FLOW MEASUREMENT

INSTRUMENT

3.1 An Overview of Our Approach

Figure 1 shows an overview of our design and

evaluation approaches. Three actors are involved in

the design of our flow measurement instrument: the

psychologist researcher who designs the instrument,

the learning psychologist who uses the proposed

instrument in order to contextualize it, and the teacher

who validates the contextualized instrument based on

the course and the vocabulary used in the class.

Regarding the use of the instrument, the learning

psychologist puts the contextualized instrument in the

dedicated learning environment (for example lime

survey) and the teacher runs it in the class where

students answer the questions of the contextualized

instrument. Then, the learning psychologist

anonymizes and analyzes student’s answers via a

statistical analysis software like SPSS statistic, R, etc.

A Flow Measurement Instrument to Test the Students’ Motivation in a Computer Science Course

497

Figure 1: An overview of our approach from the design side (on the left) and the evaluation/use side (on the right).

3.2 Instruments

The Flow in education scale version 2 (EduFlow-2) is

a twelve-item scale and it differentiates 4 flow

dimensions (there are three items per dimension):

- FlowD1-Cognitive Control: a strong feeling of

control, specifically over one’s actions,

characterized by a feeling of ability to deal with

the situation and a feeling that the student knows

how to deal with whatever comes next (“I feel

completely in control of my actions”);

- FlowD2-Immersion and Time Transformation:

alteration in the perception of time, sometimes

leading to a lengthened duration of immersion in

the task (“I am wholly absorbed in what I am

doing”);

- FlowD3-Loss of Self-Consciousness: lack of self-

concern related to an increase in importance of the

psycho-social dimension of learning (“I don’t care

about what others may think of me”);

- FlowD4-Autotelic Experience: well-being

provided by the activity itself enhances

persistence and the desire to engage in the activity

again (“This activity brings me a sense of well-

being”).

When tested via Confirmatory Factor Analysis, the

EduFlow-2 showed significant improvement in all fit

indices (Heutte et al., 2016). We have consequently

gathered FlowD1, FlowD2 and FlowD3, namely

Cognitive Control, Immersion and Time

Transformation, and Loss of Self-Consciousness, under

Cognitive Absorption (Figure 2).

The EduFlow has three main advantages:

- It suits flow measurement in various educational

contexts;

- It is a short instrument (reducing respondent

burden);

- It highlights the difference between four

dimensions of flow that are related to a cognitive

process.

This scale was used to measure flow after

classrooms activities (7-point scale).

Flow was complementary measured with the

Flow Short Scale (Rheinberg et al., 2003). This scale

measures all components of flow experience with ten

items (“My mind is completely clear”) and was used

to measure flow after classrooms activities (7-point

scale). The first flow model (Csikszentmihalyi 1975)

proposes that flow occurs when the actor perceives a

balance between the challenge of the activity and his

or her own skill. Due to theoretically inconsistent

results, this model was reformulated: the revised

model proposes that flow is experienced only when

challenge and skill are both high (Csikszentmihalyi

and Csikszentmihalyi, 1988), that’s why according to

(Engeser and Rheinberg, 2008), we add three

additional items to measure the perceived importance

(‘‘Something important to me is at stake here’’, ‘‘I

won’t make any mistakes here’’, and ‘‘I am worried

about failing’’). The experienced difficulty of the

task, perceived skill and perceived balance were

measured on a 7-point scale.

Many studies on motivation and flow are linked

to Social Cognitive Theory (Bandura, 1986). Results

highlighting that self-efficacy is linked to flow

frequency and have a positive impact on optimal

experiences in academic settings (Heutte et al., 2016;

Salanova et al., 2006). The German version of

General Self-Efficacy Scale (GSES) developed in

1979 by Jerusalem and Schwarzer and later revised

(Schwarzer, and Jerusalem, 1995), and adapted to 26

other languages by various co-authors. GSES is a ten

items scale; (“It is easy for me to stick to my aims and

accomplish my goals”) created to assess a general

sense of perceived self-efficacy with the aim in mind

to predict coping with daily hassles as well as

adaptation after experiencing all kinds of stressful life

events. This scale was used to measure flow after

classrooms activities (7-point scale).

CSEDU 2019 - 11th International Conference on Computer Supported Education

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Figure 2: Cognitive Absorption Modelling (Heutte 2017).

4 CASE STUDY

The goal of this research study was to investigate

learner motivation in a Computer Science course.

This section presents the evaluation methodology

applied and case study set-up.

The evaluation included a group of master

students who were taught the Research in Computing

course. In this course, learners acquire the

fundamental computing research skills in Cloud

computing of an MSc programme in order to set up

the foundations of a research project via a major

literature review and a project plan. In the course,

there is an assessment about writing a research paper

(~20 pages). This paper must be submitted at the end

of the course.

The evaluation took place in class, during the

normal hours of study. A total of 33 students (21.2%

woman and 78.8% man) of average age 25.7 years

(Standard Deviation = 2.3 years) with a range from

21 to 30, from an engineering school located in

Dublin, Ireland took part in the case study. Team

members from the school and the Université de Lille

(in France) have prepared and helped perform the

tests. The students volunteered to participate in a

study that required them to complete two surveys for

an average test–retest interval of about seven days.

Once the assessment is corrected, we compare the

grades with the motivation indicators.

The evaluation meets all Ethics requirements.

Prior to running the case study, all required forms

were provided to the students including informed

consent form, informed assent form, plain language

statement and data management plan. These

documents include a detailed description of the

testing scenario, as well as information on study

purpose, data processing and analysis, participant

identity protection, etc.

The evaluation process is illustrated in Figure 3

that presents in details the steps followed by the

researchers. It can be seen that prior to beginning the

evaluation, the consent forms signed by students were

collected. Then the students were introduced to the

research case study and asked to review and sign the

assent form. The students had roughly 20 minutes to

answer the questionnaire (see appendix).

The learner motivation questionnaire assessing

student motivation was collected. After 1 week of this

evaluation, the students were asked to answer the

same questionnaire in order to verify the temporal

stability and internal consistency of our flow

measurement instrument. This instrument is the first

English version of the Flow in education scale.

Figure 3: Evaluation process.

5 CASE STUDY RESULTS

ANALYSIS

The analyses were carried out in two stages: (1) verify

certain psychometric qualities of our novel

instrument EduFlow, and (2) study links between

academic success and different psychological

determinants of motivation highlighted by this new

measurement instrument. Note that all statistical

Collection of the consent

forms (signed by the students)

Description of the research

study

Collection of assent forms

Learning experience

Survey

A Flow Measurement Instrument to Test the Students’ Motivation in a Computer Science Course

499

analyses were performed using the Statistical

Package for the Social Sciences (SPSS), version 23.

5.1 Quality of the Eduflow

Table 3 presents the test and retest means, standard

deviations, and test–retest stability coefficients for the

12 items of EduFlow. The results of this first analysis

highlight that there is no significant difference in the

answers at one week interval for each of the 12 items.

This confirms the temporal stability of the EduFlow.

Cronbach’s alpha (Cronbach 1951) is so far the

most frequently reported reliability coefficient. Table

1 presents the internal consistency coefficient alphas

for each dimension of the EduFlow. According to

(Hinton et al., 2014), coefficient alphas .50 to .70

shows moderate reliability, .70 to .90 shows high

reliability, .90 and above shows excellent reliability.

According to Moss and colleagues (1998, cited by

Hair et al., 2006), the .60 level of Cronbach´s alpha is

acceptable.

The coefficient alphas (Table 1) shows excellent

reliability for FlowD3-Loss of Self-Consciousness

(.93), high reliability for FlowD1-Cognitive Control

(.84) and FlowD4-Autotelic Experience (.83),

moderate reliability for FlowD2-Immersion and Time

Transformation (.60).

Although the coefficient for FlowD2 is a little bit

low, the analyses show that the reliability of the scale

is acceptable. This confirms the internal consistency

of each dimension of the EduFlow.

Bartlett’s test of sphericity and the Kaiser–

Meyer–Olkin (KMO) measure of sampling adequacy

(Kaiser 1974) were used to evaluate the strength of

the linear association between the items in the inter-

item correlation matrix (Table 2). Bartlett’s Test was

significant (Chi-Square = 380.55, p < .000) and KMO

value was .76, which is good (if >.70). The number of

students in the case study was too small to conduct an

exploratory factor analysis.

The convergent validity test (Table 3) highlights

that all dimensions of EduFlow are significantly

positively highly correlated (r=.56 to r=.88, p < .01)

with Flow Short Scale (Rheinberg et al., 2003) and

the General Self-Efficacy Scale (Schwarzer and

Jerusalem, 1995). These results are fully in line with

expectations. All these analyses confirm the good

quality of the first English version of the EduFlow.

5.2 Results Focused on Motivation

First of all, we can see in table 4 that all mean score

of motivation indicators (evaluated with 7-point

Likert scale) are particularly high overall, both for all

dimensions of the EduFlow (5.03 to 5.76), the Flow

Short Scale (4.93) and the General Self-Efficacy

Scale (5.56). We also notice that in all selected

indicators, the one relating to difficulty has the lowest

score (4.24).

Contrary to expectations, the results (Table 5)

show that there is no significant link between the

students' self-efficacy and their academic success

(final grade obtained at the end of the course). Among

all the flow indicators, there is only FlowD4-

Autotelic Experience that is significantly related to

academic success (r = .38, p< .05).

Some unexpected results, particularly regarding

flow, may be due to the fact that students did not feel

they were having a difficult experience. Indeed, in

free fields of expression, many students emphasized

the quality of the teacher's pedagogical support,

particularly the time spent even outside the classroom

to ensure that they had understood the requirements

of the prescribed tasks (Prof. Nour El Mawas helped

us a lot. Prof. made us understand things in a very

simple format by giving multiple example. Prof. used

to repeat until we understand the concept clearly. I

thoroughly enjoyed the lecture given by Prof. Nour El

Mawas. I truly appreciate the way you had cleared

Table 1: Reliability Statistics (Cronbach’s Alpha Based

on Standardized Items).

Cronbach’s

Alpha

N of

Items

FlowD1

.835

FlowD2

.602

FlowD3

.926

FlowD4

.831

Table 2: KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy

.764

Bartlett’s Test of

Sphericity

Approx.

Chi-Square

380.554

Sig.

.000

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Table 3: Paired Samples T-test Output.

Paired Differences

Sig.

(2-

tailed)

Mean

Std.

Dev.

Std.

Error

Mean

95%

Confidence

Interval of the

Difference

Inf.

Sup.

Pair 1

FlowD1a (t1 vs t2)

-.273

1.153

.201

-.682

.136

-1.359

.184

Pair 2

FlowD2a (t1 vs t2)

-.030

1.357

.236

-.512

.451

-.128

.899

Pair 3

FlowD3a (t1 vs t2)

.129

2.109

.379

-.645

.903

.341

.736

Pair 4

FlowD4a (t1 vs t2)

-.061

1.638

.285

-.641

.520

-.213

.833

Pair 5

FlowD1b (t1 vs t2)

.030

1.649

.287

-.554

.615

.106

.917

Pair 6

FlowD2b (t1 vs t2)

.091

1.646

.287

-.493

.675

.317

.753

Pair 7

FlowD3b (t1 vs t2)

.188

1.635

.289

-.402

.777

.649

.521

Pair 8

FlowD4b (t1 vs t2)

-.485

1,889

.329

-1.155

.185

-1.474

.150

Pair 9

FlowD1c (t1 vs t2)

-.091

1.721

.300

-.701

.519

-.304

.763

Pair 10

FlowD2c (t1 vs t2)

-.152

1.839

.320

-.804

.501

-.473

.639

Pair 11

FlowD3c (t1 vs t2)

-.030

2.468

.430

-.906

.845

-.071

.944

Pair 12

FlowD4c (t1 vs t2)

-.269

1.845

.362

-1.015

.476

-.744

.464

Note: t1: test ; t2: retest (at one week interval); FlowD1, FlowD2, FlowD3 and FlowD4 are dimensions of EduFlow Scale

(Heutte et.al., 2016), for more information about all items please see appendix.

Table 4: Descriptive Statistics.

Motivation (7-point Likert scale)

Mean

S.D.

Cognitive Control (FlowD1)

5.76

1.35

Immersion and Time Transformation (FlowD2)

5.03

1.12

Loss of Self-Consciousness (FlowD3)

5.45

1.81

Autotelic experience (FlowD4)

5.12

1.60

Flow Short Scale (FSS)

4.93

1.15

Perceived importance (FSS-Imp)

4.48

1.86

Experienced difficulty (FSS-Diffic)

4.24

2.05

Perceived skill (FSS-Skill)

5.45

1.54

Perceived challenge-skill balance (FSS-Balance)

4.90

1.51

General Self-Efficacy Scale (GSES)

5.56

1.19

Grade (0 à 100)

Mean

S.D.

Grade

72.27

13.87

Note: FlowD1, FlowD2, FlowD3 and FlowD4 are dimensions of EduFlow scale (Heutte et.al., 2016); FSS-Imp, FSS-Diffic,

FSS-Skill and FSS-Balance are additional factors include in Flow Short Scale (FSS, Rheinberg, et al., 2003), GSES

(Schwarzer, and Jerusalem, 1995).

A Flow Measurement Instrument to Test the Students’ Motivation in a Computer Science Course

501

Table 5: Correlation among motivational indicators and academic performance (grade).

EduFlow

Cognitive

Abs.

.958**

FlowD1

.899**

.878**

FlowD2

.810**

.845**

.712**

FlowD3

.808**

.889**

.628**

.596**

FlowD4

.743**

.520**

.629**

.440*

.333

FSS

.832**

.820**

.797**

.700**

.668**

.564**

FSS-Imp

.452**

.433*

.332

.385*

.409*

.334

.331

FSS-Diffic

.387*

.440*

.312

.437*

.406*

.127

.191

.129

FSS-Skill

.792**

.814**

.679**

.774**

.697**

.460**

.782**

.391*

.192

FSS-

Balance

.501**

.512**

.477**

.585**

.340

.294

.460**

.253

.570**

.434*

GSES

.875**

.795**

.816**

.697**

.603**

.752**

.781**

.442*

.270

.703**

.535**

Grade

.304

.226

.290

.028

.233

.380*

.259

.076

-.064

.019

-.098

.240

** Correlation is significant at the .01 level (2-tailed).

* Correlation is significant at the .05 level (2-tailed).

Note: FlowD1, FlowD2, FlowD3 and FlowD4 are dimensions of EduFlow Scale (Heutte et.al., 2016); Cognitive absorption

= FlowD1+FlowD2+FlowD3; FSS-Imp, FSS-Diffic, FSS-Skill and FSS-Balance are additional factors include in Flow Short

Scale (FSS, Rheinberg, et al., 2003), GSES: General Self-Efficacy Scale (Schwarzer and Jerusalem, 1995).

everybody’s doubts and was amazed when I found out

that you have made a lot of contributions in the field

of RIC. Will be happy to get a professor like Nour El

Mawas in future. We are really glad to have prof.

Nour for our RIC subject. I haven’t met professor like

Nour in my life ever. I am sure that the notes given

by you will definitely be helpful to us during our

project and I am sure that I am going to score well in

RIC. Thanks Professor.) Note that RIC refers to

Research in Computing.

To summarize the above analysis, we can deduce

that:

(1) All our tests confirm the good quality of the first

English version of the EduFlow: temporal stability,

internal consistency, and convergent validity.

(2) The relation between flow and learning is

complex because the learning process is not simple.

Flow predicts students’ motivation, particularly self-

efficacy, but not always academic performance.

(3) It is better to use a multidimensional

measurement instrument, as EduFlow, to study

students’ motivation in learning situations, because it

allows to highlight some components of the optimal

experience, which is not possible with a

unidimensional scale.

6 CONCLUSIONS

In this research paper, we investigate the motivation

in a Computer Science course. A flow measurement

instrument was designed and tested with students

from a Dublin-based engineering school. This

instrument is the first English instrument to assess

flow in education. The case study proves that the

students’ academic achievement is significantly

correlated with the autotelic experience which

presents the well-being provided by the activity itself.

Future work will aim to expand the research study

on our flow measurement tool by increasing the

number of participants (learners) in order to follow

researchers’ recommendations on exploratory and

confirmatory analyses (Hair et al., 2006). Further

research will include a scale to assess student's

feelings of relatedness (Deci and Ryan, 2014; Richer

and Vallerand, 1998). The motivation and the social

CSEDU 2019 - 11th International Conference on Computer Supported Education

502

belonging impact on the learning will also be

evaluated.

We also want to increase students’ motivation by

promoting an optimal learning environment. In fact,

researchers have shown the benefits of integrating

interpersonal relations on the motivation (Deci and

Ryan, 2002, 2014, Heutte 2017).

ACKNOWLEDGEMENTS

This research is supported by the Dig-e-Lab project

(https://dig-e-lab.eu/fr/) funded under the Interreg

European Union.

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APPENDIX: EduFlow

Read each sentence carefully and answer, on the scale opposite, by checking a number that best

corresponds to what you think.

In general as part of activities related to the Research in Computing course...

Not

all

Partly

Very

much

don't

know

Code

FlowD1a

I trust my ability to meet the high demands of the situation.

FlowD2a

I am wholly absorbed in what I am doing

FlowD3a

I don’t care about what others may think of me.

FlowD4a

I have the feeling I am living a very exciting experience.

FlowD1

I feel completely in control of my actions

FlowD2

I am deeply focused on what I am doing.

FlowD3

I am not concerned about the judgement of others.

FlowD4

This activity brings me a sense of well-being.

FlowD1c

At each step, I know exactly what I have to do

FlowD2c

I am losing track of time.

FlowD3c

I am not worried about what others might think of me.

FlowD4c

When I talk about this activity, I feel such a deep emotion that

I want to share it.

FlowD1: Cognitive control

FlowD2: Immersion and Time transformation

FlowD3: Loss of self-consciousness

FlowD4: Autotelic experience (well-being rooted in the

activity itself)

Note : FlowD1+FlowD2+FlowD3 = Cognitive absorption

Note that the elements written in red were only visible for the

teacher and the learning psychologist.

Please use this reference to cite the Flow in Education scale

(EduFlow)

Heutte, J., Fenouillet, F., Martin-Krumm, C., Boniwell, I., and Csikszentmihalyi, M. (2016).

Proposal for a conceptual evolution of the flow in education (EduFlow) model. 8th European

Conference on Positive Psychology (ECPP 2016), Angers, France.

A Flow Measurement Instrument to Test the Students’ Motivation in a Computer Science Course

505