Design and Validation of an Emerging Educational Technologies

Acceptance and Integration Questionnaire for Teachers

Ana González-Marcos

, Fermín Navaridas-Nalda

and Jesús Castellano-Latorre

Department of Mechanical Engineering, University of La Rioja, c/San José de Calasanz 31, Logroño, La Rioja, Spain

Department of Education, University of La Rioja, c/San José de Calasanz 31, Logroño, La Rioja, Spain

Center for Educational Innovation, Government of La Rioja, c/Marqués de Murrieta 76, Logroño, La Rioja, Spain

Keywords: Educational Technologies, Questionnaire, Validation, Structural Equation Modelling.

Abstract: The integration of technology in the teaching and learning processes depends to a large extent on the teaching

attitude towards these resources. Thus, our research is focused on the internal beliefs that predisposed teachers

to their acceptance and pedagogical use in schools. The main objective of this work was to design and validate

a questionnaire for non-university teachers with the main purpose of identifying the most important factors

of their teaching attitude towards the use of emerging technological resources. Taking the Theory of Planned

Behaviour and its subsequent development as reference behavioural research models, we developed a

questionnaire that interrelatedly combines the following factors: perceived usefulness, perceived self-efficacy,

facilitating conditions and the subjective norm. A total of 661 teachers were recruited from public schools in

La Rioja (Spain). Content validity, construct validity, internal consistency and reliability of the questionnaire

were undertaken as part of the validation process. The results showed good psychometric qualities in the

questionnaire and indicated that the instrument is reliable and a valid measure to identify the factors that

explain the teaching attitude towards technological resources. The findings have both theoretical and practical

implications for the educational administration, management teams and teachers.

1 INTRODUCTION

For many authors (Johri et al., 2014; Hubalovsky et

al., 2019; Navaridas et al., 2020), the acceptance and

standardized use of information and communication

technologies (ICT) in educational processes in

schools are key factors for promoting change and

innovation in teaching. The use of ICT allows to

transform traditional ways of acquiring, preparing,

organizing and transmitting knowledge in the

classroom. In accordance with this idea, in recent

years there are also various international

organizations and institutions that have tried to

promote their development to improve access to

education and training, raise the quality of learning

and promote a culture of collaboration between

educational institutions (Commission of the

European Communities, 2001; OECD, 2015;

UNESCO, 2009). Thus, for example, among the

standards proposed by UNESCO (2008, 2011) on

https://orcid.org/0000-0003-4684-659X

https://orcid.org/0000-0003-4147-1311

ICT skills for teachers, those that refer to the creation

of a new "technology-based teaching model" stand

out. In this sense, teachers must take full advantage of

technological progress in order to create new

environments and pedagogical methods focused on

learning, to develop innovative didactic material and

to exchange experiences of the application of these

technologies to teaching, training and research. All

this allows to improve the practices and results of

current education.

In the current educational scenario, which is

affected by the pandemic derived from Covid-19,

some research works seem to show that it is not

enough to increase investment in ICT (Montenegro et

al., 2020) to achieve these objectives. Indeed, despite

the efforts made by the Educational Authorities of our

region in recent years to provide ICT resources to the

basic and compulsory education centres, the results

provided by some studies (Santiago et al., 2014; Pérez

and Rodríguez, 2016) question whether these

González-Marcos, A., Navaridas-Nalda, F. and Castellano-Latorre, J.

Design and Validation of an Emerging Educational Technologies Acceptance and Integration Questionnaire for Teachers.

DOI: 10.5220/0010526705250532

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 1, pages 525-532

ISBN: 978-989-758-502-9

525

resources are used effectively for educational

purposes. In general, it seems to be observed that

teaching decisions related to the use of ICT in

teaching and learning processes are largely

determined by the teachers' own conceptions of these

resources (e.g., perceived usefulness, perceived self-

efficacy, perceived availability). In this case, the own

experiences and formative experiences of the

teaching staff are important elements in their teaching

perceptions.

It is essential and necessary that educational

institutions develop an energetic training policy for

their teaching staff (Fernández-Cruz and Fernández-

Díaz, 2016). Thus, for this training to have the desired

effect at the teaching level, during the planning

process, it may be interesting to start with a study of

the teachers' own system of motivations or beliefs as

the main components of their attitude and intention to

use the technological resources during the teaching

and learning processes in the educational centres

(Straub, 2009).

In this sense, many of the reviewed studies

propose using research models based on the Theory

of Reasoned Action -TRA- (Fishbein and Ajzen

1975, Ajzen and Fishbein 1980), which considers that

a person's decision to execute a certain action is

fundamentally conditioned by an attitudinal

component (understood as the relatively stable

predisposition of a person to respond favorably or

unfavorably to a specific situation or stimulus) and a

component based on normative beliefs (understood as

the perception of a person about the different

pressures to which it may be subjected from the social

circles of belonging or reference). According to this

premise, one of the models most used to study

technological acceptance is the Technology

Acceptance Model -TAM- (Davis, 1989; Davis et al.,

1989; Venkatesh and Davis, 2000), where perceived

usefulness and perceived ease of use are included as

determinants of human behavior.

In an attempt to integrate in a harmonious and

related way the different factors considered by the

previous models as determinants of the effective

implementation of the technology, Venkatesh et al.

(2003) formulated the Unified Theory of Acceptance

and Use of Technology (UTAUT). Although in a very

broad framework of research assigned to different

fields of specialization (Cimperman et al., 2016;

Khalilzadeh et al., 2017; Khaksar et al., 2019) there

seems to be a consensus on the factors that can predict

to a greater extent the acceptance and use of

technological resources. In the specific context of

education, there seems to be some controversy and

inconsistent results both in the level of influence and

in the relationships established between the identified

factors (Scherer et al., 2019). Sensitive to these

findings in the educational field, in this research we

set the following objective: to design and validate a

questionnaire to corroborate its goodness with respect

to a theoretical model of research on acceptance and

technological integration.

2 METHOD

2.1 Research Model

In accordance with the proposed research objective,

and based on a review of the most prominent models

of technological acceptance in the specialized

literature in this field (Davis, 1989; Venkatesh and

Davis, 2000, Venkatesh et al., 2003; Venkatesh and

Bala, 2008), we designed a theoretical research model

that incorporates the factors that we consider most

appropriate and relevant to the context under study.

In this sense, the designed model combines the factors

highlighted in the TRA, TAM and UTAUT models in

an adapted way (Figure 1).

Figure 1: Theoretical research model (own elaboration).

The definition of each of the factors considered is

summarized as follows:

 Current use: refers to the degree or frequency of

use of technological resources during teaching

activity.

 Attitude: this factor refers to the teacher's

predisposition to respond favorably or

unfavorably to a technological resource.

 Perceived usefulness: refers to the degree to

which a teacher believes that the use of

technological resources will improve their

teaching activity.

 Perceived self-efficacy: the degree to which a

teacher believes that he or she possesses the

knowledge and skills necessary for the

pedagogical use of technological resources.

 Facilitating conditions: this factor refers to the

ease of use perceived as a consequence of the

teacher's control over external conditions or

ATTITUDE

CURRENT

USE

SUBJECTIVE

NORM

FACILITATING

CONDITIONS

PERCEIVED

SELF-EFFICACY

PERCEIVED

USEFULNESS

H10

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526

variables and the availability of organizational

and technical resources.

 Subjective norm: refers to the teacher's beliefs

about what most of the people important to him or

her think about the use of technological resources

during their teaching activity.

Thus, we postulated the following hypotheses that

establish the relationships and influences between the

factors defined in the theoretical model designed:

H1. The teacher's subjective norm or normative

beliefs positively influences perceived

usefulness.

H2. The teacher's subjective norm positively

influences his or her attitude toward the use of

technological resources.

H3. The teacher's subjective norm influences his or

her perceived self-efficacy.

H4. Facilitating conditions positively influences the

teacher’s attitude toward the use of educational

technologies.

H5. Facilitating conditions influences the teacher’s

perceived self-efficacy for the use of

technological resources.

H6. Perceived self-efficacy positively influences the

teacher’s perceived uselfulness of educational

technologies.

H7. Perceived uselfulness of technology positively

influences the teacher’s attitude toward the use

of technological resources.

H8. Perceived self-efficacy positively influences the

teacher’s attitude toward the use of

technological resources.

H9. Perceived self-efficacy positively influences the

current use of technological resources during

teaching activity.

H10. Teachers’ attitude positively influences the

current use of technological resources during

their teaching activity.

2.2 Participants

The aim of the survey is to better understand teachers’

own conceptions of the use of technological resources

for educational purposes and, thus, to help the

educational authorities of our region in the

development of an efficient training policy. Since the

responsibilities of these local educational authorities

are limited to non-university levels, the target

population is the total set of employed teachers in

non-university centres.

Data were collected from 6 April 2020 to 10 May

2020, i.e., after COVID-19 outbreak. This ensured

that the target population had at least a computer at

home for educational purposes: some teachers

borrowed the technological devices from their own

educational centres. The study sample included 661

teachers who carried out their teaching activities from

early childhood education to short-cycle tertiary. The

demographic information of the respondents shown

in Table 1 illustrates the heterogeneity of the sample,

which improves the external validity of the study.

Table 1: Demographic characteristics of the participants.

Demo

hics Cate

Fre

uenc

(

)

Gender

Female 438 (62.3)

Male 223 (33.7)

Age group

21-30

ears 57

(

8.6

)

31-40

ears 172

(

26.0

)

41-50

ears 241

(

36.5

)

51-60 years 183 (27.7)

> 60 years 8 (1.2)

Teaching

experience

≤ 5 years 128 (19.4)

6-10

ears 90

(

13.6

)

11-20

ears 240

(

36.3

)

21-30

ears 131

(

19.8

)

> 30 years 72 (10.9)

Level of

education

taught

Early childhoo

101 (10.9)

Primar

266

(

28.7

)

Lower secondar

246

(

26.6

)

er secondar

125

(

13.5

)

Short-cycle tertiary 102 (11.0)

Othe

86 (9.3)

2.3 Development and Description of the

Instrument

As indicated previously, the questionnaire was

developed after a review of the main theories and

models of technological acceptance and use. It was

designed to gather demographic data on the one hand

and the measurement items of the selected factors on

the other hand. Before its final version, and in

accordance with the classic patterns of content

validity of a survey (Gómez et al., 2013), it was

subjected to the scrutiny of a panel of five experts in

the field of educational research. The focus of this

panel of experts was to ascertain if the purpose of the

questionnaire was clear, if the structure and relevance

of the proposed elements (factors and items) were

adequate, if questions and language were appropriate

and accurate, and if it was necessary to add or remove

any element. Also, they were asked to provide an

overall rating of the questionnaire.

The questionnaire was amended according to the

comments and suggestions of the panel of experts

and, thus, the final version of the survey consists of

39 questions (items): 11 questions to collect

demographic data and 28 questions related to the six

defined factors (see Appendix): (1) subjective norm,

Design and Validation of an Emerging Educational Technologies Acceptance and Integration Questionnaire for Teachers

527

(2) facilitating conditions, (3) perceived usefulness,

(4) perceived self-efficacy, (5) attitude and (6) current

use. The first four factors are mainly predictive in

nature, while the last two refer more to results. The

teachers were asked to indicate their agreement or

disagreement with each item on a Likert-type scale

with five response levels that ranged from 1 (totally

disagree) to 5 (totally agree).

2.4 Data Analysis

Data were analysed using the software package R

version 4.0.3. First, data coding and cleaning was

performed. Then, teachers’ responses were analysed

using descriptive statistics. Next, factor analysis,

along with validity and reliability of construct were

performed. Finally, structural equation modelling

was used to test the goodness of fit indices in a

measurement model and the proposed hypothesis.

3 RESULTS AND DISCUSSION

First, correlations between the measurement items

were investigated (Figure 2). It was assessed that

inter-items correlations were higher than 0.30 and

lower than 0.90, which allows to consider the final

items appropriate for the six factors.

Figure 2: Correlations among measurement items.

3.1 Exploratory Factor Analysis

Exploratory factor analysis was conducted to assess

the purification of measurement items. Thus, the

Kaiser-Meyer-Olkin (KMO) measure and the

Bartlett’s test of sphericity were calculated. With

values of KMO = 0.95 and Bartlett’s test χ

13650.62, df = 378, and p < 0.001, it was confirmed

the sample appropriateness, i.e., the sample was

adequate and the correlation matrix was significantly

different from an identity matrix.

Then, principal component analysis (PCA) with

oblique rotation was used since we assumed that the

factors in the analysis are correlated. This analysis

showed that the 28 items were divided into five

factors, which had eigenvalues greater than 1 and

explained 71.58% of the overall variance.

Percentages of total variance explained were 38.65%

for the first factor, 25.79% for the second factor,

15.54% for the third factor, 8.49% for the fourth

factor, 7.44% for the fifth factor, and 4.10% for the

sixth factor.

3.2 Confirmatory Factor Analysis

Confirmatory factor analysis was applied for

checking the loadings of observed variables (items)

over latent variables (factors or constructs). Also,

convergent and discriminant validity and reliability

were assessed.

Table 2 shows values for factor loading (FL),

composite reliability (CR), average variance

extraction (AVE) and the Cronbach’s alpha (CA).

These results revealed that factor loading values were

above the threshold of 0.5 suggested by Hair et al.

(2010). Also, it was observed that the CR of

subjective norm and current use factors were below

the recommended threshold of 0.7 (Straub et al.,

2004). The same factors showed Cronbach’s Alpha

values below the recommended threshold of 0.7

(Nunnally, 1978). However, these results are still

within the general accepted rule that values of 0.6-0.7

indicate an acceptable level of reliability (Lam, 2012;

Ursachi et al., 2015). Finally, values for AVE were

greater than threshold recommended by Hair et al.

(2010), i.e., 0.5, except for the subjective norm factor.

In this case, since CR is higher than 0.6 AVE, the

convergent validity of the factor (construct) is still

adequate (Fornell & Larcker, 1981).

Discriminant validity was assessed through the

heterotrait-monotrait ratio (HTMT) of the

correlations (Henseler et al., 2015). Table 3 shows

that most HTMT values are lower than the suggested

threshold of 0.85 (Kline, 2011). However, other

authors suggest a value of 0.90 (Henseler et al., 2016).

Thus, discriminant validity of the proposed model

was established.

CSEDU 2021 - 13th International Conference on Computer Supported Education

528

Table 2: Construct reliability and convergent validity.

Construct Ite

FL CR AVE CA

Subjective

Norm

SN1 .57

.66 .42 .65 SN2 .75

SN3 .54

Facilitating

conditions

FC1 .67

.83 .50 .83

FC2 .80

FC3 .72

FC4 .72

FC5 .59

Perceived

usefulness

PU1 .66

.92 .60 .92

PU2 .65

PU3 .82

PU4 .74

PU5 .68

PU6 .87

PU7 .83

PU8 .87

Perceived

self-efficacy

PSE1 .90

.95 .75 .95

PSE2 .89

PSE3 .92

PSE4 .76

PSE5 .88

PSE6 .85

Attitude

AT1 .90

.94 .79 .94

AT2 .95

AT3 .87

AT4 .83

Current Use

USE1 .78

.68 .61 .68

USE2 .66

FL: Factor Loading; CR: Composite Reliability; AVE:

Average Variance Extracted; CA: Cronbach’s Alpha

Table 3: Discriminant validity.

Constructs SN FC PU PSE AT USE

Subjective

Norm

(

)

.42

Facilitating

conditions

(

)

.54 .50

Perceived

usefulness

(PU)

.65 .39 .60

Perceived

self-efficacy

(PSE)

.34 .31 .58 .75

Attitude

(

)

.53 .28 .87 .53 .79

Current Use .63 .40 .77 .58 .73 .61

Diagonal values are AVE and off-diagonals are HTMT

values

Finally, the statistical fitness of the model was

assessed with three sorts of fit indices used in

structural equation modelling (SEM), i.e., absolute,

parsimonious and incremental. Table 4 illustrates that

the obtained results are within the accepted threshold

values for different fit indices, showing the goodness

of the proposed model and questionnaire.

Table 4: Summary of fit indices.

Absolute

fit

measure

Parsimonious

fit measure

Incremental

fit measure

SRMR RMSEA CFI TLI

Acceptable

fit

≤ .08 ≤ .06 ≥ .90

≥

.90

Obtained

fit

.045 .055 .95 .94

SRMR: Standardized Root Mean Square Residual;

RMSEA: Root Mean Square Error of Approximation;

CFI: Comparative Fit Index; TLI: Tucke

-Lewis Index

3.3 Structural Model

Causal relationships were evaluated in the structural

model using the open-source lavaan package

available in R, which was developed by Roosseel

(2012). Thus, Table 5 and Figure 3 present the output

generated for the proposed model.

Table 5: Hypothesis testing.

Hypothesis β values Status

H1: Subjective Norm →

Perceived usefulness

0.517*** Accepted

H2: Subjective Norm →

Attitude

0.014

Not

orte

H3: Subjective Norm →

Perceived self-efficac

0.279*** Accepted

H4: Facilitating conditions

→ Attitude

-0.063

Not

orte

H5: Facilitating conditions

→ Perceived self-

efficac

0.172** Accepted

H6: Perceived self-efficacy

→ Perceived usefulness

0.419*** Accepted

H7: Perceived usefulness →

Attitude

0.889*** Accepted

H8: Perceived self-efficacy

→ Attitude

0.005

Not

supporte

H9: Perceived self-efficacy

→ Current Use

0.270*** Accepted

H10: Attitude → Current Use 0.571*** Accepted

NOTE: significant at: * p < 0.05; ** p < 0.01; *** p <

0.001

Results inferred the significant acceptance of

seven hypothesized relationships:

 The data supported both H1 and H3, which

predicted that the subjective norm would

positively influence the teachers’ perceived

usefulness of technology and their perceived self-

Design and Validation of an Emerging Educational Technologies Acceptance and Integration Questionnaire for Teachers

529

efficacy. These results are in line with other works

(Abdullah and Ward, 2016; Scherer et al., 2019).

 With respect to H6 and H9, the assumptions that

perceived self-efficacy would influence perceived

usefulness and current use of technology were

individually confirmed.

 The relationship between facilitating conditions

and perceived self-efficacy (H5) was also

corroborated by the data. In this case, perceptions

of possible barriers that are related to external

conditions or availability of organizational and

technical resources are linked to beliefs about the

degree to which a teacher can perform teaching

tasks with technology.

 In the case of H7, the results supported the

expectation that perceived usefulness would

positively influence the teachers’ attitude toward

the use of educational technologies. This finding

agrees with other studies (Venkatesh et al., 2003;

Scherer et al., 2015) that emphasize the

importance of teachers’ perceptions for user

attitudes and use intentions toward technology.

 Finally, H10, which predicted that teachers’

attitude positively influences their use of

technological resources, was also supported. Once

again, this finding is consistent with other studies

(Nistor and Heymann, 2010; Scherer et al., 2018).

NOTE: significant at: ** p < 0.01; *** p < 0.001

Figure 3: The results for the suggested model.

Surprisingly, behavioural intention (attitude)

toward the use of technological resources was not

significantly influenced by facilitating conditions

(H4), perceived self-efficacy (H8), or subjective

norm (H2). In addition, the negative value of the path

coefficient between facilitating conditions and

attitude (H4) implies that teachers’ attitude toward the

use of educational technologies decreases with higher

perceptions that using technology will be either free

of effort or involve a minimum of effort, which seems

counterintuitive. These results could be explained

because data were collected after COVID-19

outbreak so there was not an option to choose whether

to use of educational technologies for teachers’

pedagogical activities or not.

4 CONCLUSIONS

The main objective of this study was to investigate

the validity of a questionnaire designed to measure

the teachers’ acceptance and integration of emerging

educational technologies. Also, the study examined

the factors influencing teachers’ attitude toward the

use of educational technologies during their activities

and their integration. Specifically, this work

examined the subjective norm, facilitating conditions,

perceived self-efficacy for technology use, perceived

usefulness of integration technology, attitude, and

technology use.

Subjective norm and perceived self-efficacy are

important predictors of perceived usefulness (H1 and

H6). Although the effects vary across studies, there is

agreement on these results. In the present work, these

variables explained 60% of variance (R

) in perceived

usefulness. Also, perceived usefulness and attitude

seem to be critical factors for user attitude toward

technology (H7) and current use (H10), respectively.

In addition to attitude, perceived self-efficacy seems

to play a relevant role of teachers’ use of

technological resources during their teaching activity

(H9). Overall, about 77% of variance in attitude and

56.3% of variance in current use were explained

within the proposed model. These results suggest the

importance of planning teachers' training programs

which focus on improving the perceived usefulness as

well as enhancing teachers’ self-efficacy in using

technology.

Although further analyses and refinement of the

tool are planned in the future, the findings presented

in this work suggest that the questionnaire effectively

measures educational technologies acceptance and

use in non-university teachers.

As with most educational research, this study has

certain limitations to address and improve in future

research. First, the research was conducted in a region

with a specific context and, therefore, the results

could not be generalized on other regions or countries

which have different contexts. In future, the proposed

model will be strengthened by considering mediating

and moderating variables. Also, the research model

will be used and extended to check the acceptance and

integration of technology in a variety of different

domains (e.g., face-to-face universities, e-health,

etc.).

ATTITUDE

CURRENT

USE

SUBJECTIVE

NORM

FACILITATING

CONDITIONS

PERCEIVED

USEFULNESS

0.517***

0.014

0.279***

-0.063

0.172**

0.419***

0.889***

0.005

0.270***

0.571***

PERCEIVED

SELF-EFFICACY

0.492***

CSEDU 2021 - 13th International Conference on Computer Supported Education

530

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APPENDIX

Emerging Educational Technologies Acceptance

and Integration Questionnaire

Table 6: Questions used to measure the subjective norm.

Label Ite

SN1

In general, families demand the use of

technolo

in learnin

rocesses.

SN2

My students expect me to use ICT in the

teaching and learning process.

SN3

In general, teachers in my department consider

it important to use ICT in the teaching and

learnin

rocesses.

Table 7: Questions used to measure the facilitating

conditions.

Label Ite

FC1

In my opinion, the necessary technical

resources to facilitate the use of ICT in

educational

rocesses are available.

FC2

In my institution, the availability of

classrooms and spaces with technological

resources (computers, tablets, digital screens,

etc.) is very suitable for the development of

ital com

etence.

FC3

In general, the Educational Authority provides

the necessary resources and support (training,

advice, etc.) to make effective the use of ICT

in educational

rocesses.

FC4

During my teaching activity, I find it easy to

use ICT spaces (computer room, classrooms

with digital screens, etc.) and ICT resources

(educational platforms, free access to

websites, YouTube, etc.

)

FC5

In my opinion, I have a good service and

com

uter su

ort when I need it.

Table 8: Questions used to measure the perceived

usefulness.

Label Ite

PU1

Technological resources are necessary to

develo

teachin

activities.

PU2

Mobile devices are useful to me and reduce

my working time.

PU3

ICTs help me to facilitate the expected

learning of my students.

PU4

I am sure that ICTs affect students’

motivational aspects such as interest,

satisfaction and curiosity.

PU5

ICTs allow me to attend to the diversity of the

students more effectivel

PU6

As a teacher I enjoy the use of ICT in the

teachin

rocess.

PU7

Technologies allow me to develop

rofessionall

PU8

I find satisfaction and feelings of well-being

with the use of ICT in teaching and learning

rocesses.

Table 9: Questions used to measure the perceived self-

efficacy.

Label Ite

PSE1 I

elieve that I have

ood di

ital com

etence.

PSE2

I have the necessary knowledge to locate

relevant information and transform it into

knowled

e throu

h ICT.

PSE3

I have the necessary knowledge to share and

collaborate throu

h di

ital media.

PSE4

I know the main aspects related to security,

digital identity and data protection.

PSE5

I consider myself competent to create digital

content for m

students.

PSE6

Generally, I am able to solve technical

roblems throu

h di

ital means.

Table 10: Questions used to measure the attitude and use.

Label Ite

AT1

I intend to use technology in teaching and

learnin

rocesses in the short/medium term.

AT2

I think it is a good idea to use technology in

my teaching activities.

AT3

Being able to use technology to improve the

quality of learning is important to me.

AT4

I am willing to use new technologies for the

different teaching and tutoring tasks with my

students.

USE1

I use ICT for the general dynamics of my

classes.

USE2

Approximate number of hours of technology

use

er da

in m

classroom.

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