https://doi.org/10.37815/rte.v37n1.1310
Original articles
Pilotaje,
Validación y Definición de un Instrumento de Autopercepción para las
Competencias Digitales de Docentes Universitarios en la Enseñanza del Idio=
ma
Inglés en Costa Rica
Mario
Esteban Alpizar RodrÃguez1 https://orcid.org/0000-0003-0254-56=
80
=
1Uni=
versidad
Sent:Â Â Â Â Â 2025/04/14
Accepted:Â Â Â Â Â Â 2025/07=
/18
Published: Â Â Â Â Â 2025/12=
/15Â
                     Â
Abstract
This study aimed to develop, validate, and
define a self-perception instrument to assess the digital competencies of
university teachers in English language teaching in Costa Rica. Based on
previous research hand theoretical frameworks on digital competencies, a
questionnaire was constructed, focusing on key dimensions such as digital
literacy, ICT integration in teaching, and teacher appropriation and
commitment. The methodology employed a quantitative approach with a
correlational and cross-sectional design, utilizing statistical techniques,
including Confirmatory Factor Analysis (CFA), Classical Test Theory (CTT) a=
nd
Item Response Theory (IRT), to evaluate the instrument's validity, reliabil=
ity
and objectivity. The results enabled informed decision-making and the
definition of the most appropriate instrument for application to university
English language teachers at the national level.
=
Keywords: D=
igital
competencies, teacher competencies, didactic resource use, technological
competencies, Information and Communication Technologies.
Summary: Introductio=
n, Methodology, Results and Discussion, Conclusions and recommendations. How to cite: Alpizar, M.=
(2025).
Piloting, Validation, and Definition of a Self-Perception Instrument f=
or
University Teachers' Digital Competencies in English Language Teaching=
in
Costa Rica. Revista Tecnológica - Espol, 37(2), 14-35. https://rte.espol.edu.ec/index.php/tecnologica/article/view/1310=
Resumen
Este estudio tuvo como objetivo
desarrollar, validar y definir un instrumento de autopercepción para evalu=
ar
las competencias digitales de los docentes universitarios en la enseñanza =
del
idioma inglés en Costa Rica. Basado en investigaciones previas, modelos y
marcos teóricos sobre competencias digitales, se diseñó y construyó un
cuestionario enfocado en dimensiones y subdimensiones claves como la
alfabetización digital, la integración de las TIC en la enseñanza y la
apropiación y el compromiso docente. La metodologÃa siguió un enfoque
cuantitativo con un diseño correlacional y transversal, utilizando técnic=
as
estadÃsticas como el Análisis Factorial Confirmatorio (AFC), la TeorÃa C=
lásica
del Test (TCT) y la TeorÃa de Respuesta al Ãtem (TRI), para evaluar la va=
lidez,
confiabilidad y objetividad del instrumento. Los resultados permitieron una
toma de decisiones fiables e informadas y la definición del instrumento mÃ=
¡s
adecuado, asà como sus dimensiones y competencias digital, para su aplicac=
ión a
docentes universitarios de inglés a nivel nacional.
Introduction
T=
his
article presents the development, validation, and definition of a
self-perception instrument tailored to assess the digital competencies of
university-level English language teachers in Costa Rica. It is built upon =
two
previous works by the author: a systematic review of instruments in this fi=
eld
(Alpizar, 2024b) and a postgraduate thesis on the relationship between digi=
tal
competencies and the didactic use of ICT in English teaching (Alpizar, 2024=
a).
<= o:p>
W=
ithin the
vast diversity of models, frameworks, and profiles on teachers' digital
competencies, existing references provide general guidelines for digital
teaching competencies but lack a contextual focus on English language teach=
ing
and the practical integration of ICT. Thus, this research aims to generate a
more context-specific tool that reflects both self-perceived proficiency and
the pedagogical application of ICT by English language teachers.
<= o:p>
I=
n this
article the following acronyms are employed:
<= o:p>
Table 123
Variables,
conceptualization, instrumentalization, and operationalization
|
       Variables                   =
Definition     |
Teachers' digital competencies |
Didactic use of ICT in English teaching courses |
|
Conceptual |
Based on the type of responses, it is
classified as an ordinal qualitative variable, since its items are
prescriptive, with restricted responses, ordered from lowest to highest in
terms of proficiency level according to a Likert scale. It is worth noting
that this is a latent variable (construct), as digital teaching competenc=
ies
are based on a person's cognitive traits. Additionally, it is considered =
an
index variable, as it has been theorized that competencies comprise skill=
s,
knowledge, attitudes, practices, emotions, and other aspects, which gener=
ate
a series of manifestations that serve as necessary indicators to assess
digital teaching competencies. |
Based on the type of responses, it is
classified as an ordinal qualitative variable, since its items are
prescriptive, with restricted responses, ordered from lowest to highest in
terms of frequency of use according to a Likert scale. It is a latent var=
iable
(construct) because the didactic use of ICT is based on the frequency of
technology use in the classroom. Furthermore, it is considered an index
variable, as it has been theorized that competencies generate a series of
manifestations that serve as the necessary indicators to assess the didac=
tic
use of ICT. |
|
Instrumental |
The study measured teachers' perception=
s of
their digital competencies in English language teaching and identified the
competencies they possess, as outlined in the proposed theoretical framew=
ork.
|
Through the instrument, the relationship
between digital competencies of English language teaching staff and the u=
se
of technological resources, as well as the organization of didactic work =
with
ICT, is described. Depending on the level of digital competencies teachers
possess, the didactic use of ICT may be enhanced or not. |
|
Operational |
The first part of the questionnaire, us=
ing
a three-point Likert scale, focused explicitly on teachers' digital
competencies. It consisted of 12 items and aimed to assess the digital
competencies of English language teaching staff. The questionnaire was st=
ructured
into dimensions and sub-dimensions of different digital competencies, with
the following distribution: Digital Literacy (6 items), Teacher Integrati=
on
of ICT (2 items), and Teacher Appropriation and Commitment (2 items). |
The second pa=
rt
of the questionnaire aimed to gather descriptions from English language
teaching staff regarding elements such as ICT use, lesson planning phases,
didactic strategies, and instructional materials for teaching English thr=
ough
ICT. The dimensions considered were: Digital Literacy (17 items), Teacher
Integration of ICT (2 items), and Teacher Appropriation and Commitment (1
item). |
Source: <=
span
lang=3DEN-GB style=3D'font-size:10.0pt;mso-ansi-language:EN-GB;mso-bidi-fon=
t-weight:
bold;mso-bidi-font-style:italic'>Alpizar, M. (2024a)
<= o:p>
A=
ccording
to Wallace (1998), although the construction and implementation of a
questionnaire require time, once this stage is completed, it allows for its
application to a large population, as in this case, and enables the efficie=
nt
and effective extraction of the necessary results.
T=
his
technique selection was sufficient to support the collection of the necessa=
ry
information, especially since it aligns with the theoretical and methodolog=
ical
approach. The measurement object allowed for the specification of construct
indicators; the items correspond to each indicator defined by the dimension=
s of
digital teaching competencies and the didactic use of ICT, as per Delgado
(2014).
<= o:p>
Dete=
rmination
of the validity, reliability, and objectivity of the instrument
A=
ccording
to Hernández et al. (2014), Delgado (2014), and Hernández and Mendoza (20=
18),
reliability refers to the consistency with which repeated application of an
instrument to the same individual or object produces identical results. This
was demonstrated through internal consistency and coherence measures using
Cronbach's alpha coefficient.
<= o:p>
V=
alidity
refers to the extent to which an instrument truly measures the intended
variable. Validity can be demonstrated through three different types of
evidence: content-related, criterion-related, and construct-related evidenc=
e.
For this research, all three types were selected.
<= o:p>
C=
ontent
validity was established first, as it reflects how well the instrument cove=
rs
the specific content domain being measured, according to Delgado (2014) and
Hernández et al. (2014). The questionnaire included dimensions and
subdimensions of digital teaching competencies based on theoretical
foundations. For this stage, four
university professors specializing in English language teaching and Educati=
onal
Technology validated the instrument, providing feedback on item wording,
application inclusion, and scale reduction.
<= o:p>
F=
ollowing
Rojas et al. (2022), a pilot test was conducted using items approved by
experts, administered to 125 English teachers from elementary, secondary, a=
nd
open education levels.
<= o:p>
C=
onstruct
validity was established through a Confirmatory Factor Analysis (CFA) of the
collected data, which examined the instrument's internal structure. Criteri=
on
validity was established by comparing the instrument with external criteria
measuring the same construct, using predictive validity in this case.
<= o:p>
A=
dditionally,
construct validity was considered from a scientific perspective, referring =
to
how well the instrument represents and measures a theoretical concept, as w=
ell
as its operationalization, and three stages were implemented.
<= o:p>
<=
span
style=3D'mso-spacerun:yes'>Â In the first stage, a theoretical frame=
work
was developed through a literature review, establishing the relationship
between digital teaching competencies and ICT didactic use.
<= o:p>
T=
he second
stage included the statistical analysis of concept associations through a
correlation design, supplemented by expert validation.
<= o:p>
T=
he next
stage involved interpreting empirical evidence regarding the construct's
validity. The instrument successfully measured the relationship between dig=
ital
teaching competencies and ICT didactic use in English language teaching,
achieving full validity through these complementary approaches.
<= o:p>
F=
inally,
objectivity, the extent to which the instrument is free from researcher bias
and tendencies in its administration, scoring, and interpretation, was ensu=
red
by standardizing administration procedures, maintaining consistent instruct=
ions
and conditions for all participants, and applying an unbiased analysis meth=
od
to interpret the results.
<= o:p>
Resu=
lts and
Discussion
Preliminary Pilot Testing of the Instrument
To appropriately define and justify the
instrument, a preliminary pilot test was conducted using items approved by
experts with a sample of 125 English teachers from elementary, secondary, a=
nd
open education settings. With the collected data, a Confirmatory
Factor Analysis (CFA) was performed, along with other necessary tests
detailed below. This process enabled the justification and strengthening of=
the
instrument's internal structure, ensuring that it effectively measured what=
it
was intended to measure.
Confirmatory Factor Analysis (CFA)
The items
associated with each dimension were assigned to the model to ensure the best
possible representation. It is important to note that, for the purpose of
applying the analysis, Spanish acronyms were used for the stipulated dimens=
ions
to facilitate their entry into the corresponding software. These acronyms a=
re
as follows: Digital Literacy (AD), Teacher Integration of ICT (ID), and Teacher Appropriation and Commitment (ACD).
The results obtained are presented below:
Figure 1
Factor Loadings by Dimensions
Source: Prepared based on data
obtained from the preliminary pilot test
It is important to highlight that, for ea=
ch
dimension, the factor loadings associated with each item were expected to f=
all
within similar value ranges. The analysis revealed that:
· =
In
Dimension (AD), item values ranged between 0.54 and 0.77.
· =
In
Dimension (ID), item values ranged between 0.48 and 0.70.
· = In Dimension (ACD), most of the eight items had values between 0.50 and 0.75.<= o:p>
However, two items deviated significantly=
:
· =
Item
22 (use of spreadsheets): factor loading of 0.39.
· =
Item
27 (use of local storage devices): factor loading of 0.44.
Both items showed lower factor loadings
compared to the others, preliminarily indicating that these items did not a=
lign
well with their assigned dimension.
Based on the previous figure and the
Confirmatory Factor Analysis (CFA) results, the following comparative tests
were conducted:
a) A comparison was made to det=
ermine
whether the proposed model fits better than a null model (without variables=
),
yielding a p-value of 0.000, which indicates a significantly better fit than
the null model.
b) A comparison was made to det=
ermine
whether the proposed model fits better than a baseline model (a theoretical
model that covaries the dimensions), also yielding a p-value of 0.000,
indicating a significantly better fit than the baseline model.
Both results indicated that the proposed model is superior to simpler
theoretical models. However, additional fit measures were examined to assess
the model's accuracy:
Table 4=
Test/Measure and Value
|
Value |
|
|
Comparative Fit Index (CFI) |
0,731 |
|
Tucker-Lewis Index (TLI) |
0,709 |
|
Source: Prepared based=
on
collected data. |
|
Both fit measures yielded values between 0 and 1, where the closer t=
he
value is to 1, the better the model fit. Values around 0.9 are considered
acceptable. However, in both cases, it was observed that the evaluated model
did not provide an optimal fit to the observed data, prompting further anal=
ysis
of the data.
The initial step involved examining eigenvalues to determine the opt=
imal
number of dimensions, with results as follows:
Figure
2
Sedimentation Plot
Source: Prepared based on
eigenvalues
The graphical analysis showed up to 7 pos=
sible
dimensions, but the optimal and recommended solution was to retain two fact=
ors,
as indicated in the graph legend. This revealed initial issues in dimension
detection.
Given the research objective to optimize =
the
three predefined dimensions, these were used as reference points for intern=
al
analysis to improve overall consistency. The analysis incorporated both
Classical Test Theory (CTT) and Item Response Theory (IRT), as indicated by
MartÃnez, Hernández, and Hernández (2014).
Â
Dimension: Digital Literacy
Table 5=
Dimension: Digital = Literacy (CTT)
|
Item Mean |
Alpha if Item is Deleted |
|
|
Item 1 |
|
|
|
Item 2 |
|
|
|
Item 3 |
|
|
|
Item 4 |
|
|
|
Item 5 |
|
|
|
Item 6 |
|
|
|
Item 7 |
|
|
|
Item 8 |
|
|
|
Item 9 |
|
|
|
Item 10 |
|
|
Source: Prepared based on the results of the items
According to IRT, as stated by MartÃnez, Hernández, and Hernández
(2014), when dealing with polytomous models (i.e., models with items that h=
ave
more than two response categories), Partial Credit Models should be employe=
d.
Initially, it is necessary to determine which model best fits the dimension.
The hypothesis test indicates that the two-parameter Rasch model best expla=
ins
the dimension, as shown below:
Table 6=
Dimension: Digital Literacy (Rasch model)
|
LRT |
Degrees of Freedom |
p-value |
|
|
Rasch - 1Parameter |
|
|
|
|
1Parameter - 2Paramet=
er |
|
|
|
Source: Prepared based on results for the dimension
Once the model was identified, the aim was to ensure that the cut-off
points between the responses of the items were at a similar distance and not
overly skewed toward the positive or negative side of the measured competen=
cy.
Therefore, in general, the items are presented appropriately, as shown belo=
w:
Table 7
Dimension: Digital Literacy (Categories and Discrimination)
Source: Prepared based on the results of the items
The items, in general, appeared optimal, but there are six that are
important to highlight as follows:
Figure
3
Items 2, 6, 7, 8, 9, and 10 discrimination
Source: Prepared based on the
results of the items
The previous figure highlights:
Dimension: Teacher Integration of ICT
According
to Classical Test Theory (CTT)<=
/span>, the Cronbach's Alpha coeff=
icient
for this dimension was 0.8478, which is considered quite optimal. Then, whe=
n analyzing the impact of removing any item, it was obs=
erved
that the coefficient decreased in each case. Therefore, according to CTT, no
item should be removed.
Table 8
Dimension: Teacher Integration of ICT (CTT)
|
Item Mean |
Alpha if Item is Deleted |
|
|
Item 11 |
|
|
|
Item 12 |
|
|
|
Item 13 |
|
|
|
Item 14 |
|
|
|
Item 15 |
|
|
|
Item 16 |
|
|
|
Item 17 |
|
|
|
Item 18 |
|
|
|
Item 19 |
|
|
|
Item 20 |
|
|
Source: Prepared based on the results of the items
According to the Item Response Theory (IRT) approach, since these are
polytomous models, Partial Credit Models must be used. To determine the best
fit for this dimension, the following results were obtained:
Table 9
Dimension: Teacher Integration of ICT (Rasch model)
|
LRT |
Degrees of Freedom |
p-value |
|
|
Rasch - 1Parameter |
|
|
|
Source:<=
/i> Prepared based =
on
results for the dimension
Since items 19 and 20 did no=
t show
diversity in the responses obtained, it was not possible to present the tab=
le
with category intersections. Therefore, a direct analysis of the item
characteristic curve was conducted.
The following observations w=
ere made
from the item characteristic curves:
· =
Item
11: Response category 2 did not contribute, while categories 3 and 4
overlapped.
· =
Item
12: Item discrimination based on skill level was very low in the negative s=
kill
range, and categories 2 and 4 contributed almost nothing.
· =
Item
13: Categories 2 and 4 did not contribute at all, and category 3 contributed
only slightly below the neutral skill level.
· =
Item
14: This item did not discriminate well because category 5 encompassed both
neutral and high skill levels, while categories 3 and 4 did not help determ=
ine
low skill levels.
· =
Item
15: Category 2 did not contribute, and categories 3 and 4 did not add
significantly.
· =
Item
16: Categories 2 and 4 did not contribute, category 3 contributed very litt=
le,
and category 5 captured both neutral and positive skill levels.
· =
Item
17: The discrimination from categories 3 and 4 was almost non-existent, whi=
le
categories 1 and 2 effectively distinguished low skill levels, and category=
5
identified neutral and positive skill levels.
· =
Item
18: Categories 2 and 4 did not contribute. Category 3 provided slight
discrimination at neutral skill levels, while categories 1 and 5 discrimina=
ted
at negative and positive skill levels, respectively.
· =
Item
19: No one selected category 1, which shifted the graph values by one categ=
ory.
Categories 3 and 4 did not contribute; category 2 only discriminated at low
skill levels; and category 5 captured neutral and high skill levels.
· =
Item
20: No one selected category 1, causing the graph values to shift by one
category. Category 3 did not contribute; category 2 discriminated at low sk=
ill
levels; category 4 slightly discriminated at low levels; and category 5
distinguished between neutral and high skill levels.
Figure
4
Items 11, 12, 13, 1= 4, 15, 16, 17, 18, 19, and 20 discrimination
Source: Prepared based on the
results of the items
Dimension: Teacher Appropriation and Commitment
According to Classical Test =
Theory
(CTT), the Cronbach's Alpha for this dimension was 0.8250, which is conside=
red
quite optimal. When applying CTT, it was found that removing Item 22 (relat=
ed
to the use of spreadsheets) would increase internal consistency to 0.8282. =
Upon
re-evaluating the consistency of the dimension without this item, no further
items were found to require elimination, as doing so would decrease internal
consistency. It is worth noting that Item 27 (focused on the use of differe=
nt
local storage devices) showed a slight decrease in Cronbach's Alpha if remo=
ved,
with a value of exactly 0.8270.
Considering the Item Response Theory (IRT), since these are polytomo=
us
models, Partial Credit Models must be used. To determine the best fit for t=
his
dimension, the following results were obtained:
Table 10
Dimension: Teacher Appropriation and Commitment (Rasch model)=
|
Models |
LRT |
Degrees of Freedom |
p-value |
|
Rasch - 1Parameter |
|
|
|
Source:<=
/i> Prepared based=
on
results for the dimension
Since Item 26 did not show diversity in
responses, it is not possible to present the table with the category
intersections. Therefore, a direct analysis of the item characteristic curve
was conducted.
It is essential to note that, in general =
terms,
category 5 in Items 21 to 27 encompassed both positive and neutral skill
levels. Below is a detailed breakdown by item:
· =
Item
21: Category 1 indicated low skill levels, category 2 contributed nothing, =
and
categories 3 and 4 also reflected low skill levels.
· =
Item
22: Category 1 demonstrated low skill levels, while categories 2 and 3 also
showed low levels closer to neutral, and category 4 contributed nothing.
· =
Item
23: Categories 1 and 3 contributed nothing, whereas categories 2 and 4
indicated low skill levels.
· =
Item
24: Category 1 reflected low skill levels, category 2 contributed nothing, =
and
categories 3 and 4 slightly indicated low skill levels.
· =
Item
25: Category 1 showed low skill levels and discriminated well; categories 3=
and
4 slightly reflected low skill levels near neutral. Category 2 contributed
nothing.
· =
Item
26: Since no one selected category 1, the graph values shifted by one categ=
ory.
Categories 2 and 4 contributed nothing; category 3 showed low skill levels,=
and
category 5 increased rapidly.
· =
Item
27: Correct discrimination was observed at low skill levels in category 1,
followed by category 3, which discriminated well near neutral levels.
Categories 2 and 4 contributed nothing.
· = Item 28: All five categories contributed some level of discrimination. Categorie= s 1 through 3 reflected low skill levels; category 4 indicated a level very clo= se to neutral; and category 5 increased rapidly across positive skill levels.<= o:p>
· =
Item
29: Category 1 declined rapidly in low skill levels; category 2 contributed
nothing; categories 3 and 4 spanned low and neutral levels; and category 5
increased rapidly at positive skill levels.
· =
Item
30: Category 1 declined slowly across low skill levels, covering a signific=
ant
range; category 2 contributed nothing; categories 3 and 4 reflected neutral
levels; and category 5 increased rapidly at positive skill levels.
Figure 5
Items 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30 discrimination
Source: Prepared based on the
results of the items
Conclusions and recommendations
Based on the results of the tests performed, the following decisions
were made regarding the final version of the instrument:
•&=
nbsp;
To achieve more appropriate and accurate item functioning and behavi=
our,
considering that the curves must have similar shapes, with the lowest categ=
ory
always on the left and the highest on the right, the scale was reduced from=
a
five-point to a three-point scale to fit better. This followed the same
approach used for the first dimension, which performed best, unlike the oth=
er
two dimensions, as it consisted of a three-point scale. This decision was
supported by previous analyses, which showed that response categories 2, 3,=
and
4 in most items did not contribute, making it more reasonable to reduce the
scale. The same applied to items 21 to 30, where response categories 2 and 4
did not contribute.
•&=
nbsp;
Items that were weak in measuring their intended constructs were
reworded to strengthen their clarity and precision.
•&=
nbsp;
Items 22, focused on the use of spreadsheets, and item 27, related to
the use of different local storage devices, were eliminated, as their remov=
al
increased internal consistency according to Cronbach's Alpha.
•&=
nbsp;
Following the recommendations of the advisors, items 23 and 24, rela=
ted
to using audio, podcasts, and videos to expose students to the English
language, were divided into two separate items each: one focused on use and
another on creation.
•&=
nbsp;
Furthermore, it was suggested to incorporate two new items: one rela=
ted
to basic knowledge of network functionality and another addressing the use =
of
different operating systems.
The validated instrument demonstrated strong psychometric properties=
and
contextual relevance, effectively assessing the self-perceived digital
competencies of English language university teachers and their frequency of=
ICT
integration in English language teaching.
Its structure enables decision-makers to use it as a diagnostic and
planning tool within higher education institutions. Beyond academic
applications, it can support professional development strategies, teacher
evaluations, and resource allocation at both the institutional and governme=
ntal
levels.
The adjustments made based on pilot testing—such as reducing the L=
ikert
scale to three points and removing items with weak discriminative
power—enhanced the instrument's overall coherence, clarity, and reliabili=
ty.
To ensure its long-term utility, it is recommended that the instrume=
nt
be applied periodically and adapted as digital technologies and teaching
modalities evolve.
Unlike internationally recognized frameworks such as DIGCOMP or the
INTEF Common Framework for Digital Competence, the instrument presented in =
this
study was specifically designed for the Costa Rican higher education contex=
t.
It focuses on English language teaching, incorporating dimensions such as
digital literacy, didactic use of ICT, and self-reflection. This specificity
provides universities and policymakers with a more practical tool for
diagnosis, evaluation, and planning professional development.
Moreover, it is suggested that educational institutions integrate th=
is
tool into teacher training programs to promote digital inclusion and didact=
ic
innovation in English language instruction. Finally, fostering collaborative
environments where teachers share their ICT-based practices will further
strengthen the role of technology in language education.
<= o:p>
Acknowledgeme=
nts
T=
he author
thanks Universidad de Costa Rica, Universidad Nacional, Universidad Estatal=
a
Distancia, Universidad Americana, among others, for their support in data
collection and academic endorsement of the study.
<= o:p>
T=
he author
declare that no artificial intelligence tools have been used for the
preparation of this article.
<= o:p>
<= o:p>
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C. y Baptista, P. (2014). MetodologÃa de la investigación, Sexta edición.
México D.F., México. McGraw Hill. ISBN: 978-1-4562-2396-0
Hernández, R., M=
endoza,
C. (2018). MetodologÃa de la investigación: Las Rutas Cuantitativa, Cuali=
tativa
y Mixta. México D.F., México. McGraw Hill. ISBN: 978-1-4562-6096-5
INTEF, (2017). Ma=
rco
Común de Competencia Digital Docente. Instituto Nacional de TecnologÃa
Educativa y de Formación del Profesorado. Ministerio de Educación, Cultur=
a y
Deporte, Gobierno de España. Recuperado de: http://educalab.es/mwginternal/de=
5fs23hu73ds/progress?id=3Dc2dGknKSQlvgTC8LuYf_aldw51tRLbH-Gh7T02SJSBs,&=
dl
Kerlinger, F. y L=
ee, H.
(2000). Investigación del Comportamiento, Métodos de Investigación en Ci=
encias
Sociales (4a edición). McGraw-Hill / Interamericana de México. ISBN:
9789701030707
MartÃnez, Herná=
ndez y
Hernández, (2014). PsicometrÃa (1a edición). Alianza Editorial. ISBN:
978-84-206-8859-6
Rojas, L., Furlan, L., Sánchez, J. y Rojas, G. (2022). Construc=
ción y
validación de la Escala de Afrontamiento de la Ansiedad durante los Exáme=
nes. Ansiedad y estrés, 2(28), 81-90.
<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;mso-ansi-language:EN-GB'>Wallace, M.=
(1998).
Action Research for Language Teachers (1a edición).
Cambridge University Press. ISBN: 0521555353
Appendices
Annex No.1 – Likert Scale Questionnaire=
Used
for Preliminary Pilot
T=
his
instrument arises from research on the topic of teachers' digital competenc=
ies
and the didactic use of ICT by English language teachers. This instrument w=
ill
be used solely for academic purposes.
Obje=
ctive:
T=
he purpose
of this instrument is to identify the current digital competencies of Engli=
sh
language teachers and their perceptions of the didactic use of ICT in the
courses they teach.
<= o:p>
P=
lease read
and complete the following questionnaire. Select one option from the ones
provided according to the scale—based on your level of knowledge in the f=
irst
part, and frequency of use in the second part. The scale is structured in t=
wo
sections: the first focused on teachers' digital competencies, and the seco=
nd
on the didactic use and integration of ICT.
<= o:p>
Dear=
Teacher,
Y=
ou will be
asked a series of questions regarding your perception of your digital
competencies in English language teaching. The questionnaire is anonymous a=
nd
confidential. It consists of 30 items and will take no more than five minut=
es
of your time. You are kindly asked to reflect on each question and answer w=
ith
complete honesty. Please remember to mark only one answer per question.
<= o:p>
I=
f you have
any questions before completing the instrument, you can contact me at the
following email address: mario.alpizarrodriguez@ucr.ac.cr
<= o:p>
Info=
rmed
Consent:
I=
f you feel
that you have received the necessary information and had the opportunity to=
ask
questions—which have been answered appropriately—please indicate that y=
ou
understand the purpose of the project, the conditions of your participation,
and that you consent to participate as a research subject in this study:
<= o:p>
Y=
ES   NO
<= o:p>
T=
hank you
very much.
<= o:p>
Part One: Basic Information:
Gender: M:   F:   Other:
Age: ________
Years of experience: ________
Part Two:
Perception of Your Digital Teaching Competencies
|
Dimension |
Subdimension=
|
Item |
Statements |
Do Not Posse=
ss |
Beginner |
Advanced |
|
Digital Literacy |
Information Literacy |
1 |
I have the knowledge to recover deleted,
corrupted, or inaccessible files, as well as those with formatting errors=
. |
|
|
|
|
Communication and Collaboration |
2 |
I am familiar with netiquette rules. |
|
|
|
|
|
3 |
I have knowledge about students' digital iden=
tity
and profile (e.g., online citizenship, participation through official
channels, netiquette, and digital identity management). |
|
|
|
||
|
Creation and Manipulation of Digital Content<= o:p> |
4 |
I am able to verify different types of licens=
es
for publishing content (e.g., copyright, copyleft, Creative Commons). |
|
|
|
|
|
Technological Devices |
5 |
I have basic technical knowledge as a teacher
regarding networks, connections, software, hardware, etc., that do not
require immediate intervention from IT support. |
|
|
|
|
|
6 |
I have the knowledge to protect my devices
against threats such as viruses, malware, among others. |
|
|
|
||
|
ICT Integration in Teaching |
Curricular, Pedagogical, and Didactic Field |
7 |
I have knowledge of designing educational
environments supported by ICT. |
|
|
|
|
8 |
I am familiar with methodologies for English
teaching that involve the use of ICT. |
|
|
|
||
|
Appropriation and Teacher Commitment |
9 |
I am able to identify technological needs (e.=
g.,
equipment, ICT knowledge, connectivity, etc.) |
|
|
|
|
|
10<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I am aware of my areas for improvement and se=
ek
ongoing training options for the didactic use of ICT. |
|
|
|
Part
Three: Frequency of Didactic Use of ICT
|
Dimension |
Subdimension=
|
Item |
Statements |
Never |
Almost Never |
Sometimes |
Usually |
Always |
|
Digital Literacy |
Information Literacy |
11<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
Do I use different browsers to search and fil=
ter
information, data, and digital content? |
|
|
|
|
|
|
12<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use online platforms to store and manage sh=
ared
files and content (e.g., Drive, Dropbox, Office 365, iCloud, among others=
). |
|
|
|
|
|
||
|
Communication and Collaboration |
13<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use websites, blogs, and wikis to support my
classes. |
|
|
|
|
|
|
|
14<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use online communication tools (e.g., email,
forums, instant messaging, chats, videoconferencing, among others). |
|
|
|
|
|
||
|
15<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use learning management systems (e.g.,
SharePoint, Moodle, Blackboard, Schoology, Google Classroom). |
|
|
|
|
|
||
|
16<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use social media and learning communities to
share English language content (e.g., Facebook, Twitter, Instagram, Googl=
e+,
among others). |
|
|
|
|
|
||
|
17<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I apply basic netiquette rules, including |
|
|
|
|
|
||
|
18<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I manage spaces to help students develop their
digital identity and profile (e.g., online citizenship, participation thr=
ough
official channels, netiquette, digital identity management). |
|
|
|
|
|
||
|
Creation and Manipulation of Digital Content<= o:p> |
19<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use text file formats (e.g., doc, docx, txt,
pdf, among others). |
|
|
|
|
|
|
|
20<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use images as part of my didactic planning
(e.g., PNG, JPEG, BMP, GIF, PSD, TIFF, among others). |
|
|
|
|
|
||
|
21<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use presentations (e.g., PowerPoint, Canva,
Prezi) and visual designs (e.g., infographics, interactive charts, concept
maps, timelines, among others). |
|
|
|
|
|
||
|
22<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use spreadsheets (e.g., Excel, Access, among
others). |
|
|
|
|
|
||
|
23<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use audio or podcasts to expose students to=
the
English language (e.g., mp3, wav, among others). |
|
|
|
|
|
||
|
24<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use videos to expose students to the contex=
t of
the English language (e.g., MP4, AVI, MKV, Flash Player, open-source
formats). |
|
|
|
|
|
||
|
25<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use gamification tools to teach English (e.=
g.,
Kahoot, Quizziz, Quizlet, word searches, Cypher Code, among others). |
|
|
|
|
|
||
|
Technological Devices |
26<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use various digital devices to support my
teaching (laptop, PC, tablets, smartphones, among others). |
|
|
|
|
|
|
|
27<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use different local storage devices (e.g., =
USB
drives, CDs, DVDs, external hard drives, among others). |
|
|
|
|
|
||
|
ICT Integration in Teaching |
Curricular, Pedagogical, and Didactic Field |
28<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
When planning and designing my lessons, I
integrate the use of ICT by both myself and my students. |
|
|
|
|
|
|
29<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
After completing my courses, I evaluate and
receive feedback on the effectiveness of ICT-mediated learning environmen=
ts. |
|
|
|
|
|
||
|
Appropriation and Teacher Commitment |
30<=
span
lang=3DEN-GB style=3D'font-size:10.0pt;line-height:105%;mso-fareast-font-=
family:
Calibri;mso-ansi-language:EN-GB;mso-fareast-language:EN-US'> |
I use spaces to self-reflect on the use of ICT
throughout my courses. |
|
|
|
|
|
Piloting, Validation, and
Definition of a Self-Perception Instrument for University Teachers' Dig=
ital
Competencies in English Language Teaching in Costa Rica 5