INTRODUCCIÓN
Oral
production (speaking) is one of the most important skills used as the primary
means of language production in communication. Moreover, speaking is the art of
effectively expressing ideas, thoughts, and emotions with words. It is the
clear enunciation of words and phrases; it contains proper rhythm and
intonation and embeds the message in the context and audience to which it is
targeted. Speaking embodies listening skills in an effective and timely
response. Regarding education, Guebba (2021) states that speaking is a vital
part of the teaching and learning field.
Bonilla-Guachamín
et al., (2020) claim that technological resources play a crucial
role in facilitating knowledge acquisition and offering a wide array of
learning opportunities for students. They enable students to engage themselves
in interactive and interesting ways to understand complex ideas, provide
options for different learning styles, and open doors to unlimited information.
Integration of technology in schools helps students to be more participatory
toward learning in a more personalized way. Furthermore, technological means
are effective didactic tools that favor meaningful learning. For this
integration into teaching methodologies to be truly effective, however,
educators do need training (Duailibi et al., 2020).
In the
rapidly evolving educational environment, technology has become a real option
to engage learners in the teaching and learning field (Ghory & Ghafory,
2021). Universities worldwide increasingly engage with an online or blended
environment; therefore, educators around the world face challenges in
implementing technological resources within their pedagogical practices (Cain,
2015). University English teachers are among such educators, with a prominent
role in fostering improvement in language skills, including oral proficiency in
their students. This study regards teachers’ view of technological resources employed
in their classrooms as effective in promoting students' oral production;
however, the alternative hypothesis suggests that educators may not view these
resources as effective.
The
transition to online education has made clear the need for a deep understanding
of how technology tools impact language acquisition outcomes (Srivastava, 2020).
Studies led by Cardoso et al., (2022) have shown that technology can enhance
learners' speaking abilities when it is appropriately integrated into language
teaching. However, this perspective of university instructors of English about
the efficiency of such tools in oral development remains an underexplored area (Rio,
2020). This study, therefore, attempts to investigate the perceptions of
university English teachers on the effectiveness of technological tools in
their virtual classrooms in improving their students' oral output, which is
considered to be a gap in the current research literature.
According
to Günüç (2017) in language teaching and learning, technology integration has
played a significant role in teaching methodology, helping engage students
more. As educators try to work their way through this modern digital era, tech
integration for online language classes has widely become the focus of
attention to improve language skills. Technology integration in language
education not only brings a range of learning opportunities but also
significantly supports the development of students' linguistic competencies (Iqbal
et al., 2021). For that reason, Liesa et al., (2020) emphasized the urgent need
to train educators on the effective use of technological tools.
MATERIALS AND METHODS
The
design:
The
research design for this study is quantitative and is based on a descriptive
cross-sectional design. According to Creswell (2012), a descriptive
cross-sectional design is especially effective in the education sector to
measure, for instance, teachers' perceptions at any one particular time without
having to conduct follow-ups over long periods. The quantitative approach was
selected as the favored methodological design for this research due to its
capacity to utilize structured questionnaires for collecting numerical data
from educators concerning their perceptions, thereby facilitating statistical
analysis prior to deriving conclusions from measurable outcomes (Tarusha &
Bushi, 2024).
The
participants:
The
sample for this study was 27 English teachers from the Language Institute of
the University of the Armed Forces - ESPE, who taught in online mode during the
semester from November 2023 to April 2024. The teachers participated based on a
voluntary sampling strategy, which is commonly used with eligible participants in
quantitative studies (Murairwa, 2015).
Regarding
inclusion and exclusion criteria, in educational research, inclusion and
exclusion criteria play a significant role. The criterion is used to include or
exclude relevance in the study. Such criteria provide a frame within which the
study can be conducted, bringing consistency to the review process (Arias et al.,
2016).
On the
one hand, the selection was based on the inclusion criteria of active teaching
of English at the university level, a fact that justifies the purpose of the
research to study perceptions concerning technology use in the language
classroom. To be more concrete, within the total of 27 respondents who
participated in this study, 16 women and 8 men aged between 25 and 65 years
took part in it.
Although
there are many challenges, technology use is one of the motivating factors that
provides students with varied learning resources and reinforces language skill
development (Nahartini et al., 2018). Fortunately, the respondents’ technological
literacy levels varied from intermediate to advanced; therefore, they had
appropriate skills to make use of the technological resources during their
online English classes.
On the
other hand, two exclusion criteria were put into consideration when choosing
the subjects for this analysis. The exclusion criteria included those who at
the time were either not teaching English at the university level or had never
taught with technological resources online. This was done to ensure that only
those teachers who had a profound knowledge of the use of technology in
boosting oral communication abilities among learners would be included in this
study.
The
instrument:
The
research instrument is a closed-ended questionnaire comprising 11 questions
drawn from specific data points regarding teachers’ perceptions of the
effectiveness of technological resources in online classes for enhancing oral
production. According to Garcia-Marques and Bártolo-Ribeiro (2020), this type
of questionnaire will make it possible to have an analysis from a quantitative
approach allowing to reach the goal of the study.
The
instrument had to pass through the validation of two experts in the field of
education, specifically English language teaching, and one expert in
educational technology. Roy and Kant (2024) state that expert validation is
necessary in order to establish the content validity of the questionnaire since
experts can evaluate the relevance and appropriateness of the items. Consequently,
the content validity of the questionnaire was enhanced by guaranteeing clarity
and relevance to the research objectives, along with other contributing factors
(Yun et al., 2023).
Data
collection methods included an online administration of the pre-validated instrument
to the participants. This therefore allowed consistency and ease in collecting
data. There is evidence that in educational research studies, response rates
and accuracy have increased with the use of electronic questionnaires (Patel et
al., 2020). After data collection, the answers were analyzed through Excel,
which is a useful tool to conduct statistics analysis and obtain quantitative
data (Reiter & Matthaeus, 2000).
RESULTS
Y DISCUSSION
Descriptive
statistical methods were used to analyze data obtained from the closed-ended
questionnaire. To explain the results of the questionnaire, measures of central
tendency- arithmetic mean, and measures of dispersion-standard deviation were
calculated accordingly (Zulfiqar et al., 2019). The data was first organized in
frequency tables, which allows for better visualization to derive useful
interpretations from it. interpretation (Spriggs et al., 2018).
The
interpretation of the results was supported by the analysis of computed
descriptive measures that indicated the existence of positive attitudes
concerning the use of technological resources. (Roig-Vila et al., 2015). The
range was measured to establish the level of dispersion or standard deviation.
The variation in responses from different subjects determined the level of
consensus or dispersion of perception at a group level (Twycross & Shields,
2004).
The
results are presented in the following tables:
|
Technological
Resources
|
Frequency
of mention
|
Relative
frequency
|
Percentage
|
|
Language
Learning Apps or webpages to practice speaking
|
15
|
0,104895105
|
10%
|
|
Language
Learning Apps or webpages to practice pronunciation
|
15
|
0,104895105
|
10%
|
|
Language
Learning Apps or webpages to practice reading
|
10
|
0,06993007
|
7%
|
|
Language
Learning Apps or webpages to practice listening
|
16
|
0,111888112
|
11%
|
|
Language
Learning Games
|
17
|
0,118881119
|
12%
|
|
Podcasts
and Audio Lessons
|
14
|
0,097902098
|
10%
|
|
Apps
or online Language Assessments
|
14
|
0,097902098
|
10%
|
|
Language
Learning Blogs and Websites
|
9
|
0,062937063
|
6%
|
|
Social
media for language learning
|
17
|
0,118881119
|
12%
|
|
Educational
YouTube Channels
|
16
|
0,111888112
|
11%
|
|
Other
|
0
|
0
|
0%
|
|
I
don’t use any technological resource
|
0
|
0
|
0%
|
|
Total
|
143
|
|
100%
|
Table 1. Primary
technological resources teachers use to foster the students’ oral production.
In
table one, it is observed that the most used resources by teachers to improve
students' oral production are language learning games and social media for
language learning. They obtained 17 mentions and a percentage of 12% each. This
suggests that games and social media are very popular among teachers, probably
due to their interactivity and capacity to maintain interest and motivation in
students.
Popularity
and trend:
Interactive and multimedia technology resources, such as games and social
networks are the most frequently mentioned, indicating teachers’ preference for
dynamic and immersive learning tools.
Diversity
of resources used: Although there is a distinct bias towards
the usage of some resource types, teachers also use various other resources for
supporting different aspects of learning, such as speaking, pronunciation,
reading, and listening.
Learning
preferences: The notable frequency with which resources about
listening practice and games are referenced indicates that educators prioritize
immersion and interactive elements within their instructional methodologies
(Cruz & Torres, 2022).
Taken
together, the second-best options for improving the students' oral production
are Language Learning Applications and websites for listening practice and
Educational YouTube Channels; both options received 16 mentions, or 11% each.
On the
other hand, language learning blogs and websites are the least popular
resources educators use, represented by 6%, which is the smallest proportion
out of the choices given. Last, but not least, it is relevant to point out that
the other types of resources that teachers use during their teaching practices
weren't indicated.
|
|
Teachers’
perception
|
Mean
|
SD
|
|
1
|
It is
necessary to integrate technological resources to foster the students’ oral
production
|
4,00
|
0,76
|
|
2
|
The
technological resources I employ effectively enhance students’ oral
production in comparison to traditional teaching methods.
|
4,11
|
0,79
|
|
3
|
It’s
easy to integrate technological resources to foster the students’ oral
production.
|
3,44
|
0,65
|
|
4
|
I seek
out new technological tools and resources to foster the students’ oral
production in my online classes.
|
4,19
|
0,81
|
|
5
|
The
technological resources I use help create a cooperative and collaborative
learning environment to foster the students’ oral production in my online
classes.
|
2,04
|
0,75
|
|
6
|
The
technological resources I use help create a more inclusive learning
environment for all my students to foster their oral production in my online
classes.
|
1,96
|
0,77
|
|
7
|
The
technological resources are effective in adapting to different learning
styles to foster the students’ oral production.
|
3,96
|
0,75
|
|
8
|
The technological resources used have facilitated the
evaluation and monitoring of the students’ oral production
|
4,04
|
0,77
|
|
9
|
I have noticed improvement in my students’ oral
production since the implementation of technological resources.
|
4,07
|
0,78
|
|
|
|
|
|
Table 2. Teachers’
perception of the effectiveness of technological resources they implement to
foster the students’ oral production.
Table
two shows the 9 statements about the teachers’ perception of the effectiveness of
technological resources they implement to foster the students’ oral production
along with their corresponding mean and standard deviation.
The
mean of the responses for statement one is (4). It’s significantly higher than
3 and the standard deviation (0,76), indicating that teachers agree that they consider
it necessary to integrate technological resources to encourage students' oral
production.
The
mean of the responses to statement two is (4.11) which is significantly higher
than 3 as well as the standard deviation (0,79) indicates that teachers agree
that the technological resources they use effectively improve students' oral
production compared to traditional teaching methods.
The
mean of the responses to statement three is (3.44). It’s slightly higher than
3. The standard deviation is (0,65), indicating that teachers do not find it
easy or difficult to integrate technological resources to encourage students'
oral production.
Statement
four has a mean of (4.19) in its responses which is significantly higher than
3. The standard deviation is (0,81), indicating that teachers frequently seek
new technological tools and resources to encourage students' oral production in
online classes.
The
mean of the responses to statement five is (2.04) is significantly less than 3 and
the standard deviation is (0,75), indicating that teachers disagree that the
technological resources they use help create a more cooperative and
collaborative learning environment for all students to foster their oral
production in online classes.
The
mean of the responses to statement six is (1.96). It is significantly less than
3 and the standard deviation is (0,77), indicating that teachers disagree that
the technological resources they use help create a more inclusive learning
environment to foster the students’ oral production.
The
mean of the responses in number seven is (3.96). It is significantly higher
than 3 and the standard deviation is (0,75), indicating that teachers consider to
be quite effective the use of technological resources to adapt to different
learning styles to encourage students' oral production.
The
mean of the responses in statement eight is (4.04). It is significantly greater
than 3 and the mean is 4,07, indicating that the technological resources used
moderately facilitate the assessment and monitoring of the students' oral
production.
The
mean of the responses in number nine (4.07) is significantly higher than 3 and
the mean is 0,78, indicating that students' oral production has improved
moderately since implementing technological resources in online classes.
|
Challenges
|
Frequency
of mention
|
Relative
frequency
|
Percentage
|
|
Technical
difficulties
|
4
|
0,148148148
|
15%
|
|
Student
distraction and lack of attention
|
4
|
0,148148148
|
15%
|
|
Limited
student participation and interaction
|
9
|
0,333333333
|
33%
|
|
Student
competency problems
|
6
|
0,222222222
|
22%
|
|
Students
don't have technological access and availability
|
3
|
0,111111111
|
11%
|
|
Teacher
competency problems
|
1
|
0,037037037
|
4%
|
|
Total
|
27
|
|
100%
|
Table 3. What challenges
have you found when implementing technological resources?
In
table three, it is observed that one of the most mentioned challenges is
Limited student participation and interaction. This challenge has obtained a
percentage of 30% and the highest frequency of mentions. This suggests that one
of the major concerns is to get students actively involved in the learning
process using technological resources.
Participation
and interaction: Limited student participation and interaction is the
most significant challenge, which could be related to the network used, for
example: students don’t have a steady connection.
Environmental
issues as family members interrupting during class. Psychological issues, for
instance, absence of enthusiasm, difficulty in organizing their time, and
feeling of being isolated. And, financial issues like students who don’t have
financial resources to afford a good internet service provider (Qutishat et al.,
2022).
Student
competency: Student competency issues are also a notable concern,
suggesting the need for additional support or training to enable students to
effectively use technology tools.
Teacher
competence: Teacher competence issues were rarely mentioned,
suggesting that teachers generally have the necessary skills to use
technologies in language teaching which is fundamental at the moment of
planning interactive lessons to keep learners engaged and encourage learning a
new language (Tünde, 2021).
Hypothesis
testing.
The
abbreviation of "hypothesis test statistic" is t-statistic (Al-Kassab,
2022): It was used for all of the 9 statements in Table 2. We used the
t-statistic formula for a sample:
uo=3
Statement
one:
t=
6,841052551
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2.056
Since
the t-statistic value (6.841052551) is much larger than the critical t-value
(2.056) and the p-value is extremely small (much smaller than 0.05), we reject
the null hypothesis.
Statement
two:
t=
7,320367905
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (7.320367905) is much larger than the critical t-value
(2.056) and the p-value is extremely small (much smaller than 0.05), we reject
the null hypothesis.
Statement
three:
t=
3,552495562
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (3,552495562) is larger than the critical t-value (2.056)
and the p-value is small (smaller than 0.05), we reject the null hypothesis.
Statement
four:
t=
7,610840905
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (7,610840905) is much larger than the critical t-value
(2.056) and the p-value is extremely small (much smaller than 0.05), we reject
the null hypothesis.
Statement
five:
t= -6,668984289
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (-6,668984289) is much smaller than the critical t-value
(2.056), we accept the null hypothesis.
Statement
six:
t=
-7,0068655
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (-7,0068655) is much smaller than the critical t-value
(2.056), we accept the null hypothesis.
Statement
seven:
t=
6,668984289
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (6,668984289) is much larger than the critical t-value
(2.056) and the p-value is extremely small (much smaller than 0.05), we reject
the null hypothesis.
Statement
eight:
t=
7,0068655
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (7,0068655) is much larger than the critical t-value (2.056)
and the p-value is extremely small (much smaller than 0.05), we reject the null
hypothesis.
Statement
nine:
t=
7,166581864
The critical
value of t for α=0.05 and n-1=26
From t
student distribution table
critical
t= 2,056
Since
the t-statistic value (7,166581864) is much larger than the critical t-value
(2.056) and the p-value is extremely small (much smaller than 0.05), we reject
the null hypothesis.
DISCUSSION:
From
this research, the consensus of English language teachers at ESPE is that they
find it essential and useful to incorporate technologies into their teaching
practice.
Other
studies have supported this finding too; for example, Cruz and Torres (2022)
found out that game-based interactive tools such as language learning games
enhance students’ participation and oral production. Likewise, according to
Al-Kassab (2022), technology can help in developing better communication skills
among students as reflected by teachers’ views obtained through this study.
On the
other hand, some studies contrast the findings of this study. For example, Tünde
(2021) observed that teachers often struggle with technical aspects of
technology integration in language learning, and teachers expressed moderate
confidence in including these resources. This inconsistency between perceived
effectiveness and actual implementation calls for more focused professional
development.
Despite
the valuable insights gained, this study has limitations. The first limitation
is that the sample size may not be a representation of all university English
teachers which could affect the finding’s generalizability. Self-reported data
may also introduce bias as teachers can overestimate their competence while
using technological resources (Aesaert et al., 2017). Finally, although it
would have given some depth into best practices, the study did not explore what
types of technological resources are most effective and this could be worth considering
for a further study.
Lastly,
limited student participation and technical difficulties were among the
challenges identified without going further into understanding why these
problems occurred in a such context.
CONCLUSIONS
The
results obtained through this research show that the technological resources
used in online education have a positive impact on the oral production of
learners of the English language. Results also show that most of the teachers
interviewed are aware of such resources and consider them useful tools to enhance
language learning and practice due to the speed at which access to various
content is possible, and also because activities can be personalized. The
educator's disposition toward interactive tools, such as educational games and
social networking platforms, reflects the importance of student motivation and
involvement in building their communication competency.
However,
one of the most serious problems detected by the study is the challenge of
creating collaboratively and inclusively virtual environments, despite the
facilitation by technologically advanced tools. The results show that while
instructors have tried to use multimedia resources that may allow for activity
and participation, the interaction among the students remains limited. It
therefore calls for further research into pedagogical matters that improve
group cohesiveness and allow for more collaboration online.
Finally,
the inability of some students to participate in online courses has been cited
as the main limitation to the fullest implementation of technological tools and
devices.
Despite
the wide variety of options available, it has been widely noted by teachers
that student interest and participation remain unsatisfactory, which limits the
fullest exploitation of the technologies that have been implemented. This
finding points to the need to embed technology into the virtual learning
environment, develop student motivation, and acquire an appropriate atmosphere
that promotes active learning.
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