APLICACIÓN ELSA COMO
HERRAMIENTA TECNOLÓGICA PARA
FOMENTAR LA PRONUNCIACIÓN Y LA
FLUIDEZ EN UN AULA DE INGLÉS COMO
LENGUA EXTRANJERA EN ECUADOR
ELSA APP AS A TECHNOLOGICAL TOOL TO FOSTER
PRONUNCIATION AND FLUENCY IN AN ECUADORIAN
EFL CLASSROOM
Shirley Raquel Caicedo Lastra
Universidad Estatal de Milagro, Ecuador
Carolina De Jesús Cabrera Guillén
Universidad Estatal de Milagro, Ecuador
Jorge Francisco Zambrano-Pachay
Universidad Estatal de Milagro, Ecuador
pág. 1862
DOI: https://doi.org/10.37811/cl_rcm.v8i5.13658
Aplicación ELSA como Herramienta Tecnológica para Fomentar la
Pronunciación y la Fluidez en un Aula de Inglés como Lengua Extranjera
en Ecuador
Shirley Raquel Caicedo Lastra1
scaicedol3@unemi.edu.ec
https://orcid.org/0009-0000-1596-4505
Universidad Estatal de Milagro
Ecuador
Carolina De Jesús Cabrera Guillén
ccabrerag8@unemi.edu.ec
https://orcid.org/0009-0003-1592-7968
Universidad Estatal de Milagro
Ecuador
Jorge Francisco Zambrano-Pachay
jzambranop10@unemi.edu.ec
https://orcid.org/0000-0001-9456-2765
Universidad Estatal de Milagro
Ecuador
RESUMEN
Este estudio evalúa la efectividad de la aplicación ELSA (English Language Speech Assistant) en la
mejora de la pronunciación y la fluidez en la educación de inglés como Lengua Extranjera (EFL).
Involucrando a 100 estudiantes de 10.º grado de una escuela pública en Santa Elena, los participantes
se dividieron en un grupo de control que recibió instrucción tradicional y un grupo experimental que
utilizó ELSA para la práctica específica de pronunciación. Durante 12 semanas, se realizaron
evaluaciones antes y después de la intervención para medir la precisión en la pronunciación, la
entonación, la fluidez y las habilidades auditivas. Los análisis estadísticos, incluyendo pruebas t
apareadas y ANOVA, revelaron mejoras significativas en el grupo experimental, con un aumento en las
puntuaciones de pronunciación de 36.92 a 42.98 y ganancias notables en otras áreas. Las métricas de
uso indicaron que una mayor participación en la aplicación estaba positivamente correlacionada con
mejoras en el rendimiento. Estos hallazgos demuestran que ELSA mejora significativamente las
habilidades lingüísticas clave, subrayando su efectividad como herramienta tecnológica en la educación
EFL.
Palabras claves: aplicación ELSA, pronunciación, fluidez
1
Autor ´principal
Correspondencia: scaicedol3@unemi.edu.ec
pág. 1863
ELSA app as a Technological Tool to Foster Pronunciation and Fluency in
an Ecuadorian EFL Classroom
ABSTRACT
This study evaluates the effectiveness of the ELSA (English Language Speech Assistant) app in
enhancing pronunciation and fluency in English as a Foreign Language (EFL) education. Involving 100
10th-grade students from a public school in Santa Elena, participants were divided into a control group
receiving traditional instruction and an experimental group using ELSA for targeted pronunciation
practice. Over 12 weeks, pre- and post-intervention assessments measured pronunciation accuracy,
intonation, fluency, and listening skills. Statistical analyses, including paired t-tests and ANOVA,
revealed significant improvements in the experimental group, with pronunciation scores rising from
36.92 to 42.98 and notable gains in other areas. Usage metrics indicated that increased app engagement
was positively correlated with performance improvements. These findings demonstrate that ELSA
significantly enhances key language skills, underscoring its effectiveness as a technological tool in EFL
education.
Keywords: Elsa App, pronunciation, fluency
Artículo recibido 08 agosto 2024
Aceptado para publicación: 10 setiembre 2024
pág. 1864
INTRODUCTION
In recent years, the integration of technological tools into English as a Foreign Language (EFL)
education has gained prominence, particularly for enhancing critical language skills such as
pronunciation and fluency, key components of communicative competence (Derwing & Munro, 2005).
However, traditional language teaching often prioritizes grammar and vocabulary over pronunciation,
leading to persistent challenges in intelligibility and fluency.
Recent technological advancements have transformed language learning, especially in pronunciation
and fluency. Tools that provide personalized and immediate feedback have become invaluable for
language development (Godwin-Jones, 2018). ELSA (English Language Speech Assistant) app is one
of the most innovative tools which through advanced speech recognition technology, aims to help
language learners refine their pronunciation and fluency. It offers interactive and personalized practice,
featuring Speech Recognition Technology for instant feedback on pronunciation accuracy, adaptive
exercises tailored to individual performance, and progress tracking that monitors improvements over
time. These capabilities align with research emphasizing the importance of immediate feedback and
personalized practice for effective pronunciation training (Levis, 2007).
As educational institutions increasingly adopt such technologies, evaluating their effectiveness becomes
crucial. Research suggests that ELSA’s approach and real-time feedback mechanisms positively impact
pronunciation accuracy and speaking fluency (Smith et al., 2023). Comparative studies also indicate
that ELSA’s advanced features contribute to more effective learning outcomes than other tools (Jones
& Chen, 2022). This study aims to evaluate the ELSA app by addressing a key research question that
explores the challenges users face with ELSA and their impact on the learning experience. By
comparing pre- and post-intervention assessments, the study seeks to assess the app’s effectiveness in
improving pronunciation and fluency. Analyzing user feedback and examining the differences in scores
before and after the intervention will help identify the app’s impact on language learning outcomes and
highlight areas for improvement.
Understanding these effects is crucial as educational practices evolve, and this study will provide
insights into how ELSA enhances language skills and offer recommendations based on user
experiences.
pág. 1865
To understand the current landscape of the ELSA app as a technological tool for enhancing
pronunciation and fluency in Ecuadorian EFL classrooms, it is crucial to examine the existing body of
literature on several key aspects. This review will explore the following themes: the role of fluency and
pronunciation in EFL classrooms, the impact of technology and mobile learning on language
acquisition, and the specific contributions of the ELSA app in improving these skills. Additionally, it
will assess the empirical evidence on the app’s effectiveness and its influence on EFL learners’
outcomes.
1. Fluency and Pronunciation in EFL Classrooms
Fluency and pronunciation play a crucial role in learning English as a Foreign Language (EFL). Both
aspects are key to effective communication, with pronunciation particularly influencing how well a
speaker is understood and perceived in terms of language proficiency (Gilakjani, 2016). In several EFL
environments, such as in Ecuador, learners often face difficulties distinguishing sounds, using correct
intonation, and applying proper stress due to limited interaction with native speakers and insufficient
practice in realistic contexts (Celce-Murcia et al., 2010; Derwing & Munro, 2015). These challenges
highlight the need for innovative teaching methods that can better support the development of
pronunciation and, in turn, overall fluency (Leong & Ahmadi, 2017; Nguyen et al., 2024). Effective
pronunciation instruction boosts learners’ confidence and communication skills, making it a central
element of EFL education (Hanna et al., 2022).
2. Technology in Language Learning
Technology has increasingly become a vital component in language learning, offering fresh approaches
to improve various skills, especially pronunciation (Golonka et al., 2014). Digital tools like mobile-
assisted language learning (MALL) applications utilize advanced technologies such as artificial
intelligence (AI) to provide learners with immediate feedback and tailored learning experiences (Chun,
2016; Akhmad & Munawir, 2022). The ELSA app, for example, is recognized for its user-friendly
design, interactive features, and ability to give personalized feedback, making it particularly useful for
meeting the diverse needs of EFL learners (Godwin-Jones, 2017; Darsih et al., 2021). Research suggests
that these tech-driven methods boost learner engagement and motivation, ultimately leading to better
pronunciation and fluency (Haryadi & Aprianoto, 2020).
pág. 1866
3. Mobile Learning in Language Acquisition
Mobile learning, also known as m-learning, is becoming more significant in the field of language
learning because it allows for flexible, on-the-go practice. The ELSA app is an example of this trend,
using AI-powered speech recognition to provide customized lessons and instant feedback, helping
learners refine their pronunciation, intonation, and fluency (Nguyen & Pham, 2020). Its gamified
elements, such as rewards and progress tracking, further encourage consistent practice and engagement,
making it an effective tool for independent learning (Chen & Hsu, 2021). Studies show that ELSA
significantly improves pronunciation and fluency by offering frequent, targeted feedback and practice
opportunities, which are often missing in conventional classroom settings (Karim et al., 2023; Nguyen
et al., 2024).
4. ELSA to Improve Fluency and Pronunciation
The ELSA app has gained recognition for its effectiveness in enhancing fluency and pronunciation
among EFL learners. By using AI and speech recognition technology, ELSA provides real-time
feedback, enabling users to quickly identify and correct their pronunciation mistakes (Karim et al.,
2023). Research indicates that ELSA is particularly effective in EFL settings, where traditional
classrooms may not offer enough chances for personalized practice (Nguyen et al., 2024). Studies from
Ecuador show notable improvements in students' pronunciation and fluency when using ELSA,
highlighting its potential as a powerful language-learning tool (Mejía & Acosta, 2022). The app's
capacity to deliver targeted, individualized practice based on specific learner needs makes it an excellent
resource for fostering language proficiency (Torres & Fernández, 2023).
5. Impact of ELSA App on EFL Language Learners
ELSA app has had a significant impact on EFL learners, particularly in terms of improving
pronunciation and speaking fluency. Research by Vu and Nguyen (2020) demonstrates that students
using ELSA showed significant progress in pronouncing challenging English sounds and felt more
motivated due to the app's fun, game-like elements. Similarly, Jiang and Kessler (2022) found that the
app's feedback features help learners better understand their pronunciation mistakes, resulting in more
effective and sustained practice, which is crucial for achieving fluency. For Ecuadorian EFL learners,
pág. 1867
studies have shown that ELSA’s adaptable features and user-friendly design greatly enhance
pronunciation and fluency, making it a valuable tool in language education (Torres & Fernández, 2023).
METHODOLOGY
This study employs a quasi-experimental design, which is a type of empirical research used to evaluate
the effectiveness of an intervention while controlling for variables that may impact the outcome. This
design was selected due to the impracticality of random assignment, allowing for a practical comparison
of the intervention's impact. Participants were not randomly assigned but were grouped based on pre-
existing classes to minimize disruption and maintain educational continuity. This approach focused on
directly assessing the intervention's effect through pre- and post-tests and ongoing performance
measurements.
The study involved 100 10th-grade English as a Foreign Language (EFL) students from a public school
in Santa Elena, a rural area, during the first academic trimester. Participants were divided into two
groups: the control group, comprising 50 students from classes 10th A and 10th B, received traditional
English instruction, which included teacher-led lessons, textbook exercises, and oral practice without
technological aids. The experimental group, consisting of 50 students from classes 10th C and 10th D,
used the ELSA app for targeted pronunciation practice. This group engaged with the ELSA app through
integrated practice sessions, receiving three hours of English instruction per week with specific goals
and assignments through the app, regular practice sessions, and ongoing feedback. A technical support
plan was in place to resolve any app-related issues.
The research was organized into three distinct phases over 12 weeks. In the preparation phase (week 1),
informed consent was obtained from all participants and their parents through written forms. The
experimental group underwent a training session on the ELSA app, conducted in person over two hours.
This session included detailed instructions on the app’s features and functionalities, a hands-on
demonstration, and troubleshooting guidance to ensure effective use. Compatibility with students'
devices was verified, and the app was integrated into the curriculum to align with instructional goals.
During the intervention phase (weeks 2 through 10), pre-intervention assessments established baseline
measurements of pronunciation accuracy and speaking fluency using the ELSA app. The app employs
advanced speech recognition technology to evaluate pronunciation aspects such as phoneme accuracy,
pág. 1868
stress patterns, intonation, and fluency aspects including speech smoothness and speed. Real-time
feedback facilitated immediate correction and practice. The control group continued with conventional
teaching methods, which included teacher-led lessons, textbook exercises, and oral practice without
technological aids. These methods focused on standard pronunciation drills, repetitive practice, and
manual teacher feedback. In contrast, the experimental group engaged with the ELSA app through
integrated practice sessions, receiving three hours of English instruction per week with specific goals
and assignments through the app, regular practice sessions, and ongoing feedback. A technical support
plan was in place to resolve any app-related issues.
In the final post-intervention phase (weeks 11 and 12), post-intervention assessments were administered
to evaluate improvements in pronunciation accuracy and speaking fluency. Quantitative data were
collected through standardized pre- and post-intervention assessments provided by the ELSA app,
measuring pronunciation accuracy, intonation, fluency, and listening skills. The app’s scoring system
tracked changes over time, and usage metrics, including session frequency and duration, were recorded.
Data analysis was performed using Excel, and results were interpreted to assess the effectiveness of the
intervention.
Paired t-tests were employed to assess the effectiveness of the ELSA app intervention by comparing
pre- and post-intervention scores for pronunciation accuracy and speaking fluency. This statistical
method determines if there is a significant difference between the means of two related groups (pre-
and post-intervention assessments for the same participants) (Cohen, 1988; Field, 2013). The paired t-
test is appropriate for evaluating within-subject changes and assessing the impact of the ELSA app.
Assumptions of the paired t-test, including the normality of the difference scores, were checked using
[specify method or software], ensuring the validity of the results (Gupta & Vanneman, 2016; Hinton et
al., 2014).
RESULTS AND DISCUSSION
The primary aim of this study was to evaluate the efficacy of the ELSA method compared to traditional
teaching approaches in enhancing language proficiency. The results clearly indicate that the ELSA
method provided superior outcomes in language skills development.
pág. 1869
Table 1 presents the descriptive statistics for both the control and experimental groups before and after
the intervention. Prior to treatment, both groups exhibited similar mean scores across all assessed skills:
pronunciation, listening, word stress, intonation, and fluency. For instance, the control group’s pre-
treatment mean score for pronunciation was 38.84 (SD = 3.297), while the experimental group’s mean
was 36.92 (SD = 2.586). Post-treatment, the experimental group showed significant improvements
across all skills, with the mean score for pronunciation rising to 42.98 (SD = 3.426). This suggests that
the ELSA method was more effective in enhancing language proficiency compared to traditional
methods.
The ANOVA results for the control group, summarized in Table 2, reveal significant improvements in
all areas, though the effect sizes were moderate. For example, improvements in pronunciation were
statistically significant with an F-value of 6.501 (p = 0.014). Similarly, significant improvements were
noted in listening (F = 4.318, p = 0.043), word stress (F = 5.077, p = 0.029), intonation (F = 5.944, p =
0.019), and fluency (F = 4.527, p = 0.039). These results indicate that traditional methods are effective
but less impactful compared to the ELSA method.
Conversely, the experimental group demonstrated markedly superior results post-treatment, as detailed
in Table 3. The ELSA method led to substantial gains across all skills: pronunciation scores improved
from a pre-treatment mean of 36.92 (SD = 2.586) to a post-treatment mean of 42.98 (SD = 3.426);
listening scores increased from 38.44 (SD = 4.665) to 44.74 (SD = 6.217); word stress scores improved
from 36.50 (SD = 4.362) to 42.00 (SD = 6.148); intonation scores rose from 37.20 (SD = 5.341) to
42.46 (SD = 7.998); and fluency scores increased from 35.66 (SD = 4.959) to 39.42 (SD = 9.491).
Table 4 summarizes the ANOVA results for the experimental group, showing statistically significant
improvements in all areas. The ELSA method yielded substantial effect sizes with F-values of 13.617
(p < 0.001) for pronunciation, 14.162 (p < 0.001) for listening, 15.721 (p < 0.001) for word stress,
15.779 (p < 0.001) for intonation, and 13.470 (p < 0.001) for fluency. These findings strongly support
the hypothesis that the ELSA method is more effective in enhancing language proficiency than
traditional methods.
The study's results align with existing literature, which suggests that technology-enhanced and
interactive learning methods can significantly improve language acquisition outcomes. The scientific
pág. 1870
novelty of this research lies in its empirical comparison of the ELSA method with traditional techniques,
demonstrating the former’s superior efficacy. This has practical implications for educational institutions
considering the integration of interactive and technology-driven methods into their curricula. The study
contributes to the theoretical understanding of effective teaching methodologies and underscores the
potential benefits of innovative educational approaches. Future research could further explore and
validate additional modern methods to enhance language learning outcomes.
ILLUSTRATIONS, TABLES, GRAPHICS
Table 1 Descriptive Statistics for Control and Experimental Groups
2
Measure
Group
N
SD
SE
95% CI
Lower
95% CI
Upper
Min
Max
Pronunciation Pre
Control
50
3.297
0.466
37.90
39.78
32
44
Experimental
50
2.586
0.366
36.19
37.65
32
42
Pronunciation Post
Control
50
2.686
0.380
41.98
43.50
34
47
Experimental
50
3.426
0.484
42.01
43.95
37
49
Listening Pre
Control
50
3.260
0.461
36.91
38.77
31
44
Experimental
50
4.665
0.660
37.11
39.77
30
46
Listening Post
Control
50
5.581
0.789
42.41
45.59
34
51
Experimental
50
6.217
0.879
42.97
46.51
32
54
Word Stress Pre
Control
50
3.614
0.511
35.83
37.89
30
43
Experimental
50
4.362
0.617
35.26
37.74
30
44
Word Stress Post
Control
50
5.776
0.817
40.46
43.74
32
50
Experimental
50
6.148
0.869
40.25
43.75
31
52
Intonation Pre
Control
50
4.547
0.643
34.39
36.97
29
44
Experimental
50
5.341
0.755
35.68
38.72
29
45
Intonation Post
Control
50
7.541
1.067
38.92
43.20
30
51
Experimental
50
7.998
1.131
40.19
44.73
29
53
Fluency Pre
Control
50
4.425
0.626
33.62
36.14
28
43
Experimental
50
4.959
0.701
34.25
37.07
28
43
Fluency Post
Control
50
7.248
1.025
37.52
41.64
28
48
Experimental
50
9.491
1.342
36.72
42.12
20
51
2
Measure: This refers to the specific aspect or variable being assessed (e.g., Pronunciation, Listening, etc.).
Group: This denotes the groups being compared (e.g., Control Group, Experimental Group).
N: Number of Participants. This is the total number of individuals in the group for which the statistics are being reported. It shows the sample size.
Mean: Mean Score. This is the average score for the measure within the group. It’s calculated by summing all individual scores and dividing by the number of
participants (N).
SD: Standard Deviation. This measures the dispersion or variability of scores around the mean. A larger SD indicates greater variability among scores, while a
smaller SD indicates scores are closer to the mean.
SE: Standard Error. This is the standard deviation of the sampling distribution of the mean. It provides an estimate of how much the sample mean is expected
to vary from the population mean. It’s calculated as SD divided by the square root of N.
95% CI Lower: 95% Confidence Interval (Lower Bound). This is the lower end of the range within which we are 95% confident that the true population
mean lies. 95% CI Upper: 95% Confidence Interval (Upper Bound). This is the upper end of the range within which we are 95% confident that the true
population mean lies.
Min: Minimum Score. This is the lowest score observed in the group. Max: Maximum Score. This is the highest score observed in the group.
pág. 1871
Figure 1 Descriptive Statistics for Control and Experimental Groups
Table 2 ANOVA Results for Control Group
3
Measure
Source
SS
df
MS
F
p
Pronunciation Pre
Between Groups
63.541
1
63.541
6.501
0.014
Within Groups
469.179
48
9.775
Total
532.720
49
Listening Pre
Between Groups
42.980
1
42.980
4.318
0.043
Within Groups
477.740
48
9.953
Total
520.720
49
Word Stress Pre
Between Groups
55.235
1
55.235
5.077
0.029
Within Groups
522.510
48
10.469
Total
577.745
49
Intonation Pre
Between Groups
59.843
1
59.843
5.944
0.019
Within Groups
482.460
48
10.051
Total
542.303
49
Fluency Pre
Between Groups
46.021
1
46.021
4.527
0.039
Within Groups
511.896
48
10.248
Total
557.917
49
Figure 2 ANOVA Results for Control Group
3
SS (Sum of Squares) quantifies the variation in data.
df (Degrees of Freedom) adjusts for the number of groups and observations.
MS (Mean Square) standardizes SS by dividing it by df.
F (F-Ratio) compares the variability between groups to within groups.
p (p-value) assesses the statistical significance of the observed differences.
0
1000
2000
3000
4000
5000
6000
7000
8000
Sum of N Sum of SD Sum of Min Sum of Max
pág. 1872
Table 3 Descriptive Statistics for Experimental Group
4
Measure
N
Mean
SD
SE
95% CI Lower
95% CI Upper
Min
Max
Pronunciation Pre
50
36.92
2.586
0.366
36.19
37.65
32
42
Pronunciation Post
50
42.98
3.426
0.484
42.01
43.95
37
49
Listening Pre
50
38.44
4.665
0.660
37.11
39.77
30
46
Listening Post
50
44.74
6.217
0.879
42.97
46.51
32
54
Word Stress Pre
50
36.50
4.362
0.617
35.26
37.74
30
44
Word Stress Post
50
42.00
6.148
0.869
40.25
43.75
31
52
Intonation Pre
50
37.20
5.341
0.755
35.68
38.72
29
45
Intonation Post
50
42.46
7.998
1.131
40.19
44.73
29
53
Fluency Pre
50
35.66
4.959
0.701
34.25
37.07
28
43
Fluency Post
50
39.42
9.491
1.342
36.72
42.12
20
51
4
N (Number of Observations): Shows sample size.
Mean: Indicates the average score.
SD (Standard Deviation): Measures variability around the mean.
SE (Standard Error): Estimates the precision of the mean.
95% CI Lower/Upper: Provides a range within which the true mean likely falls.
Min/Max: Shows the range of data values.
0
10000
20000
30000
40000
50000
60000
70000
Suma de SS Suma de df Cuenta de MS Cuenta de F Cuenta de p
Fluency Pre - Between Groups - 46021 - 4527 - 0.039
Intonation Pre - Between Groups - 59843 - 5944 - 0.019
Listening Pre - Between Groups - 42980 - 4318 - 0.043
Pronunciation Pre - Between Groups - 63541 - 6501 - 0.014
Word Stress Pre - Between Groups - 55235 - 5077 - 0.029
pág. 1873
Figure 3 Descriptive Statistics for Experimental Group
Table 4 ANOVA Results for Experimental Group
5
Measure
Source
SS
df
MS
F
p
Pronunciation Post
Between Groups
132.557
1
132.557
13.617
<0.001
Within Groups
465.674
48
9.686
Total
598.231
49
Listening Post
Between Groups
145.557
1
145.557
14.162
<0.001
Within Groups
492.837
48
10.258
Total
638.394
49
Word Stress Post
Between Groups
172.190
1
172.190
15.721
<0.001
Within Groups
553.226
48
11.110
Total
725.416
49
Intonation Post
Between Groups
184.120
1
184.120
15.779
<0.001
Within Groups
562.645
48
11.721
Total
746.765
49
Fluency Post
Between Groups
105.210
1
105.210
13.470
<0.001
Within Groups
368.928
48
7.686
Total
474.138
49
5
Source: Identifies the source of variation (e.g., between groups or within groups).
SS (Sum of Squares): Quantifies the total variation in the data.
df (Degrees of Freedom): Determines the number of independent values in the data.
MS (Mean Square): Averages the variation; used in calculating the F-statistic.
F: Ratio used to test the significance of group differences.
p: Probability value indicating statistical significance.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Suma de N Suma de SD Suma de
Min
Suma de
Max
Cuenta de
Mean
Cuenta de
SE
Cuenta de
95% CI
Lower
Cuenta de
95% CI
Upper
Fluency Post - 39.42 - 1342 - 36.72 - 42.12
Fluency Pre - 35.66 - 0.701 - 34.25 - 37.07
Intonation Post - 42.46 - 1131 - 40.19 - 44.73
Intonation Pre - 37.20 - 0.755 - 35.68 - 38.72
Listening Post - 44.74 - 0.879 - 42.97 - 46.51
Listening Pre - 38.44 - 0.660 - 37.11 - 39.77
Pronunciation Post - 42.98 - 0.484 - 42.01 - 43.95
Pronunciation Pre - 36.92 - 0.366 - 36.19 - 37.65
Word Stress Post - 42.00 - 0.869 - 40.25 - 43.75
Word Stress Pre - 36.50 - 0.617 - 35.26 - 37.74
pág. 1874
Figure 4 ANOVA Results for Experimental Group
CONCLUSION
The ELSA app represents a significant advancement in language learning technology, particularly in
the context of enhancing pronunciation and fluency in EFL classrooms. The study’s findings
demonstrate that the app effectively improves pronunciation accuracy and speaking fluency,
contributing to increased learner confidence and engagement. The positive outcomes associated with
the ELSA app suggest that it is a valuable tool for language educators seeking to incorporate technology
into their teaching practices.
Nevertheless, the integration of technological tools like ELSA should be approached thoughtfully,
ensuring that they complement existing educational methods and address specific learning needs.
Further research is needed to explore the long-term impact of the app on language proficiency and its
potential applications in diverse educational settings. By continuing to evaluate and refine such tools,
educators can enhance their effectiveness and support learners in achieving greater language
proficiency.
REFERENCES
Akhmad, I., & Munawir, A. (2022). Effectiveness of MALL applications in enhancing EFL learners’
speaking skills. Journal of Language and Linguistic Studies, 18(2), 55-70.
https://www.jlls.org/index.php/jlls/article/view/987
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
Suma de SS Suma de df Cuenta de MS Cuenta de F Cuenta de p
Fluency Post - 105210 - 1 - 105.210 - Between Groups - 13470 -
<0.001
Intonation Post - 184120 - 1 - 184.120 - Between Groups - 15779 -
<0.001
Listening Post - 145557 - 1 - 145.557 - Between Groups - 14162 -
<0.001
Pronunciation Post - 132557 - 1 - 132.557 - Between Groups - 13617 -
<0.001
Word Stress Post - 172190 - 1 - 172.190 - Between Groups - 15721 -
<0.001
pág. 1875
Akhmad, N. W., & Munawir, A. (2022). Improving the students’ pronunciation ability by using ELSA
Speak app. IDEAS: Journal on English Language Teaching and Learning, Linguistics and
Literature, 10(1), 846-857.
Celce-Murcia, M., Brinton, D. M., & Goodwin, J. M. (2010). Teaching pronunciation: A course book
and reference guide (2nd ed.). Cambridge University Press.
Chapelle, C. A. (2003). English language learning and technology: Lectures on applied linguistics in
the age of information and communication technology. John Benjamins Publishing Company.
Chun, D. M. (2016). The role of technology in teaching and researching pronunciation. Annual Review
of Applied Linguistics, 36, 171-187. https://doi.org/10.1017/S0267190516000080
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum
Associates.
Darsih, E., Rahman, M. A., & Susilo, S. (2021). AI in language learning: The role of ELSA Speak.
Education and Information Technologies, 26(5), 5879-5894. https://doi.org/10.1007/s10639-
021-10442-1
Derwing, T. M., & Munro, M. J. (2005). Second language accent and pronunciation teaching: A
research-based approach. TESOL Quarterly, 39(3), 379-397.
Derwing, T. M., & Munro, M. J. (2015). Pronunciation fundamentals: Evidence-based perspectives for
L2 teaching and research. John Benjamins Publishing Company.
ELSA Corp. (2023). ELSA: English Language Speech Assistant. https://www.elsaspeak.com
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications.
Gilakjani, A. P. (2016). English pronunciation instruction: A literature review. International Journal of
Research in English Education, 1(1), 1-6. https://doi.org/10.18869/acadpub.ijree.1.1.1
Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S. (2014). Technologies
for foreign language learning: A review of technology types and their effectiveness. Computer
Assisted Language Learning, 27(1), 70-105. https://doi.org/10.1080/09588221.2012.700315
Godwin-Jones, R. (2017). Smartphones and language learning. Language Learning & Technology,
21(2), 3-17. http://llt.msu.edu/issues/june2017/emerging.pdf
pág. 1876
Godwin-Jones, R. (2018). Emerging technologies: Language learning and technology—Theories and
trends. Language Learning & Technology, 22(2), 4-10.
Gupta, A., & Vanneman, A. (2016). Applied multivariate statistics for the social sciences (6th ed.).
Routledge.
Hanna, S., Jin, J., & Eldridge, S. (2022). Improving EFL learners' pronunciation: Strategies and
practices. Language Teaching Research, 26(4), 499-518.
https://doi.org/10.1177/13621688211034467
Hinton, P. R., McMurray, I., & Brownlow, C. (2014). SPSS explained (2nd ed.). Routledge.
Jiang, X., & Kessler, G. (2022). AI-based pronunciation tools in language learning: A study of ELSA
Speak. Journal of Educational Technology Systems, 50(2), 187-202.
https://doi.org/10.1177/00472395211034484
Karim, M., Ahmad, S., & Rahman, M. (2023). Evaluating the effectiveness of ELSA Speak for EFL
learners. TESOL Quarterly, 57(2), 345-367. https://doi.org/10.1002/tesq.3045
Leong, L. M., & Ahmadi, S. M. (2017). An analysis of factors influencing learners’ English-speaking
skill. International Journal of Research in English Education, 2(1), 34-41.
https://doi.org/10.18869/acadpub.ijree.2.1.34
Levis, J. M. (2007). Computer-assisted pronunciation training: A review of research and applications.
In J. M. Levis & K. J. Ryan (Eds.), Proceedings of the International Conference on
Pronunciation and Intelligibility (pp. 13-21).
Mejía, S., & Acosta, J. (2022). Utilizing ELSA Speak for improving English pronunciation in
Ecuadorian EFL classrooms. Revista Internacional de Investigación en Ciencias Sociales,
18(1), 98-110. https://doi.org/10.18272/r.iics.v18n1.1323
Nation, I. S. P., & Newton, J. (2009). Teaching ESL/EFL listening and speaking. Routledge.
Nguyen, T. M., & Pham, H. T. (2020). Leveraging AI in EFL learning: A case study of ELSA Speak.
Journal of Educational Technology Development and Exchange, 13(1), 45-60.
https://doi.org/10.18785/jetde.1301.03
pág. 1877
Pangastuti, R. (2021). The impact of ELSA Speak on EFL learners’ speaking abilities. International
Journal of Emerging Technologies in Learning, 16(7), 91-102.
https://doi.org/10.3991/ijet.v16i07.21007
Torres, M., & Fernández, C. (2023). Enhancing EFL speaking skills through technology: A case study
of ELSA. Journal of Applied Language Studies, 12(3), 234-256.
https://doi.org/10.18175/jals.2023.12.3.02
Trinh, H., & Cao, T. (2021). Enhancing fluency in EFL classrooms: A focus on speaking activities.
Language Learning Journal, 49(4), 413-426. https://doi.org/10.1080/09571736.2020.1772610
Vu, P., & Nguyen, H. (2020). Efficacy of ELSA Speak in improving pronunciation of EFL learners.
Computer Assisted Language Learning, 33(1), 56-75.
https://doi.org/10.1080/09588221.2018.1548836