Document Type : Research Article
Authors
^{1} IAU, Isfahan (Khorasgan) Branch
^{2} English Department, IAU, Isfahan Branch
Abstract
Keywords
Main Subjects
Introduction
Today people around the world are using information and communication technologies (ICTs) and the Internet almost anywhere and anytime. Technology is playing very crucial role in everyday life and in academic settings. As Ralston (2012) pointed out, technology is a fact of life as the medium of daily communication affecting language learning. According to Van der Beemt, Akkerman, and Simons (2010), today’s young generation is called net generation because technologies and World Wide Web are the two most familiar items for them. Horovits (2012) stated that this net generation has been found to be applied in different methods of learning from the earlier generation. The new generation is more engaged and constantly connected to the net. It is believed that net generation students prefer independent learning style and have stronger beliefs in technologies for better learning.
Tapscott (1998) coined the term net generation that refers to the children who grow up with digital world. He believes that the new generation is exceptionally curious, self –reliant, contrarian, smart, focused, able to adapt high in selfesteem, and has a global orientation. There has been a change in the way children gather, accept, and retain information. The main positive influence of social network has been connection and interaction between their members as well as exchanging and sharing information, knowledge and ideas. By these networks, students can increase their creativity and productivity and learn how to work in a group and society. They can make groups, help each other solve problems. This can be related to learning a second language. The members can be from all over the world without any limitations. Studies conducted by the Institute for Prospective Technological Studies (IPTS) demonstrate that the high speed of social media application outside the formal educational classroom provides new opportunities for innovation and modernization of education and training institution.
Online social networking websites like Twitter, Facebook, Myspace and YouTube are becoming more and more popular among people around the world. These networks become part of daily life for many purposes including educational ones at all levels. Recently, many studies have shown that younger people spend a considerable portion of their daily routines interacting through social media. These sites have become important part of most students’ lives (Van den Beemt, Akkerman, & Simons, 2010; Boyd, 2007; Lomicka & Lord, 2011; Van der Beemt, Akkerman, & Simons, 2010; Yapici & Hevedanli, 2014). Therefore, these have a great potential for the pedagogical system. Social networks can help students and teachers for facilitating teaching and learning inside and outside the classroom. In recent years, numerous studies have attempted to explain the potential of these networks in different teaching and learning areas (Ajjan & Hartshorne, 2008; Armstrong & Franklin, 2008; Jones 2008; Jones, Blackey, Fitzgibbon, & Chew 2010; Tiryakioglu & Erzurum, 2011).
Jones, Blackey, Fitzgibbon, and Chew (2010) explained that social networks can develop formal learning, and become part of the educational environment of students. According to Lee (2009) and Kabilan (2010), students believe that social networks such as Facebook, Telegram, or Tweeter could utilize an online situation to facilitate learning English. Some studies have shown that online environment can be an appropriate device to help learning different language skills, increase students’ motivation, confidence, and attitudes towards learning English that can lead to successful application of CALL (Kabilan, 2010; Vandewaetere & Desmet, 2009).
One of the main problems that students challenge in language learning is lack of chance for having personalized course contents and they complain about following the same curriculum. (BartlettBragg, 2006; Donmus, 2010). New technologies such as social network sites have created an effective new means irrespective of time, place, and pace which lead to selfstudy autonomous learning where language is always described as a selfstudy subject. These tools and many others, are tools to change traditional approaches in language learning generally. Recently, many studies have been carried out to investigate the impact of social networks on learning second language in EFL learners. The most related studies are to determine whether the student and teacher find out it commutable, helpful and to use social network as a tool for educational purpose inside and outside the classroom. A large number of these studies agree on the positive impact of social networks on teaching a second language (Habash, 2015; Oxford, 1990; Oxford & Ehrman, 1995; O’Malley & Chamot, 1990; Tapscott, 2009; Wenden, 1991).
Selim (2007) stated that users who were very familiar with web technologies and the skills needed to use computer and mobile devices for instructure developed positive attitudes. On the other hand, students who were not skilled in ICT became anxious about the use of computers, had lower expectations from educational technology, and they did not believe in the benefits of elearning (Vrana, Garyfallos, Zafiropoulos, & Paschaloudis, 2005). In addition, Habbash (2015) represented the impact of mobile phones on learning English vocabulary. The main objective of his study was investigating the role of mobile phones in EFL classes for the undergraduate student of the University of Tabuk for improving their English vocabulary. The data were collected through questionnaires distributed among the teachers who teach to EFL students. Most of the teachers believed that mobiles were the new technological tools that should be regarded as useful devices and younger students and teacher can use new technology in their comfort level.
Due to the popularity of applications like Telegram in Iran, many studies have been conducted on the use of such applications on the language, linguistic features, or gender differences in the textese (Akbari, 2013; Chalak, 2017; Heidari & Alibabaee, 2013). There have been other studies which have focused on the use of different applications to help foster language learning. Some of these studies have focused on the use of applications to improve vocabulary knowledge of Iranian EFL learners. Jafari and Chalak (2016) investigate the role of WhatsApp in the vocabulary learning improvement of Iranian junior high school EFL students and found that WhatsApp was an effective tool in vocabulary learning of the learners and improved their vocabulary gain. Ghaemi and Golshan (2017) also conducted a study on Iranian EFL learners via social networks to improve their vocabulary gain and enhance their interest in learning vocabulary. The findings of their study showed that the students in the experimental group outperformed the control group and the use of social networks were regarded as teaching tools that had a positive effect on students’ vocabulary learning. The study by Mashhadi Heidar and Kaviani (2016) also confirmed that learning English through the use of Telegram can have unique technological and pedagogical advantages for Iranian EFL learners and could have a positive effect on the development of EFL learners’ vocabulary gain.
The present study was a similar attempt to determine the impact of Telegram, as an available social network, on learning L2 vocabulary by Iranian EFL beginners. To achieve the purposes of this study, the following research questions were posed:
1. Does using Telegram have any effect on teaching vocabulary to Iranian EFL beginners?
2. Do EFL learners have positive attitudes towards using Telegram in order to learn English vocabulary?
Methodology
Design of the Study
Based on the purposes of study, the blue print of the procedure was a quasiexperimental research design to investigate the effect of using Telegram on teaching vocabulary by Iranian EFL beginners. Measures were also taken to elicit the learners’ attitudes towards using Telegram for the purpose of vocabulary learning.
Participants
The participants of this study were 31 female students at an English Language Institute, Isfahan, Iran, in 2016 and their age ranged from 10 to 14. Their native language was Persian. They had roughly the same educational background, and were selected nonrandomly through availability sampling. Table 1 demonstrates the characteristics of the participants.
Table 1. Demographic Background of the Participants
No. of Students 
31 Learners at a Language Institute 
Gender 
All Females 
Native Language 
Persian 
Target Language 
English 
Age 
1014 
Proficiency Level 
Beginner 
Academic Years 
2016 
Research Instruments
For the data collection, different instruments were designed: a pretest, a posttest,and an attitude questionnaire, not to mention a Placement Interchange Test, which was administered to the learners at the outset of the study to make sure they were homogeneous in terms of language proficiency before the treatment. Then, the pretest was given to the learners, the aim of which was to make sure the learners were not familiar with the target words which were to be taught during the instructional period. The test consisted of 40 multiplechoice questions, and it was approved by 3 university professors for validity concerns. The KR20 reliability of the test had also been established (r = .78) with a group of 10 learners similar to the learners of the current study.
On the last day of the project, a multiple choice posttest, parallel to the pretest, was used to test the participants after instruction. The aim of this test was to find out how many words were learnt in the online class via Telegram and how many words were learnt from books in the classroom. The posttest could reveal the possible effect of the social media on the vocabulary learning of the second language. The validity and reliability issues of the posttest were addressed by the researchers as well; the validity of the test was approved by a panel of experts in ELT and its KR20 reliability was found to be .81.
The attitude questionnaireconsisted of 15 Likertscale items about the learners’ attitudes towards using Telegram for learning second language vocabulary out of classroom. All of the 31 participants of this study completed the questionnaire on the last session of the classroom after having been taught with Telegram. The statements in the questionnaire were very simple, short, clear, and easy to understand for the participants. Yet, the researchers were present in the class and ready to help the learners in case misunderstanding occurred. The Cronbach alpha coefficient for the reliability of the questionnaire was .85.
Data Collection Procedure
The main goal of this study was to investigate the impact of using Telegram on learning vocabulary by EFL beginners. At the outset, the teacher gave the learners an Interchange Placement Test to measure their level of proficiency and to ensure their homogeneity in this regard. The teacher also administered a pretest, which aimed to check whether the target words to be taught to the learners were new and unfamiliar to them. Teacher’s observation and interview with students showed that they were familiar with Telegram and they used it regularly. In the course of the experiment, the teacher taught a number of vocabulary items in classroom every session, and some other words in Telegram as the online instruction and practice. At the end of the instructional period, the teacher gave the learners a posttest, which was intended to compare the learners’ knowledge of words taught in class and those taught through Telegram. Finally, the attitude questionnaire was given to the learners to unearth their perceptions of using Telegram for vocabulary learning purposes.
In order to analyze the data collection, a pairedsample t test in SPSS (version 21) was employed. This statistical test was conducted because a comparison was to be made between two sets of scores (i.e. scores of Telegramtaught words and those of traditionally – taught words) from one and the same group of language learners. Prior to conduction of pairedsample t test, its assumption was checked: in order to check the normality assumption, Kolmogorov – Smirnov was applied twice: once to assure the normality of distribution of scores of telegramtaught words, and scores of traditionallytaught words. Finally, frequency counts, percentage, and one sample t test were used to analyze the data collected by the questionnaire. These results of data analyses are presented in the following sections.
Results
Results of the Placement Test
The learners who took part in this research were all studying at the beginner level in the institute where they were learning English. However, to ensure their homogeneity and to doublecheck their being beginners, measures were taken to calculate the descriptive statistics of their placement test scores and to identify the mean score. The sample of the study were those whose scores ranged between one standard deviation above and below the mean. Because a single group of EFL learners was recruited as the participants of the study, no inferential statistics would apply here. Table 2 presents the descriptive statistics.
Table 2. Descriptive Statistics of the Placement Test

N 
Minimum 
Maximum 
Mean 
Std. Deviation 
Skewness 
Kurtosis 
Placement Test 
31 
2.00 
8.00 
4.51 
3.69 
.002 
1.28 
The number of participants (N = 31), the mean score (M = 4.51), standard deviation (SD = 3.69), and other descriptive statistics of the placement test are shown in the above table. As illustrated, the learners with a score between 2.00 and 8.00 were selected as beginners to take part in the current investigation. Also, the skewedness value (which provides an indication of the symmetry of the distribution) and the kurtosis value (which provides information about the peakedness of the distribution) are presented in Table 2. If the distribution is perfectly normal, a skewedness and kurtosis value of 0 would be obtained. Here, the skewedness value was a very small negative one (.002), which indicated a slight clustering of scores at the high righthand end of the distribution (negatively skewed), and the kurtosis value (1.28) indicated that the distribution was not peaked, but rather slightly flat.
Results of Pretest and Posttest
The first research question of the study was: Does using Telegram have any effects on learning vocabulary by Iranian EFL beginners? On the vocabulary posttest, the Telegramtaught vocabulary scores of the learners and the traditionallytaught vocabulary scores of them had to be compared by means of a pairedsamples t test. However, before conducting the t test, measures had to be taken to assure that no violation of the assumptions underlying t test (i.e., random sampling, independence of observations, normality of the distributions, and interval data) were happening. Of these assumptions, the one which could be tested statistically was normality. Thus, descriptive statistics and KolmogorovSmirnov test were checked once for the scores of the Telegramtaught words, and once for traditionallytaught words. The results are presented in the following tables and figures. The descriptive statistics of the scores obtained for the traditionally taught words are first presented here. Then, the descriptive statistics of the scores obtained from the Telegram taught words are provided. Finally, a comparison was made between the two sets of scores. Table 3 shows the descriptive statistics of the scores of the words taught through the traditional approach.
Table 3. Descriptive Statistics for the TraditionallyTaught Words
TraditionallyTaught Words 
Statistic 

Mean 
15.38 

95% Confidence Interval for Mean 
Lower Bound 
14.27 
Upper Bound 
16.49 

5% Trimmed Mean 
15.50 

Median 
15.00 

Variance 
9.11 

Std. Deviation 
3.01 

Minimum 
8.00 

Maximum 
20.00 

Range 
12.00 

Interquartile Range 
4.00 

Skewedness 
.37 

Kurtosis 
.16 
It could be seen in Table 3 that the mean score of the traditionallytaught words was 15.38 and the trimmed mean was 15.50. The trimmed mean is calculated by excluding the high and low extreme scores and calculating a new mean for the distribution. A small difference between the mean and the trimmed mean indicates that there were no extremes scores, or that those extreme scores could not influence the mean score to a large extent.
In Table 3, the median was shown to be 15.00, and the standard deviation of the distribution equaled 3.01. It could be seen that the minimum score was 8.00, while the maximum score was 20.00, and thus the range equaled 12.00. Finally, skewedness and kurtosis values of .37 and .16 were obtained, which mean the distribution was a bit negatively skewed, and it was not peaked. The distribution, thus, must have been normal. The normality of the distribution was checked by a more robust statistical test (i.e. KolmogorovSmirnov), the results of which are shown in Table 4.
Table 4. KolmogorovSmirnov Results for the TraditionallyTaught Words

KolmogorovSmirnov 
ShapiroWilk 

Statistic 
df 
Sig. 
Statistic 
df 
Sig. 

Traditional 
.09 
31 
.20 
.96 
31 
.41 
The Sig. value under the KolmogorovSmirnov part of the table (i.e., .20) represented a value higher than .05, which indicates that the distribution of scores for the traditionallytaught words was normal. This normality of the distribution is also evident in the histogram in Figure 1.
Figure 1. Histogram Showing the Distribution of Scores for the TraditionallyTaught Words.
As Figure 1 shows, the distribution of the scores represented graphically via the histogram was not far from a normal distribution. So, the distribution of the traditionallytaught words did not violate the normal distribution assumption of the t test. Similar steps were taken to check whether the distribution of scores for the Telegramtaught words was also normal or not. Table 5 presents the descriptive statistics of the scores of the words taught
Table 5. Descriptive Statistics Results for the TelegramTaught Words
TelegramTaught Words 
Statistic 

Mean 
17.54 

95% Confidence Interval for Mean 
Lower Bound 
16.62 
Upper Bound 
18.47 

5% Trimmed Mean 
17.75 

Median 
18.00 

Variance 
6.32 

Std. Deviation 
2.51 

Minimum 
10.00 

Maximum 
20.00 

Range 
10.00 

Interquartile Range 
4.00 

Skewness 
1.02 

Kurtosis 
.96 
Table 5 shows that the mean score of the Telegramtaught words was 17.54 (which was greater than that of the traditionallytaught words, that is, 15.38) and the trimmed mean was 17.75. Furthermore, the median was 18.00, and the standard deviation of the distribution was found to be 2.51. The minimum score in this distribution was 10.00, while the maximum score was 20.00, and thus the range equaled 10.00. Moreover, the skewedness and kurtosis values of 1.02 and .96 were obtained, which means that the distribution was negatively skewed, and it was rather peaked. To see if the distribution was normal or not, KolmogorovSmirnov test was also run.
Table 6. KolmogorovSmirnov Results for the TelegramTaught Words

KolmogorovSmirnov 
ShapiroWilk 

Statistic 
df 
Sig. 
Statistic 
df 
Sig. 

Telegram 
.17 
31 
.02 
.86 
31 
.001 
In Table 6, the Sig. value under the KolmogorovSmirnov part of the table (i.e., .02) showed a value lower than .05, which indicates that the distribution of scores for the Telegramtaught words was not normal. The abnormality of the distribution is also graphically represented in the histogram in Figure 2.
Figure 2. Histogram Showing the Distribution of Scores for the TelegramTaught Words
Figure 2 illustrates that the distribution of the scores for the Telegramtaught words was far from normal, which means this distribution violated the normal distribution assumption of the t test. Thus, instead of a pairedsamples t test, its nonparametric counterpart (i.e. Wilcoxon Signed Rank Test) was conducted to compare the learners’ traditionallytaught vocabulary scores with Telegramtaught vocabulary scores.
As it was mentioned above, Wilcoxon Signed Rank Test was used to compare the learners’ traditionallytaught vocabulary scores and Telegramtaught vocabulary scores. The results of this test are presented in Table 7.
Table 7. Wilcoxon Signed Rank Test Results of the TraditionallyTaught and TelegramTaught Word Scores

Telegram  Traditional 
Z 
4.23 
Asymp. Sig. (2tailed) 
.000 
In Table 7, the p value in front of the Sig. (2tailed) row was found to be less than the significance level (.000 < .05), which means that there was a statistically significant difference between the two sets of scores. Accordingly, it could be concluded that the scores for the Telegramtaught words (M = 17.54) were significantly higher than the scores for the traditionallytaught words (M = 15.38). In other words, the Telegram application was shown to be a useful resource for enriching the vocabulary knowledge of the Iranian female EFL beginner learners. This obtained results is also graphically represented in Figure 3.
Figure 3. The Mean Scores for TelegramTaught and TraditionallyTaught Words
Figure 3 also shows the superiority of the scores of the Telegramtaught word to those of the traditionallytaught words.
Results of Attitude Questionnaire
A 15item researchermade questionnaire was employed to find an answer to the second research question. The results obtained from the questionnaire are shown in Table 8.
Table 8. Results of the Attitude Questionnaire
No. 
Statements 
F/ P 
Strongly agree 
Agree 
No opinion 
Disagree 
Strongly disagree 
Mean 
1 
Telegram helps me understand the lexical points better. 
F P 
12 38.70 
11 35.48 
6 19.35 
1 3.22 
1 3.22 
4.03 
2 
Telegram offers opportunities for more effective vocabulary practice. 
F P 
14 45.16 
12 38.70 
2 6.45 
2 6.45 
1 3.220 
4.16 
3 
Telegram offers flexibility in vocabulary learning. 
F P 
9 29.03 
11 35.48 
4 12.90 
5 16.12 
2 6.45 
3.64 
4 
Telegram allows me to have control over my vocabulary learning 
F P 
8 25.80 
11 35.48 
8 25.80 
3 9.67 
0 0 
3.80 
5 
Telegram motivates me to find out and discover more vocabulary. 
F P 
7 22.58 
14 45.16 
4 12.90 
5 16.12 
1 3.22 
3.67 
6 
Learning vocabulary via Telegram is a valuable extension of the classical learning methods. 
F P 
6 19.35 
15 48.38 
6 19.35% 
3 9.67 
0 0 
3.80 
7 
Learning vocabulary in a Telegram environment is enjoyable and amusing. 
F P 
11 35.48 
13 41.93 
1 3.22 
4 12.90 
2 6.45 
3.87 
8 
Learning vocabulary in telegram can be managed in a better way. 
F P 
10 32.25 
15 48.38 
6 19.35 
0 0 
0 0 
4.12 
9 
Learning vocabulary via Telegram is interesting. 
F P 
11 35.48 
14 45.16 
5 16.12 
1 3.22 
0 0 
4.12 
10 
Learning vocabulary via Telegram motivating to discover more lexical points. 
F P 
19 61.29 
7 22.58 
2 6.45 
2 6.45 
0 0 
4.43 
11 
Learning vocabulary via Telegram makes me more proficient. 
F P 
8 25.80 
6 19.35 
15 48.38 
1 3.22 
1 3.22 
3.61 
12 
Learning vocabulary in a Telegram environment creates less anxiety for me. 
F P 
6 19.35 
13 41.93 
10 32.25 
2 6.45 
0 0 
3.74 
13 
In Telegram, I feel less inhibited when working on vocabulary 
F P 
8 25.80 
11 35.48 
8 25.80 
2 6.45 
2 6.45 
3.67 
14 
I am satisfied with application of Telegram 
F P 
13 41.93 
7 22.58 
10 32.25 
1 3.22 
0 0 
4.03 
15 
I recommend the use of Telegram in future vocabulary courses. 
F P 
7 22.58 
12 38.70 
8 25.80 
4 12.90 
0 0 
3.70 
In this questionnaire, as it could be seen, all the mean scores of the questionnaire items were above 3.00 (which is the average value of the choices when strongly agree receives 5 and strongly disagree receives 1). This means that the learners agreed with all the questionnaire items, which were about the positive attributes and effects of Telegram. The highest mean scores in Table 8 belonged to items # 10 (M = 4.43), 2 (M = 4.16), 8 (M = 4.12), 9 (M = 4.12), and 14 (M = 4.03), in which the learners express their agreement with the statements claiming that (a) learning vocabulary through Telegram motivated the learners to find out more lexical points, (b) Telegram offered opportunities for more vocabulary practice, (c) learning vocabulary in a Telegram environment could be managed in a better way, (d) learning vocabulary in a Telegram environment was interesting, and (e) the learners were satisfied with the application of Telegram in vocabulary classes. In the same vein, all the other items received the learners’ agreement. To see whether the degree of this agreement was statistically significant or not, a onesample t test was conducted. This statistical tool compares the mean score of a distribution against a constant (which was 3.00 in this analysis since the choices in the Likertscale questionnaire ranged from 1 to 5 and the average value of the choices was 3.00). Table 9 shows the results of descriptive statistics performed for this purpose.
Table 9. Descriptive Statistics for Learners’ Attitude Scores
N 
Mean 
Std. Deviation 
Std. Error Mean 

Attitude Questionnaire 
15 
3.89 
.24 
.06 
The overall attitude mean score of the learners was found to be 3.89, which was larger than 3.00. This implies that, as stated above, the learners’ overall attitudes towards the use of Telegram for learning L2 vocabulary was positive. To find out whether this positive attitude was of statistical significance or not, the p value under the Sig. (2tailed) column in the onesample t test was checked (see Table 10).
Table 10. OneSample t Test Results for the Learners’ Attitude Scores
Test Value = 3 

t 
df 
Sig. 
Mean Difference 
95% Confidence Interval of the Difference 

Lower 
Upper 

Questionnaire 
14.26 
14 
.000 
.89 
.75 
1.02 
Table 10 shows that there was a statistically significant difference between the learners’ mean attitude score (M = 3.89) and the average value of the choices (that is 3.00) because of the fact that the p value was smaller than the specified level of significance (.000 < .05). Consequently, it could be concluded that the degree of the learners’ positive attitude towards the application of Telegram in English classes, especially for the purpose of learning L2 vocabulary, was statistically significant. In other words, the application of Telegram in order to learn English vocabulary was found to be to the learners’ taste.
Discussion
The main purpose of this study was to investigate the effect of Telegram, as an available social network in Iran with many useful options such as different stickers or transferring data quickly, on learning vocabulary of Iranian EFL beginners. The findings obviously confirm this fact that there was a remarkable difference between two sets of scores of two different test methods. Based on the findings, it could be inferred that the scores for the Telegramtaught words (M = 17.54) were significantly higher than that of traditionally taught words (M = 15.38).
In other words, the Telegram application was an effective and useful resource for enriching the vocabulary knowledge of the Iranian female EFL beginners. The previous studies related to using social networks such as Telegram, Facebook, Tweeter are in line with these results. The results support Selwyn (2003) who mentioned ICT is being used in education to help students to learn more effectively and help teachers to do administrative tasks more efficiently. In addition, these findings are also in agreement with Van der Beemt, Akkerman, and Simons (2010); Boyd (2007); Lomicka and Lord, (2011); Yapici and Hevedanli (2014) that stated younger people spend a considerable portion of their daily life interacting through social media; these sites have become an important part of most students’ lives. The results of the present study are also in line with those of Ghaemi and Golshan (2017).
Moreover, the research results support the finding of Jones, Blackey, Fitzgibbon, and Chew (2010) which mentioned that social networks can develop formal learning, and become part of the educational environment of students. The results are in line with findings of Chalak (2017), Kabilan (2010) and Girgin (2011) since they demonstrated that social networks such as Facebook, Telegram, and Tweeter could be utilized as an online situation to facilitate the learning different English language skills. Based on the findings, Telegram can enhance interaction of students out of the classroom and it can help teachers and students by creating a private, friendly, fun, simple, interesting and comprehensive relationship between teachers and students out of the classroom.
Conclusion
Social networks have important role in our today’s world. Vocabulary is an essential part in learning second language as English. It is necessary for second language learners to learn vocabulary; thus, English teachers and researchers have been looking for a new educational method for teaching vocabulary to the students. The results of this study and pervious research in this field show that social networks such as Telegram, with many options, can be an effective tool to motivate students to learn English. Beginners can improve number and level of vocabulary via Telegram as a tool out of classroom by different stickers pack and also pictures.
It is noteworthy that these kinds of tools can help teachers and students for extra learning, and help them communicate. Students can have unlimited exercise in Telegrams groups at any time and in any place, they are not limited just to the classroom settings for learning. Based on the questionnaire, learners had positive attitudes to use social networks for learning second language out of the classroom and they believed that these useful tools facilitate learning procedure.
This study could provide beneﬁts for the students in terms of developing vocabulary via Telegram or any other application. They can gain a better comprehension of the material by working with such an application. Less proficient students could get motivation to work. The findings of the present study could also be beneficial for EFL practitioners as well as curriculum designers to take these issues into high priority in teaching and learning programme at any level of education. Iranian teachers could also employ applications to improve reading comprehension of their students in the classroom. However, more studies in different settings or contexts using different variables are required to support and confirm the findings of this study in Iranian educational environment.