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Chinese University Students’ Experience of WeChat-Based English-Language Vocabulary Learning

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The outbreak of COVID-19 worldwide in 2020 has posed tremendous challenges to higher education globally. Teaching English as a foreign language (TEFL) is among the many areas affected by the pandemic. The unexpected transition to online teaching has increased challenges for improving and/or retaining students’ language proficiency. WeChat, a popular social application in China, was widely used for TEFL at Chinese universities before COVID-19. However, it remains unclear whether the use of WeChat can facilitate Chinese university students’ English-language lexical proficiency during the pandemic. To fill this gap, the aim of the present study was two-fold: (1) it initially explored the relationship between the variables including students’ academic years, genders, and academic faculties/disciplines, and their lexical proficiency; and (2) it evaluated the effectiveness of a WeChat-assisted lexical learning (WALL) program in facilitating learning outcomes of English-language vocabulary. One hundred and thirty-three students at a university in Northern China participated in the WALL program for three weeks. As the results indicated, the independent variables had no correlation with the students’ lexical proficiency. More importantly, the students had a decline in the test scores after using the program, compared to their initial test scores. Moreover, the difference was reported to be medium. The findings further proposed questions on applying WeChat to vocabulary teaching in a large-scaled transition. The study is expected to provide insights for tertiary institutions, language practitioners, and student stakeholders to troubleshoot the potential problems regarding implementing WeChat-based TEFL pedagogies.
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Chinese University Students’ Experience of WeChat-Based
English-Language Vocabulary Learning
Fan Li 1, *, Si Fan 1, Yanjun Wang 2and Jinjin Lu 1
Citation: Li, F.; Fan, S.; Wang, Y.; Lu,
J. Chinese University Students’
Experience of WeChat-Based
English-Language Vocabulary
Learning. Educ. Sci. 2021,11, 554.
Academic Editors:
Marija Kuzmanovi´c and
Dragana Makaji´c-Nikoli´c
Received: 20 August 2021
Accepted: 14 September 2021
Published: 17 September 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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Copyright: © 2021 by the authors.
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1School of Education, College of Arts, Law and Education, University of Tasmania, Newnham Campus,
Newnham, TAS 7248, Australia; (S.F.); (J.L.)
2School of Humanities, College of Arts, Law and Education, University of Tasmania, Newnham Campus,
Newnham, TAS 7248, Australia;
The outbreak of COVID-19 worldwide in 2020 has posed tremendous challenges to higher
education globally. Teaching English as a foreign language (TEFL) is among the many areas affected
by the pandemic. The unexpected transition to online teaching has increased challenges for improving
and/or retaining students’ language proficiency. WeChat, a popular social application in China, was
widely used for TEFL at Chinese universities before COVID-19. However, it remains unclear whether
the use of WeChat can facilitate Chinese university students’ English-language lexical proficiency
during the pandemic. To fill this gap, the aim of the present study was two-fold: (1) it initially
explored the relationship between the variables including students’ academic years, genders, and
academic faculties/disciplines, and their lexical proficiency; and (2) it evaluated the effectiveness
of a WeChat-assisted lexical learning (WALL) program in facilitating learning outcomes of English-
language vocabulary. One hundred and thirty-three students at a university in Northern China
participated in the WALL program for three weeks. As the results indicated, the independent
variables had no correlation with the students’ lexical proficiency. More importantly, the students had
a decline in the test scores after using the program, compared to their initial test scores. Moreover, the
difference was reported to be medium. The findings further proposed questions on applying WeChat
to vocabulary teaching in a large-scaled transition. The study is expected to provide insights for
tertiary institutions, language practitioners, and student stakeholders to troubleshoot the potential
problems regarding implementing WeChat-based TEFL pedagogies.
mobile-assisted language learning; English-language vocabulary learning; WeChat;
Chinese higher education
1. Introduction
The COVID-19 pandemic has caused massive disruptions to educational institutions
worldwide. With the advent of COVID-19 control measures, China mandated the nation-
wide school closures at the end of February 2020 [
]. An emergency policy launched by the
Chinese government—“Suspending Classes without Stopping Learning”—shifted higher
education online [
]. This initiative minimized the impact of the pandemic on education [
and ensured that learning was not disrupted at any point during the lockdown [
]. The
online or web-based teaching, served as a substitute for the traditional way of classroom
teaching, had been the mainstream mode of delivering teaching since late February 2020 [
Due to China’s successful control measures, by June 2020, a large number of university
students were able to return to campuses, resuming their studies in a blended mode [6].
Mobile learning (m-learning) refers to the phenomenon that mobile devices are ap-
plied for learning purposes [
]. As an integration of m-learning and language learning,
mobile-assisted language learning (MALL) assists or enhances language learning in formal
and/or informal environments, using handheld mobile devices, such as mobile phones or
smartphones [
]. In spite of the widely recognized advantages of using MALL, there were
Educ. Sci. 2021,11, 554.
Educ. Sci. 2021,11, 554 2 of 12
reports on unequal learning performances of some university students during the transi-
tion [
]. This calls for an investigation on MALL in the current global crisis context [
To address this gap, the present study aims to explore the factors influencing the lexical
learning outcomes in the transition. It also evaluates the effectiveness of a WeChat-based
MALL program used for English-language vocabulary learning at a Chinese university
during the pandemic.
Numerous challenges during the pandemic, including technology accessibility, In-
ternet connectivity, socioeconomic status, institutional supports, and learner experience,
have left MALL effects uncertain [
]. It also remains questionable about the adoption of
MALL-based pedagogies in higher educational systems in developing countries, such as
China [
]. This particular study thus initially unveils the potential factors that might have
impacts on students’ English-language vocabulary proficiency. It also unpacks the effec-
tiveness of equipping Chinese university students with a WeChat-based MALL program
for English-language vocabulary learning under current circumstances.
To address the research objectives mentioned above, the paper initially unpacks related
work primarily on MALL effects and WeChat-based language learning/teaching practices
at Chinese tertiary levels, especially during the pandemic. It then presents that a WeChat-
based MALL program which was used as an intervention. Next, the paper clarifies that
the quantitative data were collected by using two sets of English-language vocabulary
tests. Additionally, the test results were analyzed by using the Mann–Whitney U tests
and Kruskal–Wallis tests to determine the relationships between the students’ tests scores
and several independent variables, such as their academic years, genders, and academic
faculties/disciplines. The paired-sample t-test was then used to find out the statistically
different significance between the test scores. The effect size was applied to further measure
the difference, if there truly was a significance reported. Finally, the paper sheds light on a
number of potential reasons that attributed to the findings.
2. Related Work
Studies of MALL did not receive as much attention as its wide prevalence in academia
during the period of COVID-19 [
]. Nevertheless, m-learning has been a preferred
learning manner for online language teaching practices during the pandemic [
studies at the higher educational level focused primarily on participants’ perceptions. For
example, [
] surveyed 100 students and 45 teachers’ reactions to the transition to mobile
Russian language learning. Likewise, Chinese students held supportive attitudes that
MALL approaches have made learning easier in their university English course particularly
during the lockdown [
]. However, these studies mainly explored students and educators’
beliefs and opinions on transforming to MALL paradigms rather than investigating its
practical effects. Other studies, such as [
] and [
], analyzed affordances of implementing
QQ and WeChat as MALL tools for university English courses facing the transition. The
authors, however, largely put forward pedagogical implications of designing and applying
MALL-based TEFL models, instead of measuring the actual effectiveness. Another study
evidenced that voluntary out-of-class MALL has enhanced outcomes of learning French
language by self-regulated training and scaffolding [
]. However, the study did not clarify
whether the experiments were conducted in the transition and did not calculate the effect
size of the treatment.
Shown as above, evaluating actual MALL effects on TEFL considering the pandemic
situation was somehow overlooked, especially in the Chinese university context. For
example, one most recent study reported mobile multimedia tools, including laptops or
tablets, had positive impacts on Chinese university students’ English learning outcomes
under current situations [
]. However, the findings did not specify the facilitated language
teaching area(s). As the most researched language-teaching area in MALL studies [
mediated vocabulary learning outcomes have been investigated by using quantitative,
qualitative, and/or mixed-methods approaches in a wealth of studies in the literature
before the COVID-19 period [
]. Nevertheless, evaluating mobile-assisted vocabulary
Educ. Sci. 2021,11, 554 3 of 12
learning effects in Chinese universities during the transition received seemingly little
emphasis. Moreover, little empirical evidence scrutinized the effectiveness of specific
media/platforms on MALL, such as WeChat.
WeChat has gained a high popularity in Chinese universities during the pandemic [
However, little research has explored using WeChat for university MALL practices amid
the pandemic. One existing evidence is WeChat has developed students’ enjoyment
through emotion regulation in online collaborative English writing learning activities [
However, MALL on lexical proficiency has been sparse in the transition. Ref. [
] claimed,
factors including user groups, academic contexts, and applications in use, largely attributed
to successful MALL practices, particularly in vocabulary learning/teaching. Therefore,
when evaluating the MALL effects, it is necessary to consider the factors mentioned above,
because they potentially led to students’ uneven MALL outcomes during the pandemic [
3. Research Aim and Questions
As previously discussed, the effectiveness of applying WeChat to vocabulary learning
in the chosen context remains under-researched. To bridge this gap, 133 students at a North-
ern Chinese university participated in a WeChat-assisted lexical learning (WALL) program
for a period of three weeks. The study addresses the following research questions (RQs):
RQ One: Do students’ academic years, genders, and academic faculties/disciplines
have impacts on their English-language vocabulary learning outcomes?
RQ Two: How well does the WALL program assist students to achieve their English-
language vocabulary learning outcomes?
4. Research Method
4.1. Research Design
The reported study is under a larger scope PhD project. The research method un-
derpinning the PhD project is a mixed-methods research design, including open-ended
questionnaires, tests, and semi-structured interviews. This reported study was designed
as the pilot study. It primarily focused on the quantitative data collected through two
sets of vocabulary test papers, These tests included a Diagnostic Test administered before
the WALL program and a Follow-Up Test administered afterwards. Differences between
students’ test scores collected before and after using the program were examined. The
effect size was also calculated to determine whether the difference was large, medium,
or small. The program duration was about three weeks, from 24 May to 21 June. Such a
length of duration was decided, due to the fact that intervention or treatment shorter than
one month was conducive to learners’ improvement [
]. For longer programs, fatigue
might appear [27], due to the novelty effect [28].
4.2. Participants
A purposive sampling strategy was applied to select the subject academic institution,
sample faculties/disciplines, and sample participants in this study. Such a method was
efficient in terms of time and effort saving, and suitable for a case study, as well as well-
serving the research objectives [
]. Firstly, the university where the study was conducted
is a key and comprehensive academic university in Northern China. University students
were purposively chosen as the sample, because they are physically and mentally mature
enough and are able to manage and discipline their learning tasks and pace [
]. Learning
in universities is more independent than at other educational levels [
]. Besides, since
this study was conducted via the WeChat public account on mobile phones, China has
a large population of mobile phone users at universities [
]. Secondly, the selection
of faculties/disciplines was also purposive. Among the total 24 faculties/disciplines,
four faculties/disciplines were purposively selected as the representation, including the
School of Architecture, School of Chemistry, School of Information Technology, and School
of Media and Communication. Thirdly, the Year 1 and Year 2 students from the four
faculties/disciplines were purposively chosen, because non-English majors at most Chinese
Educ. Sci. 2021,11, 554 4 of 12
universities usually finish studying English courses at the end of Year 2. Initially, 150
students were recruited in a nonrandomized way [
]. Regarding the recruitment process,
an invitation letter was posted on the websites of the faculties/disciplines after receiving
the approval. Students volunteered for this study were contacted by the researchers via
emails. Potential participants in naturally-formed classes were chosen as a convenience
sample without randomization [
]. They were then informed of more details regarding
this study before they made the final decision on their voluntary participation.
4.3. Demographic Backgrounds of the Participants
Totally, 150 students took the two sets of vocabulary tests online. One hundred and
thirty-three submitted and completed both tests. Details of the students’ demographic
background information are presented in Table 1.
Table 1. Students’ background information.
Background Information % (n/N)
Academic faculties/disciplines/schools
Information Technology
Media and Communication
29.3 (39/133)
16.5 (22/133)
36.1 (48/133)
18.0 (24/133)
Female 75.9 (101/133)
24.1 (32/133)
Academic years
Year 1
Year 2 57.9 (77/133)
42.1 (56/133)
4.4. Research Instruments
The WeChat-based MALL WALL program
The WALL program was designed and developed by the authors for university English
vocabulary learning, including learning content and materials, daily practice and drills,
and additional learning resources. The delivered content consisted of texts, audios, and
video clips, covering a wide range of knowledge regarding a particular location, such as
natural landscape, wildlife, social life, lifestyles, architectures, and cultures. The program
was delivered through the public account service of WeChat. Students received daily
notifications from the public account via WeChat the APP on mobile phones.
The English-language vocabulary test papers
Two sets of English-language vocabulary tests were used as the measurement tool.
They were administered and collected online. In Phase 1, the Diagnostic Test, circulated
before the program, was used to identify the students’ initial lexical knowledge. The test
paper had 25 multiple-choice questions. Each question contained one target lexical item
and three distractor lexical items as interferences. Eighty lexical items were words and
20 were phrases. The lexical items were all extracted from the latest formal test papers of
the College English Test: Band-4 (CET-4) (Retrieved from
/folder/1608/1178-1.htm accessed on 19 May 2020) and selected based on the English
teaching syllabus at the university. For example, “The college students in China are ____from
smoking on campus because this will do them no good. A. discouraged B. observed C. obeyed D.
obtained”. Another example was “After talking for nearly ten hours, he ____to the government’s
pressure at last. A. expressed B. yielded C. decreased D. approved”. In Phase 2, the Follow-Up
Test, administered after the program, was used to examine the students’ mediated lexical
Educ. Sci. 2021,11, 554 5 of 12
knowledge. The test paper was designed at the same way as the former one. The lexical
items in this paper were all based on the learning materials and content delivered in the
program. One example was as follows: The wombat is a large ____found only in Australia.
A. carnivore B. arthropod C. marsupial D. reptile”. Another example was as follows: The
town of Binalong Bay is ____the southern end of the beautiful Bay of Fires. A. next to B. situated
at C. near D. far from”. All lexical items were carefully selected according to the lexicon
requirements in the College English Curriculum Requirements [
], compulsory active lexical
items [
], and word frequencies [
]. Moreover, three experienced English-language
teachers from the university were consulted to check all the target lexical items online
before the program commenced. The practical relevance and difficulty levels of the lexical
items were also verified.
Vocabulary tests were used as the test instrument to measure students’ language
proficiency and served as a vocabulary learning outcome indicator in this study for the
following reasons. Firstly, vocabulary is the foundation of any language, and vocabulary
education is a vital link in the chain of language acquisition [
]. Lexical ability is regarded
more useful in English learning and teaching objectives [
] and plays a critical role
in students’ future careers in China [
]. Secondly, the glossary in the College English
Curriculum Requirements is taken as a testing standard for lexical knowledge and as the
criterion and norm reference for university English-language lexicons [
]. Thirdly, tests,
as a comparatively easy and labor-saving way, can measure learners’ vocabulary skills
efficiently and accurately [42].
4.5. Data Analysis
The collected data were analyzed by using the Statistical Packages for Social Science
(SPSS) version 26.0. Mean values and standard deviation of the students’ test scores were
firstly calculated for descriptive statistical analyses. Next, Mann–Whitney U tests were
conducted to determine whether there were statistically significant differences between the
students’ test scores and independent variables, including academic years and genders.
Then, Kruskal–Wallis tests were applied to examine whether significant differences existed
between their test scores and academic faculties/disciplines. Finally, a paired-sample
t-test was adopted to explore statistically significant differences between the two sets of
test scores, namely the Diagnostic Test and the Follow-Up Test. If the significance value
of the paired-sample t-test is smaller than 0.05, it shows that a significant difference is
found between the students’ scores on the two different tests. This indicated the WALL
program has had an impact on the students’ lexical proficiency. Otherwise, it can be said
that no significance existed between the two sets of the test scores. Hence, the WALL
program had no impact on the students’ lexical proficiency. Subsequently, the effect size
was calculated to measure whether the difference was large (d = 0.80), medium (d = 0.50),
or small (d = 0.20), using Cohen’s d [43], if there is any significance.
5. Results
5.1. Findings to Research Question One
5.1.1. Descriptive Analysis of the Test Results
The descriptive results of the two tests are first presented in this section. Table 2shows
the results of the Diagnostic Test and the Follow-Up Test, respectively. It can be seen that
the mean value of the Diagnostic Test was 44.32, while the mean value of the Follow-Up
Test was 31.10. The mean values above showed that overall, the students failed both tests,
since 60 points (out of 100) is generally regarded as a passing score in China. A lower
extent of statistical dispersions was also found on the individual participants’ test scores on
the Follow-Up English Test (standard deviation = 14.147) than the ones on the Diagnostic
Test (standard deviation = 22.835). It can be claimed that despite a lower variation existed
among the students’ Follow-Up Test scores, their test performance was less satisfying as
a whole. Additionally, according to the frequencies of the test scores from Table 3, 28.6%
N = 38
) of the 133 students successfully passed the Diagnostic Test by scoring greater than
Educ. Sci. 2021,11, 554 6 of 12
or equal to 60 points, while only 3.8% (
) successfully passed the Follow-Up Test. A
decline was found in the students’ vocabulary proficiency test scores. That is to say, the
students scored less after using the program than before they used it.
Table 2. Descriptive analysis of the two test scores.
N Min Max Mean Standard Deviation
Diagnostic Test 133 14 94 44.32 22.835
Follow-up Test 133 8 96 31.10 14.147
Table 3. Frequencies of the two test scores.
Diagnostic Test Follow-Up Test
Test Scores
(Full Score: 100 Points)
(N = 133)
(N = 133)
91–100 4 3 2 1.5
81–90 9 6.8 0 0
71–80 13 9.8 1 0.8
60–70 12 9.0 2 1.5
Below 60 * 95 71.4 128 96.2
* Below 60 means failing the test.
5.1.2. Analysis of the Two sets of Test Scores by Independent Variables
Analysis of the Test Scores by Academic Years
Firstly, the Year 2 students had a higher average score on the Diagnostic Test (mean
value = 47.11) than the Year 1 cohort (mean value = 42.30). It was because the Year
2 students, in most cases, had more exposure to vocabulary learning and teaching than
their Year 1 counterparts. However, neither group scored greater than or equal to 60 points
on average. Next, a Mann–Whitney U test indicated no statistically significant difference
between the students’ Diagnostic Test scores and their academic years, since the p-value
(0.372) was larger than 0.05. Secondly, the Year 1 students had better performances on
the Follow-Up Test (mean value = 32.26) than the Year 2 group (mean value = 29.50). The
Year 1 students could be more likely to follow the learning requirements. They could
possibly be more hardworking and maintain better learning effectiveness, since they had
not graduated from high schools for long. However, neither group scored greater than
or equal to 60 points. Then, a Mann–Whitney U test presented no statistically significant
difference was found between the students’ Follow-Up Test scores and their academic
years, since p-value = 0.098, which was larger than 0.05. It could be stated that the students’
academic years have had no impacts on the two sets of test scores.
Analysis of the Test Scores by Genders
Firstly, the female students had a much higher score on the Diagnostic Test (mean
value = 64.56) than their male counterpart (mean value = 43.61). The female students
passed the test on average, while the male students did not do well. Female students
generally showed better language learning outcomes and greater devotion to schooling.
Next, a Mann–Whitney U test found no statistically significant difference existed between
the students’ Diagnostic Test scores and their genders, since the p-value (0.457) was larger
than 0.05. Secondly, the female students had a much higher score on the Follow-Up Test
(mean value = 31.33) than the male students (mean value = 30.37). Female students, again,
contributed slightly better test performances. Both groups, however, did not pass the
test. Mann–Whitney U test then showed no statistically significant difference between the
students’ Follow-Up Test scores and their genders, since the p-value (0. 769) was larger
Educ. Sci. 2021,11, 554 7 of 12
than 0.05. It can be observed that the students’ genders have had no impacts on the two
sets of test scores.
Analysis of the Test Scores by Academic Faculties/Disciplines
Firstly, the result showed the students from the School of Media and Communication
had the highest mean score on the Diagnostic Test (mean value = 46.29) among the four
academic faculties/disciplines, followed by the ones from the School of Chemistry (mean
value = 46.18) and the School of Information Technology (mean value = 44.92). The students
from the School of Architecture had the lowest mean score (mean value = 41.33). The reason
was possibly because arts students generally had better performances in English study than
science and engineering students. However, all four groups failed the test, since their mean
scores were lower than 60 points. The Kruskal–Wallis test result on scores by academic
faculties/disciplines indicated the students from the School of Information Technology
had the highest test score (mean rank = 70.27). However, the students from the School of
Architecture scored the lowest (mean rank = 61.71). The result indicated no correlation
between the students’ the Diagnostic Test scores and their academic faculties/disciplines
(x2 = 1.151, df = 3, p-value = 0.765 > 0.05). Secondly, the students from the School of Infor-
mation Technology scored the highest on the Follow-Up Test (mean value = 33.75) among
the four academic faculties/disciplines, followed by the ones from the School of Chemistry
(mean value = 31.82) and the School of Media and Communication (
mean value = 9.916
However, the students from the School of Architecture had the lowest mean score (mean
value = 28.51). The reason why Computer Science had better test scores could be that
they were more familiar with and adept at using mobile technologies. They could thus
take advantages of the program to its fullest. However, all students failed the test again
since their mean scores were lower than 60 points. The result of the Kruskal–Wallis test
on the scores by academic faculties/disciplines showed the students from the School of
Information Technology had the highest test score (mean rank = 71.35). However, the
students from the School of Architecture scored the lowest (
mean rank = 61.62
). There
was no correlation between the students’ Follow-Up Test scores and their academic facul-
ties/disciplines (
x2 = 1.401
, df = 3, p-value = 0.705 > 0.05). It could be concluded that the
students’ academic faculties/disciplines have had no impacts on the two sets of test scores.
5.2. Findings to Research Question Two
Paired-Sample t-Test Result of the Two Sets of the Test Scores
A paired-sample t-test was conducted to compare and analyze the 133 students’ scores
on each vocabulary proficiency test to verify whether a statistically significant difference
existed between the two test scores. According to the paired-sample t-test result (as shown
in Table 4below), a statistically significant difference was found between the students’ two
sets of test scores, since the significance value = 0.000, which was smaller than 0.05. That is,
the 133 students did perform statistically significantly on the two sets of tests. Additionally,
the effect size was calculated to further measure the WALL program effect on the test scores.
Cohen’s d indicated the effect size magnitudes, namely d = 0.20 for a small effect, d = 0.50
for a medium effect, and d = 0.80 for a large effect [
]. It was reported the WALL program
had a medium effect size of 0.50. That is, the WALL program has brought about medium
differences to the students’ test scores.
Table 4. Paired-sample t-test result of the test scores.
Paired Differences tdf Sig. (2-Tailed)
Mean Standard Deviation
Diagnostic Test
Follow-Up Test 13.226 26.460 5.764 132 0.000
Educ. Sci. 2021,11, 554 8 of 12
6. Discussions
The previous results initially indicated a number of potential influential factors/variables,
including the students’ academic years, genders, and academic faculties/disciplines, had no
correlations with the test scores. More importantly, the students had statistically significantly
different test scores. However, it was discouraging to see the decline in the test scores after
the delivery of the WALL program. Therefore, it can be argued that the program was the
only variable that has had negative impacts on the 133 students’ test performances. That is
to say, the students did not improve or retain their lexical proficiency, despite they had used
the program for the period of three weeks during the pandemic. Contrary to the findings of
Reference [
] which reported MALL positive effects by synthesizing 80 latest worldwide
publications before the pandemic, the results of this particular study showed the MALL
approach did not improve or retain students’ lexical proficiency in the transition. Negative
results, however, were not unusual according to previous evidence [
]. Several reasons could
possibly explain the unsuccessful intervention of the WALL program:
First, it was a challenge for a considerable number of students to embrace the new
MALL approach. It was true that, before COVID-19, MALL pedagogies were welcomed [
and mobile phones or smartphones were the most widespread mobile device for MALL
practices [
]. However, according to the statistics shown on the WeChat public account,
the students in this study hesitated to engage with the program. Explicitly, not all the
students have viewed the learning materials and content, performed the daily practice
and drills, or read the additional learning resources. This happened probably because
they cast doubts on integrating WeChat with the regular teaching syllabus in the face of
the unexpected transition. It was consistent with [
]’s findings that around half of the
participants remained uncertain about effects of the new learning approach. Students
would have a strong intention of conducting MALL activities, if only they perceived them
to be pronouncedly useful [10].
Second, limited technical skills and knowledge prohibited students in using MALL
tools. Owing to the large population of mobile phone users in China, it is hard to see a
student without a mobile phone on university campuses [
]. The generation of digital
nativities was familiar with and not irritated by using mobile devices for multifaceted
purposes [
]. In addition, most Chinese university students would like to use mobile
technologies for learning activities during the pandemic [
]. The subject university was
equipped with on-campus high-speed network and stable Internet connectivity. Most of
all, the program was designed while considering the costs of mobile data and challenges
of network speed. However, certain students in this study were not familiar with fully
applying the WeChat public account for vocabulary learning. They were possibly uncertain
and confused about using the program to the fullest, ranging from reading learning
materials in multiple forms, doing daily practice and drills, to viewing more learning
resources. It could also be overwhelming and complex for some students to download
texts, audios, and video clips from the program for future use. As mentioned previously,
the vocabulary-learning content and materials were purposively designed in various forms.
Language learning, however, could be interfered by redundancy and working memory
load if students are exposed to the concurrent delivery of same learning information in
different modes [50].
Third, students had difficulty concentrating on MALL activities. The students in this
study could possibly devote to social chats when logging in the public account via WeChat.
They would then get immersed in chatting with their friends rather than learning with
the program. This happened since WeChat is a social application in which messages pop
up automatically. Meanwhile, notifications and new information from other subscribed
public accounts could also distract them from engaging with the program. When scrolling
down the list of subscribed public accounts, the students’ attention could be drawn by
other appealing information, such as games. Since the learning activities in this study were
carried out in an informal learning setting, the students were likely to be disturbed by
Educ. Sci. 2021,11, 554 9 of 12
any trivial matters. Undoubtedly, environments free from distractions were conducive to
enhancing MALL outcomes [49].
Fourth, students had difficulties participating in MALL activities. As an indispensable
element in educational practices, teachers had crucial impacts on students’ learning per-
formances [
]. In this study, the students experienced reduced engagement in the lexical
learning activities when using the program in an informal/non-classroom learning setting.
It happened because of the absence of language teachers who often supervise or remind
students of learning tasks performed in formal/classroom learning environments. Students
mostly conducted autonomous learning or self-directed learning during the pandemic [
That is, it required self-regulation and self-discipline from the students when using the
program in absence of teacher-led instruction. However, as the statistics the public account
presented, numerous students would not like to use the program autonomously until their
language teachers asked them to.
Fifth, students’ negative emotions could possibly have hindered the MALL effects.
Students, when learning online, were likely prone to negative emotions, such as frustration,
anxiety, learning slackness, and weariness [
]. In this study, the students mostly had
to learn the lexical items and perform the learning activities on the program on their
own. Therefore, they would possibly feel disconnected and distant resulting from limited
interaction due to lack of physical proximity and isolation with peers. Such issues are
common in informal online learning practices [
]. Moreover, the students had weaker
intention and motivation in learning vocabulary by using the program, as their excitement
for participating in MALL activities could be temporary [
]. Therefore, the students in
this study would possibly have less satisfactory results when their enthusiasm decreased
during the period of program delivery.
7. Limitations and Suggestions for Future Research
The study has several limitations. Firstly, considering the small sample size of the
case study, the findings could be less representative regarding different situations at other
Chinese universities. The authors would suggest that further studies should have a larger
sample size and a wider geographical range, because it would be beneficial to provide a
comprehensive picture of the researched topic. Since the present project was conducted in
an academic university context, investigations in vocational colleges were also suggested.
Secondly, since the university in this study allowed the students to return to the campus
before the intervention started, the students used the program for lexical learning in a
mixed learning mode. They might have conducted learning activities, using the program
either in or out of the classroom. Findings could thus be different if the setting was
completely formal or informal. The authors call for future studies that should consider
different physical locations/environments under these circumstances. Thirdly, the present
study selected the participants by using a nonrandomized sampling method. The authors
would suggest that future studies can apply randomization for the participant recruitment
to verify the research findings. Fourthly, the present paper primarily investigated the
quantitative findings, since it was under a larger PhD project. The students’ opinions and
perceptions of using the program for MALL practices during the pandemic were explored
in a separate paper of the authors, using a mixed-methods research design, including
open-ended questionnaires and semi-structured interviews. The paper will be drafted and
submitted soon. Fifthly, considering the fact that MALL approaches have been widely used
for a broad range of language skills [
], the present study that has focused specifically
on vocabulary calls for future studies to investigate different language teaching areas.
Lastly, the intervention duration was around one month, owing to the time conflict with
the final-exam week at the university. Since intervention durations have been found to
impact the MALL effectiveness [
], future studies are expected to conduct a longitudinal
empirical study with longer intervention durations.
Educ. Sci. 2021,11, 554 10 of 12
8. Conclusions
The present study evaluated the effectiveness of a WeChat-based program in facil-
itating students’ vocabulary learning outcomes at a Northern Chinese university. Two
sets of English-language vocabulary tests were used to measure the students’ lexical profi-
ciency. The findings initially indicated that the independent variables, such as the students’
academic years, genders, and academic faculties/disciplines, had no impact on their vo-
cabulary test performances. Moreover, it was found that the students had a decline in their
test scores after using the WALL program for a period approximately three weeks. The
program has had a medium effect on the difference of the test scores. It can be stated that
the effectiveness of MALL in the Chinese higher-education context remains debatable amid
the pandemic.
Author Contributions:
Conceptualization, F.L., S.F. and Y.W.; methodology, F.L.; software, F.L.;
validation, S.F., Y.W. and J.L.; formal analysis, F.L.; investigation, F.L.; resources, F.L.; data curation,
F.L.; writing—original draft preparation, F.L.; writing—review and editing, S.F., Y.W. and J.L.;
visualization, F.L.; supervision, S.F. and Y.W.; project administration, F.L., S.F. and Y.W. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Human Research Ethics Committee (HREC) of the
University of Tasmania (UTAS), Australia (protocol code H67547 and on 15 April 2020).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
The data presented in this study are available on request from the authors.
Conflicts of Interest: The authors declare no conflict of interest.
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... The COVID-19 pandemic has disrupted education in many countries; thus, as an emergent response, many countries focused on implementing remote learning modalities. Meaningful two-way interaction between students and their teachers during COVID-19 pandemic was mostly solved via online programs and social media (Shadiev and Yang, 2020;Kamasak et al., 2021;Li et al., 2021;Lei et al., 2022). Aside online programming, Mobile-Assisted Language Learning (MALL) in learning English through SN tools excited special attention in recent years. ...
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To mitigate transmission of COVID-19, rapid changes in instructional delivery moved from in-person to remote instruction. Although literature from before the crisis suggests that online language learners fare at least as well as their face-to-face counterparts, the abrupt shift from face-to-face contexts to remote learning is fundamentally different from planned online learning. Understanding the nature of this shift can inform future online and remote teaching. This national survey study was guided by research questions that explore any substantive change in the practices and perceptions of PreK-12 and post-secondary language teachers’ instruction during COVID-19. It explores any change as related to classroom setting (PreK-12 vs post-secondary) and prior experience with distance education. Data suggest that few language educators reported experiences with or positive perceptions toward teaching online before COVID-19. However, they made numerous adjustments to their typical procedures/policies and expectations while engaged in remote teaching. Educators expressed concerns about student outcomes. PreK-12 teachers and those without prior experience with online teaching were least confident that instructional goals were met despite having reported well-designed courses. Implications for effective remote language teaching are presented for consideration.
The COVID-19 pandemic shocked school education, bringing a lot of challenges. The pandemic has changed how millions around the globe are educated and new solutions for education could bring much needed innovation. During the pandemic, online teaching has become a necessary way for teachers and students in primary schools, middle schools, high schools and even universities around the world. College English teaching becomes increasingly difficult as there are many affecting factors. The article first analyzed the advantages and challenges of the pandemic on the college English online teaching, and then put forward the strategies of online teaching design, in order to supply some reference and enlightenment for the college English teaching in China.