132
Crowdsourcing and language 
learning habits and practices in 
Turkey, Bosnia and Herzegovina, 
the Republic of North Macedonia 
and Poland in the pre-pandemic 
and pandemic periods
Çiler HATIPOĞLU
Faculty of Education, Middle East Technical University
Nihada DELIBEGOVIĆ DŽANIĆ
University of Tuzla
Elżbieta GAJEK
University of Warsaw
Lina MILOSHEVSKA
University of Information Science and Technology
The popularity of online crowdsourcing platforms was slowly increasing 
among language learners before the pandemic, but COVID-19 changed the 
educational systems worldwide. This study aims to uncover whether or not, 
and if ‘YES’, how the attitudes and habits of language learners concerning the 
Hatipoğlu, Ç., Delibegović Džanić, N., Gajek, E., Miloshevska, L.: Crowdsourcing and 
language learning habits and practices in Turkey, Bosnia and Herzegovina, the 
Republic of North Macedonia and Poland in the pre-pandemic and pandemic 
periods. Slovenščina 2.0, 10(2): 132–181. 
1.01 Izvirni znanstveni članek / Original Scientific Article
DOI: https://doi.org/10.4312/slo2.0.2022.2.132-183
https://creativecommons.org/licenses/by-sa/4.0/
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Crowdsourcing and language learning habits and practices...
use of crowdsourcing materials in Turkey, Bosnia and Herzegovina, the Re-
public of North Macedonia and Poland changed during the pandemic. 
To compare the pre-and during the covid crowdsourcing tool usage, the 
cross-culturally appropriate questionnaire utilised in the pre-COVID-19 pe-
riod was used again. The collected data were analysed qualitatively and quan-
titatively to identify the differences between the periods. 
The study’s findings showed that the shift from face-to-face to online 
learning significantly affected the development of crowdsourcing platforms 
worldwide and their employment in the studied countries. The results also 
demonstrated that a combination of factors, such as reduced interactions 
with teachers and peers, an increase in workload, and a lack of support on the 
part of institutions, led to students taking responsibility for their learning. The 
number and characteristics of the popular platforms changed from country to 
country since expectations from students varied.
Keywords: crowdsourcing, language learning, COVID-19, pre-pandemic pe-
riod, post-pandemic period
1 Introduction
Crowdsourcing, in Estellés-Arolas et al.’s (2015, p. 33) definition, is a 
problem-solving and task realisation model where thanks to harnessing 
collective intelligence, creative solutions to complex problems are found. 
Due to the success and usefulness of the initiative and its products, the 
number of fields embracing it (e.g., tourism, architecture, artificial intelli-
gence) and researchers focusing on the concept (Lyding et al., 2018; Ro-
dosthenous et al., 2019) have been steadily increasing (Xu et al., 2022). 
The popularity of online crowdsourcing platforms in language teaching 
and learning was slowly rising before the COVID-19 pandemic (Arhar 
Holdt et al., 2020; Gajek, 2020; Hatipoğlu et al., 2020; Miloshevska et 
al., 2021). Studies done in Turkey (TUR), Bosnia and Herzegovina (B&H), 
the Republic of North Macedonia (RNM), and Poland (POL) showed that 
language teachers used them both as in- and out-of-class materials, and 
students employed them as tools helping them to sharpen their skills 
and knowledge in the target languages and become more autonomous 
learners (Hatipoğlu et al., 2020, 2021; Miloshevska et al., 2021). 
The COVID-19 pandemic has changed, however, the educational 
systems worldwide and the established teaching and learning practices. 
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Face-to-face classes were abruptly suspended in almost all countries, 
and this led to the disruption of “the original teaching plans of schools 
in these countries and regions” (Chen et al., 2020, p. 1). During the first 
COVID-19 period of online education (2019-2020 spring semester, 
especially after March 2020), teachers and students had to abandon 
their familiar settings and quickly adapt to the new environments, which 
was a stressful process for all involved parties (Akat and Karataş, 2020; 
 Krajka, 2021). Teachers, who up to that point were experts in their fields 
but did not frequently use digital technology, had to learn about new 
tools and systems and modify their teaching methods, techniques, ma-
terials and assessment practices (Hatipoğlu et al., 2021). Unfortunately, 
many of the changes were done randomly or opportunistically, without 
being aware of and, therefore, not following any of the established Com-
puter Assisted Language Learning (CALL) models (Bax, 2003; Hampel 
and Stickler, 2005). Students also had to adjust to the new, mainly soli-
tary online environment where they were deprived of social contact with 
their peers and teachers, and could not expect constant support from 
their institutions (Miloshevska et al., 2020; Trung et al., 2020).
Studies related to the status of tertiary education during the sec-
ond and third semesters of online learning and teaching in some coun-
tries (e.g., Australia, the USA, and Canada in Hickling et al., 2021; Lat-
via in Baranova et al., 2020) showed that both teachers and students 
successfully settled into new routines and started following practices 
that were more suitable for the prolonged period online education. 
But what about the foreign language students in TUR, B&H, RNM and 
POL? What were their learning and teaching experiences during the 
COVID-19 pandemic with regard to using online/digital crowdsourcing 
materials/platforms? 
This study was conducted to find out whether there have been any 
changes related to the use of crowdsourcing materials for language 
learning and teaching purposes (e.g., sources such as  Wikipedia, 
 Duolingo, Kahoot, Online dictionaries, social media sites) in TUR, B&H, 
RNM and POL during the different phases of the COVID-19 pandemic 
(for more detailed examples and explanations, see the Literature Review 
section). These four countries were selected as focal points since some 
previous studies (Delibegović Džanić et al., in press; Hatipoğlu, 2021; 
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Miloshevska et al., 2020, 2021) showed that the national ministries 
of education and the university administrations planned and organ-
ized instruction in K-12 and tertiary levels differently. The expecta-
tions and requirements from students were also somewhat different 
in these countries. To reach our goal, the results of the Miloshevska 
et al.’s (2021) study, which are based on data from the pre- and ini-
tial COVID-19 periods, are compared with new data sets collected in 
the later phases of the pandemic (i.e., from December 2021 to March 
2022, that is 2021-2022 spring semester). 
2 Literature review
The term “crowdsourcing” was first coined by the American journalist 
Jeff Howe (2006) in an article for Wired magazine. The term was devel-
oped further, defined, and exemplified in his book Crowdsourcing: How 
the Power of the Crowd is Driving the Future of Business (Howe, 2008). In 
his work, Howe (2006, 2008) describes how the internet and the devel-
opment of Web2.0 tools broke down the traditional way of doing work 
as well as the employer-employee relationships. He argued that thanks 
to the collaborative nature of the newly developed digital tools, compa-
nies, institutions, and even individuals, just by posting an open call, could 
now benefit from the wisdom of the usually heterogeneous crowd on 
the internet (e.g., volunteers, experts, even amateur enthusiasts) to find 
solutions for challenging problems, create new products, sort pictures 
and a multitude of other tasks. The reward the contributors receive in 
this setting depends on the company posting the call, the project’s na-
ture, and the crowd’s interests. It could be either intangible (e.g., recog-
nition or prestige within a group with specific interests, being entertained 
because they like playing a particular game) or tangible (e.g., money). 
The first of these practices is known as “micro working crowdsourcing” 
(e.g., people add entries to Wikipedia, but they are not paid), while the 
latter is called “benevolent  crowdsourcing” (e.g., Amazon’s Mechanical 
Turk pays individuals for their work1). This model of doing things and/or 
completing projects was first used in the business environment, but has 
evolved and spread and is now being used for different purposes in fields 
1 www.mturk.com
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as diverse as geography (See et al., 2014), medicine (King et al., 2013), 
and multimedia (Soleymani and Larson, 2013). Nowadays, anyone can 
post videos on YouTube or TikTok, those who feel competent are free to 
write book reviews on Amazon, and ambitious, fearless artists submit 
their T-shirt designs to Threadless and wait for the crowd’s verdict. 
One other field where crowdsourcing has started gaining mo-
mentum and is being used more frequently in recent years is educa-
tion. Since education theories, methods and techniques, as well as 
the learner profiles (e.g., daily routines, interests, social, cultural and 
language backgrounds) are getting increasingly diverse, conventional 
education, where traditional classrooms and textbooks limit students’ 
experiences, is now being challenged and replaced by various other 
practices. Rapid developments in technology and greater respect for 
diversity in learning needs mean that the “wisdom of the crowd” is 
being considered by a growing number of competitive educational or-
ganizations (Çebi, 2018; Solemon et al., 2013; Wang, 2016). Crowd-
sourcing is used in various ways to support innovative education, and 
research shows that with such practices it is possible to create and 
offer authentic in- and out-of-class activities (Chen and Luo, 2014; Hui 
et al., 2014), innovative learning and teaching resources (Farasat et al., 
2017), and context and student group-specific support (Goel, 2017; 
Shaikh et al., 2017; Weld et al. 2012). 
Despite the advantages associated with the use of crowdsourcing 
in education, research also shows that one field where its use was not 
fully incorporated before the COVID-19 pandemic was language teach-
ing and learning. Two main reasons have been identified as to why the 
inclusion of crowdsourcing activities in language education was still in 
its initial stage at this time: 1) the lack of knowledge on the part of the 
teachers, which led to 2) disinterest and gaps in students’ knowledge 
related to them. In a study conducted by Arhar Holdt et al. (2019), where 
the researchers collected data from 1,129 language teachers from more 
than 30 countries, it was found that quite a significant number of the 
participants were not familiar with the concept of crowdsourcing, and 
therefore they were using a very small number of crowdsourcing ac-
tivities in their classrooms. Maybe this is why, several years earlier, Odo 
(2016) published an article targeting language teachers and comparing 
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Crowdsourcing and language learning habits and practices...
the advantages and disadvantages of using crowdsourcing materials. 
He also presented some lists aiming to show language teachers how 
such materials could be used in the classroom, the stages of the lessons 
where they could be incorporated, what teachers are expected to do to 
encourage the use of such materials, and the available and useful crowd-
sourcing resources. Odo (2016) completed his article by arguing that the 
“potential of these resources is immense. Ignoring the possibilities for 
our classroom is a missed opportunity for our students to join a trend 
that could revitalize our language teaching and their learning” (p. 23).
2.1 Crowdsourcing research before COVID-19
The number of studies examining language learners’ views of crowd-
sourcing was even more limited before the COVID-19 pandemic. The 
authors are aware of just four papers that specifically focus on language 
learners’ views of crowdsourcing platforms (Gajek, 2020; Hatipoğlu et 
al., 2020; Miloshevska et al., 2021; Mospan, 2018), and this indicates 
a significant gap in the field, since it is essential that students (i.e., end 
users) accept the validity of a new product and begin to use it. Rafiee 
and Abbasian-Naghneh (2019, p. 1) maintain that there are “com-
plex relationships between the perceived usefulness, perceived ease 
of use, e-learning motivation, online communication self-efficacy and 
language learners’ acceptance and readiness of e-learning”. Stated 
differently, knowing which crowdsourcing materials are employed by 
language learners, as well as when and how, is vital information not 
only for language teachers but also for platform creators, since it will 
aid them in developing and recommending resources to help students 
with their learning and progress.
Online teaching and/or blended learning were part of the educa-
tional system long before the COVID-19 pandemic. What is more, tech-
nology has often been used to support the continuity of teaching and 
learning in areas suffering from natural disasters (e.g., earthquakes, 
floods) (Baytiheh, 2018) or a lack of resources (e.g., large classes) (Kra-
jka, 2021). These were, however, implementations in a limited number 
of places or in periods that were carefully planned and followed well-
designed stages and procedures. The rest of the education, the vast 
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bulk of it, both around the world and in TUR, B&H, RNM and POL was 
done face-to-face (Miloshevska et al., 2020). 
2.2 Crowdsourcing research during COVID-19
The spread of COVID-19 and its identification as a pandemic led to 
sudden lockdowns in many countries. This required changes in all es-
tablished ways of teaching and learning, including language educa-
tion. All stakeholders in the educational institutions suddenly found 
themselves “in a new reality, with technology-mediated instruction of 
different kinds substituting for traditional face-to-face teaching” (Kra-
jka, 2021, p. 112). Neither teachers nor students had access to the 
resources, methods and techniques they were used to, so they had to 
use a system that many of them were testing for the first time, i.e., on-
line learning and teaching. As a result of this sudden but compulsory 
change, there was a boom in the development, usage and research re-
lated to the use of crowdsourcing materials during the COVID-19 pe-
riod. When Kansal et al. (2021) used Google Trends analysis to uncover 
the platforms of online teaching and learning that made remote learn-
ing around the world possible, they found that there had been signifi-
cant growth in the number of such platforms in just a year. They also 
reported that the “existing assets of educational establishments have 
effectively converted conventional education into new-age online edu-
cation with the help of virtual classes and other key online tools in this 
continually fluctuating scholastic setting” (Kansal et al., 2021, p. 418). 
That is, faced with the harsh reality of lockdowns, platform developers, 
teachers, students, and researchers were all trying to find ways to help 
formal education continue.
The research done during the COVID-19 period can be placed 
mainly in three sometimes overlapping categories. The studies in the 
first group focus on uncovering, classifying and/or listing the plat-
forms that could be used in such circumstances (Chen et  al., 2020; 
Kansal et al., 2021; Reimers et al., 2020). Reimers et al. (2020, p. 2), 
for instance, prepared an annotated selection of “online educational 
resources to support the continuity of teaching and learning during 
the 2019-20 COVID-19 Pandemic with education leaders around the 
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world”. The list of resources was compiled based on the responses of 
333 informants from 99 countries. They asked stakeholders participat-
ing in the survey to identify online educational resources that they had 
found helpful in supporting education continuity up to that point, and 
classified them into Curriculum Resources, Professional Development 
Resources or Tools. They also used Pellegrino and Hilton’s (2012) tax-
onomy to provide information related to the foreign languages, skills, 
and subjects that can be taught using the materials, as well as the 
grades of students that could benefit from them and whether or not 
they developed the interpersonal and intrapersonal skills of the users. 
To be able to construct a valid evaluation index that could be used in 
the case of other emergencies similar to the COVID-19 pandemic, Chen 
et al. (2020) asked users in China to review their experiences with on-
line education platforms before and after the outbreak of the disease 
using criteria such as access speed, reliability, timely transmission of 
video data, course management, communication and interaction, and 
learning and technical support. The study focused on the performance 
of the seven most popular platforms in the country: Chaoxing Learning, 
DingTalk, MOOC, Tencent Meeting, TIM, WeChatWork, and Zoom Cloud. 
The analysis of the data showed that before the pandemic what users 
expected from a good platform were characteristics such as good access 
speed, reliability, and smooth transmission of video data. However, af-
ter the outbreak of COVID-19, when all classes moved online, they were 
more concerned with course management, communication and interac-
tion, and the quality of the learning and technical support services of the 
platforms. In Chen et al.’s (2020) article, overall “Chaoxing Learning had 
the poorest user experience and DingTalk performed best” (p. 28). 
The second group of studies tried to uncover the general benefits 
of using certain platforms as main or supplementary materials for lan-
guage learners (Ali, 2022; Krishnan et al., 2020; Nadhifah and Puspi-
tasari, 2021). When Krishnan et al. (2020) looked at how free online 
resources were used by language learners during the pandemic, they 
found two crucial facts. One, the user-friendly technologies that were 
freely available on the internet gained popularity during the COVID-19 
crisis (i.e., they were used much more frequently and by a bigger num-
ber of students). Two, the educational lives of many students who 
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reported experiencing economic problems during the pandemic were 
saved by freely available online resources, such as online dictionaries, 
YouTube videos, foreign language material development platforms, and 
grammar checkers. Nadhifah and Puspitasari (2021), who reviewed the 
effects of remote learning on students’ study habits (before specifically 
focusing on Duolingo), maintain that the use of online platforms during 
the pandemic forced students to become more “responsible learners”, 
and that the “pandemic condition urged them to conduct a self-regu-
lated language learning by utilizing and optimizing the relevant media 
to learn” (p. 303).
The third group of studies examined whether, and if so how, certain 
online resources helped language learners develop specific language 
skills and sub-skills, such as listening, speaking, and pronunciation, 
and types of knowledge, including vocabulary and grammar (e.g., Kra-
jka, 2021; Li and Xu, 2015; Nadhifah and Puspitasari, 2021; Trinh et al., 
2021; Tsai, 2019; Waicekawsky et al., 2020). Nadhifah and Puspitasari 
(2021) studied the effects of Duolingo on the development of the struc-
tural knowledge of students with low and intermediate proficiency lev-
els. They found that while intermediate-level students did not think they 
benefited much from the exercises on the platform, low-level learners 
stated that Duolingo helped them develop their grammar knowledge in 
English with tasks that were fun and appropriate for their level. 
Trinh et al. (2021, p. 28), who worked with Vietnamese language 
learners, and Waicekawsky et al. (2020), whose participants were Ar-
gentinian EFL students, looked at the effects of another group of online 
resources that were frequently employed by foreign language learn-
ers during the pandemic – online dictionaries. Trinh et al.’s (2021) par-
ticipants, who were native speakers of a tonal language (Alvez, 2006) 
and for whom speaking patterns in English are usually tricky, reported 
benefits such as improved intonation, pronunciation and grasp of vo-
cabulary items’ meaning. Consequently, the majority of the 300 junior 
students who participated in Trinh et al.’s (2021) study demonstrated a 
strong preference for online dictionaries over paper ones. 
The concise literature review in this section demonstrates the strik-
ing differences in the use of crowdsourcing materials in educational 
settings before and during the COVID-19 pandemic. The current study 
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Crowdsourcing and language learning habits and practices...
aims to contribute to this area of research and examines the potential 
changes in TUR, B&H, RNM and POL.
The specific research questions that this study aims to answer are:
(1) What digital crowdsourcing resources did students in TUR, B&H, 
RNM and POL know about and use to learn foreign languages in 
the pre- and initial COVID-19 period (Period1, P1) versus the late 
COVID-19 period (Period2, P2)?
(2) Were there any changes in the frequencies, attitudes, contexts of 
use, and habits related to crowdsourcing materials of language 
learners in TUR, B&H, RNM and POL in P2 when compared to P1? 
3 Methodology
3.1 Data Collection
In this study, the main aim was to uncover whether there have been 
any changes related to the use, attitudes, habits and contexts of use of 
crowdsourcing materials from the pre- to during the COVID19 periods 
by language learners in TUR, B&H, RNM and POL. To achieve this goal, 
the results of the authors' earlier study (Miloshevska et al., 2021) for 
which the data were collected in the pre- and during the emergency 
online teaching period in the Spring 2020 semester are compared with 
the new data collected in Spring 2022. 
To ensure a reliable and valid comparison across countries and 
periods, the questionnaire designed for the initial study was utilized 
again, since it proved to be a cross-culturally appropriate data col-
lection tool eliciting high-quality data enabling researchers to answer 
their research questions. 
The written data collection tool employed in both studies had two 
sections, A and B. The 11 questions in Section A aimed to gather detailed 
information about the participants’ use of crowdsourcing tools and plat-
forms. Nine of the 11 items in this section were checkbox questions where 
the participants could select multiple answers from a list of options (see 
Figure 1 for an example question). There was also one Likert scale item 
and one open-ended item. The Likert scale item asked participants to 
rate the crowdsourcing platforms they used from “Very enjoyable” (5) to 
“Not enjoyable at all” (1) and “I have not used it” (0). On the other hand, 
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in the open-ended item the participants were asked to give information 
about their previous contributions to various crowdsourcing platforms.
Figure 1: Example checkbox question used in the study.
Section B of the questionnaire included six questions, and it aimed 
to collect data related to the participants’ backgrounds. Four of the six 
questions were checkbox items, and two were open-ended. 
3.2 Data Analysis
The collected data sets were analyzed both qualitatively and quantita-
tively to identify even the most minor changes between the compared 
periods in the studied four countries. The quantitative analyses were 
done using SPSS, where various descriptive (e.g., frequencies, percent-
ages) tests were performed. The qualitative data were evaluated follow-
ing the procedures proposed by Miles and Huberman (1994, pp. 58-69).
The tested hypothesis was that there had been a significant change 
in both teaching and learning habits in the pre-and pandemic periods, 
and that crowdsourcing platforms gained popularity during the COV-
ID-19 period. This hypothesis was based on the findings of a study (Mi-
loshevska et  al., 2020) showing that teachers in B&H, NM, POL and 
TUR, similarly to their colleagues around the world, were forced to use 
almost all the digital tools they had at their disposal at this difficult time, 
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especially during the emergency online teaching period in the Spring 
2020 semester. At the same time, language learners were forced to in-
dependently use different crowdsourcing tools and platforms to catch 
up with the requirements of their institutions.
3.3 Participants
A total of 396 university students participated in the study. The partici-
pants in Study1 (Period1, Spring 2020) were 211 students from TUR 
(N=43, 20.4%), B&H (N=69, 32.7%), RNM (N=42, 19.4%) and POL 
(N=58, 27.5%) (see Table 1a). Their age range was 18-39, although 
98.1% of them were 18-25 years old (Age Group 1: 18-21 years old, 
N=109, 51.7%; Age Group 2: 22-25 years old, N=98, 46.4%). Only 
1.9% of the informants were in Age Group 3 (Range: 26-39; N=4).
Table 1a: Participants in Period 1 (P1) (Spring 2020)
TUR B&H RNM POL ALL
Males (M) 12 (27.9%) 17 (24.6%) 27 (65.9%) 12 (20.7%) 69 (33%)
Females (F) 31 (72.,1%) 52 (75.4%) 14 (34.1%) 45 (79.3%) 142 (67%)
Prefer not to say 0 0 0 0 0
TOTAL 43 (20.4%) 69 (32.7%) 41 (19.4%) 58 (27.5%) 211 (100%)
As can be seen in Table 1a, 67% (N=142) of the participants were 
female, while 33% (N=69) were male. The informants from TUR, B&H 
and POL were training to become foreign language teachers, while the 
participants from RNM were Information and Communication, Engi-
neering, and Computer Science Engineering students learning English 
for specific purposes. The smaller number of male participants in the 
study reflected the gender distribution of students at the Faculties of 
Education in TUR, B&H and POL (Can Daşkın & Hatipoğlu, 2019). 
Table 1b: Participants in Period 2 (P2) (Spring 2022)
TUR B&H RNM POL ALL
Males (M) 28 (46.7%) 11 (23.9%) 33 (67.3%) 5 (16.7%) 77 (42%)
Females (F) 32 (53.3%) 31 (67.4%) 16 (32.7%) 23 (76.7%) 102 (55%)
Prefer not to say 0 4 (8.7%) 0 2 (6.6%) 6 (3%)
TOTAL 60 (32.4%) 46 (24.9%) 49 (26.5%) 30 (16.2%) 185 (100%)
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To have comparable informant groups to the first study (i.e., P1), 
the data in Study2 (Spring 2022) were collected from the same institu-
tions and faculties. The total number of participants in Study2 was 185 
– TUR (N=60, 32.4%), B&H (N=46, 24.9%), RNM (N=49, 26.5%) and 
POL (N=30, 16.2%) (see Table 1b) – and their age range was 17-40. 
Similarly to Study1, most of the students (94%) were 18-25 years old 
(Age Group 1: 18-21, N=114, 62%; Age Group 2: 22-25, N=60, 32%), 
and only 2% were in the 35-40 age group. Among the 185 participants, 
55% (N=102) were female, and 42% (N=77) were male; 3% (N=6) of 
the informants ticked “Prefer not to say” as an answer to this question.
To check whether the students’ language proficiency affected the 
type of crowdsourcing tools they utilized for language learning, the par-
ticipants in both phases of the study were asked to self-evaluate using 
CEFR levels and criteria (Council of Europe, 2001).
As shown in Table 2a, in Study1 about two-thirds (65.4%) of the 
participants placed themselves in the Proficient Users (C1=79, 37.4% 
or C2=59, 28%) category, while 18.4% identified themselves as Inde-
pendent Users (B1=6, 2.5% or B2=33, 15.6%). Only a small number of 
the participants from RNM stated they were Basic Users (A1=4, 1.9%; 
A2=3, 1.3%). 
Table 2a: Self-reported level of proficiency of the participants in Period 1 (Spring 2020)
 TUR B&H RNM POL ALL
 n % n % n % n % n %
A1 4 9.8 4 1.9
A2 3 7.3 3 1.4
B1 2 2.9 4 9.8 6 2.8
B2 3 7.0 15 21.7 10 24.4 5 8.6 33 15.6
C1 8 18.6 27 39.1 13 31.7 31 53.4 79 37.4
C2 23 53.5 19 27.5 5 12.2 12 20.7 59 28.0
No answer 9 20.9 6 8.7 2 4.9 10 17.2 27 12.8
All 43 100.0 69 100.0 41 100.0 58 100.0 211 100.0
When the students participating in our Period 2 study were asked 
to evaluate their language proficiency, 99% of them placed themselves 
in either the Proficient Users (C1=91, 49.2% or C2=56, 30.3%) or In-
dependent Users (B1=5, 2.7% or B2=31, 16.8%) categories (see Table 
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2b). Only one student from RNM chose the A1 level, and there was 
one student who did not respond to this question. So, similarly to the 
participants in P1, the students we are dealing with in Study 2 are also 
mainly advanced learners of English, and thus the preferences and ex-
periences discussed in this paper are more relevant to learners with 
more advanced skills in the target languages.
Table 2b: Self-reported level of proficiency of the participants in Period 2 (Spring 2022)
 TUR B&H RNM POL ALL
 n % n % n % n % n %
A1 1 2.0 1 0.5
A2 0 0.0
B1 1 2.2 4 8.2 5 2.7
B2 4 6.7 11 23.9 15 30.6 1 3.3 31 16.8
C1 32 53.3 18 39.1 20 40.8 21 70.0 91 49.2
C2 24 40.0 15 32.6 9 18.4 8 26.7 56 30.3
No answer 0 0.0 1 2.2 0 0.0 0 0.0 1 0.5
All 60 100.0 46 100.0 49 100.0 30 100.0 185 100.0
4 Results and discussion
Before the COVID-19 pandemic, crowdsourcing materials were a rel-
atively new phenomenon. Their use was beginning to gain pace, but 
they were still not often used in the educational context (Chen et al., 
2020; Jiang et al., 2018) or in the four countries examined in this study 
(i.e., TUR, B&H, RNM and POL) (Miloshevska et  al., 2021). The rapid 
switch from face-to-face to online learning, however, surprised and 
forced students, teachers, and institutions to alter their teaching and 
learning practices (Delibegović Džanić et al., in press; Hatipoğlu et al., 
2022). What about the crowdsourcing resources that students use to 
learn languages? Did they change from the pre- to the late COVID-19 
periods? Were there any changes in the frequencies, attitudes, contexts 
of use, and habits related to the crowdsourcing materials used by lan-
guage learners in TUR, B&H, RNM and POL in P2 when compared to P1? 
This study aims to answer these questions by comparing the 
crowdsourcing materials students from TUR, B&H, RNM, and POL knew 
about and used to learn foreign languages in P1 and P2. It was hoped 
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that comparing students’ answers in P1 and P2 would provide clues 
about the immediate and prolonged effects of online learning on the 
students’ habits, and would enable different stakeholders in education 
to create more suitable and productive learning environments for the 
current and following generations of students based on empirical infor-
mation coming from four distinct countries. 
Analysis of the students’ answers in P1 and P2 revealed some gen-
eral tendencies observed across the four countries and certain country-
specific changes (i.e., different countries were affected differently by 
the pandemic). One common feature was the increase in the number 
of platforms listed by the students in P2. In our first study, the total 
number of platforms reported by the participants was 26 (see Appen-
dix A; for more details, see Miloshevska et al., 2021). Among those, POL 
students stated that they had used 14, TUR and B&H students 13 and 
the participants from RNM had used 8 (i.e., apart from the RNM stu-
dents, the participants coming from the other three countries had expe-
rience with roughly the same number of online platforms). The number 
of platforms listed in P2 was 92 (i.e., 3.5 times more than in P1), and the 
percentage of students who said they had never used any crowdsourc-
ing materials went from 6.6% in P1 down to 2.2% (see Appendix B). 
This finding can, on the one hand, be explained by Chen et al.’s (2020) 
claim that after the outbreak of COVID-19 the number of mobile online 
platforms increased because of the market demand for online educa-
tion and the rise in the number of online platform users. Our partici-
pants could list more digital resources because more platforms cater-
ing to their needs had been created, and they could choose and use 
the ones they needed. Another plausible explanation for the observed 
sharp rise in the number of the listed online resources could be the new 
“strong technology literacy” (Ali, 2022, p. 202) of the students that was 
fostered by the prolonged online teaching and learning environment. In 
the second period examined in this study, P2, the students were still at 
home, away from their university campuses, with limited or no access 
to their teachers, peers, and university libraries. This meant that the 
resources and skills they used to depend on were partially or entirely 
inaccessible to them. But they had already had some experience with 
online learning, and they knew they had to develop new skills and find 
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new resources to help them reach their goals in the new environment. 
And that is what they did. They improved their technology literacy and 
started searching for and using tools that best suited their needs. 
Different to P1, there were clear differences between the number of 
crowdsourcing platforms used by the participants in the four countries. 
The data show that in P2 the TUR students reported using 60, RNM 26, 
B&H 23, and POL 23 of these platforms (i.e., in P2, TUR students used 
4.6 times more crowdsourcing platforms, RNM participants 3.3, B&H 
1.8 and POL 1.6). Based on these findings, it can be argued that TUR 
students were affected the most by the changed teaching mode, RNM 
students were affected moderately, and B&H and POL were affected 
the least. One explanation for the sharp rise in the number of online 
resources employed by TUR students in P2 might come from a study 
conducted by Delibegović Džanić et al. (in press) in TUR, B&H and RNM. 
In that study, students were asked to talk about the positive changes 
brought by online education and TUR students, like the one quoted in 
Example 1, frequently stated that one 
Example 1: TUR Student 71
…positive effect and advantage might be my experiences about us-
ing web 2.0 tools, computer and doing effective search on net to 
get my answer and do my assignments more fruitful.
That is, TUR students viewed their experiences with different on-
line resources as something positive, as something that gave them a 
chance to improve their computer and digital literacy skills. 
Among the 26 crowdsourcing sites listed in P1, six were used by the 
students in all four countries and with relatively high frequency (i.e., Wiki-
pedia (N=158, 74.9%), Kahoot (N=133, 63%), Duolingo (N=130, 61.6%), 
Khan Academy (N=49, 23.2%), Memrise (N=43, 20.4%, Busuu (N=21, 
10%). The remaining 20 platforms were usually rarely employed, and if 
they were, that usage was country-specific (i.e., they were employed in 
only one of the studied countries, e.g., Rosetta Stone in TUR; Flocabulary 
in B&H; Quizlet and Anki in POL) (for more details, see Miloshevska et al., 
2021). In P2, five sources were used in all the studied countries: Duolingo 
(N=75, 40.5%), Google Translate (N-40, 21.6%), Kahoot (N=32, 17.3%), 
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Wikipedia (N=32, 17.3) and YouTube (N=31, 16.8%). Similarly to P1, the 
remaining 87 platforms were utilized less frequently and not across all of 
the examined countries (see Appendix B). 
As shown in Appendix B, the order of popularity and the character-
istics of the most frequently used individual platforms changed from 
P1 to P2. Among the top six resources listed in P1, three were still at 
the top in the later period – Duolingo (N=75, 40.5%), Kahoot (N=32, 
17.3%) and Wikipedia (N=32, 17.3%). However, the number of stu-
dents who reported using them was much smaller. Wikipedia, the over-
whelming favorite crowdsourcing resource before and during the first 
COVID-19 period, as well as Kahoot (N=32, 17.3%), Khan Academy 
(N=5, 2.7%), Memrise (N=3, 1.6%), and Busuu (N=2, 1.1%), were not 
the go-to sites in P2 anymore. In contrast, platforms such as Google 
Translate (N=40, 21.6%) and YouTube (N=31, 16.8%), which just one 
student in P1 mentioned, were now the second and fifth most popular 
crowdsourcing sites, respectively, for the students in TUR, B&H, RNM 
and POL. 
Analyses of the contents and aims of the resources listed by the stu-
dents in P2 showed that they could be grouped under seven categories 
(see Table 3). The biggest of those categories, as in P1, is still the lan-
guage learning and teaching platforms (e.g., Duolingo, Rosetta Stone) 
(N=121, 25.9%), but together with those students reported using re-
sources such as online dictionaries (e.g., Cambridge Online Dictionary, 
Tureng) (N=82, 17.5%), professional development and collaboration 
Table 3: Crowdsourcing resource sub-categories in P2
CATEGORIES N %
1. Language learning and teaching platforms 121 25.9
2. Online dictionaries 82 17.5
3. Professional development and collaboration platforms 70 15
4. Game-based platforms 65 13.9
5. (Digital) TV channels and news media websites 62 13.2
6. Translation and grammar monitoring platforms 58 12.4
7. Social media messaging apps 6 1.3
8. None of the above 4 0.9
ALL 468 100.0
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resources (e.g., Anki, Wikipedia, Udemy) (N=70, 15%), game-based 
platforms (e.g., Kahoot, Scrabble) (N=65, 13.9%), (digital) TV chan-
nels and news media websites (e.g., Netflix, TwitchTV, YouTube, BBC 
websites) (N=62, 13.2%), translation and grammar monitoring plat-
forms (N=58, 12.4%), and social media messaging apps (N=6, 1.3%), 
which were mentioned by only a few students or not mentioned at all 
in P1. 
A closer look at the listed platforms showed that in contrast to plat-
forms such as Wikipedia, Duolingo, Memrise, Khan Academy and Busuu 
that offer general information or guidance related to learning foreign 
languages, in P2 the students started searching for and using more re-
sources that catered to their country-specific and/or individual needs, 
and could fill in the gaps created by the lack of regular, in-person inter-
action with the most reliable sources of information, i.e., their lecturers, 
classmates and on-campus libraries.
4.1 Language Learning and Teaching Platforms
Students who participated in the P2 study listed 14 language learn-
ing and teaching platforms (LLTP) in total, and they formed 25.9% of 
all mentioned resources (121/468) (see Appendix B). Among those, 
Duolingo was the most popular tool (overall mentioned by 40.5% of 
the students in P2) and the only one named by the participants in all 
four countries (TUR: N=26, 43.3%; B&H: N=16, 34.8%; RNM: N=19, 
38.8%; POL: N=14, 46.7%). However, when compared with P1, it was 
seen that even its popularity dropped 1.5 times in P2, as in P1, it was 
mentioned by 61.6% of the students. 
One possible explanation for the fall in popularity of Duolingo in P2 
in TUR, B&H, RNM and POL could come from Nadhifah and Puspitasari 
(2021), who worked with beginner- and intermediate-level under-
graduate students in Indonesia. The students used Duolingo to learn 
English during the COVID-19 period as a self-learning tool. The results 
of the study showed that while beginner-level users felt satisfied with 
Duolingo since it was fun, easy to use and helped them develop their 
knowledge related to basic structures in English, the intermediate-lev-
el students reported that the platform did not really help in improving 
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their target language skills. They maintained that it was a bit boring and 
too easy, but the more critical problem for them was the lack of discus-
sion boards on the application. That is, the already isolated learners 
did not have a chance to share their experiences with each other while 
using Duolingo, and felt they “needed a place to share and to interact 
with the other users about their experience during using this applica-
tion” (Nadhifah and Puspitasari, 2021, p. 308). As such, two things in 
Nadhifah and Puspitasari’s work (2021) are particularly relevant to the 
current study. First, the students who participated in our P2 study were 
predominantly advanced learners of English (79.5% of the informants 
classified themselves as proficient users). Duolingo, a novel and excit-
ing platform to use in P1, was now not satisfying their needs as ad-
vanced learners in P2. Second, the opportunity for social interaction, 
which has been shown to motivate students in self-regulated learning 
(Zimmerman and Schunk, 2001), was missing in Duolingo. This aspect 
of the Duolingo that was mentioned as a notable disadvantage by In-
donesian students might have been a critical drawback for TUR, RNM, 
B&H and POL students when choosing a self-regulated learning plat-
form in P2, too.
Among the remaining 13 platforms, Quizlet (listed by 14.1% of the 
students in P2), which only POL students mentioned in P1, was listed 
by the TUR, RNM and POL participants in P2. It was the second most 
popular platform overall in P2, and the most popular platform in POL 
(70%) once more. Quizlet is described as a “multi-facet CALL software” 
(Toy, 2019, p. 26) that can also be used as an online learning platform 
by both teachers and language learners. One reason why it was used 
by POL, TUR and RNM students in P2 could be the fact that it combines 
the benefits of classroom interactivity with personal self-study (Kose 
et al., 2016), and when using Quizlet, students can learn at their own 
pace and meet their individual needs better, in a fun manner. As seen 
in Example 2, it looks as if these features of Quizlet appealed to the 
student in three of the studied countries, and they started using it more 
in P2.
Example 2: RNM student 16 (from Delibegović Džanić et al., in press)
I have more free time since I can organize my time more freely.
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Khan Academy, Memrise and Rosetta Stone were listed by TUR and 
RNM students, while the remaining nine platforms were mentioned by 
either one or two students in a single country (e.g., Lingodeer in TUR, 
Lingvist in RNM). 
4.2 Online Dictionaries
“The importance of dictionaries in language learning is indisputable” 
(Jin and Deifel, 2013, p. 515), as they help language learners under-
stand new words’ meanings, (contextual) usage, and grammatical fea-
tures. With the creation of online dictionaries, students can now not 
only read and/or try to guess the pronunciation, intonation, and stress 
patterns of the words they encounter, but can also listen to and prac-
tice saying them. Jin and Deifel, in their 2013 study, claimed that “the 
emergence of online dictionaries has noticeably influenced the way 
students learn a foreign language” (p. 515). 
Despite these benefits and claims, online dictionaries and thesau-
ruses were not listed among the crowdsourcing materials students had 
used to learn foreign languages in our first study. This picture changed 
dramatically in P2, where they were the second most frequently men-
tioned group of resources (see Table 4). Students listed 18 online dic-
tionaries and thesauruses in total, and more than half of the students 
in POL (N=20, 66.6%), TUR (N=34, 56.7%) and B&H (N=54.3%) said 
they were using these to learn foreign languages. The exceptional 
group was the RNM students, among whom only three (6.1%) listed 
any online dictionaries and thesauruses. 
A closer look at the types and characteristics of the listed dictionar-
ies shows that students not only consulted the “known”/“global” sourc-
es (e.g., Cambridge, Oxford, Longman), but they also started depend-
ing more on locally created online dictionaries (e.g., Tureng for TUR 
students, DIKI for the POL group) where entries related to language-/
culture-specific terms, idioms and phrases, usually missing from the 
“general” sources, are included. Two such examples are the Tureng 
Online Dictionary (https://tureng.com/tr/turkce-ingilizce) initiated by 
a Turkish translation company, and DIKI: Słownik Angielsko-Polski, 
Słownik Angielski Online (www.diki.pl), whose webserver is in Warsaw, 
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Poland. Tureng was the second most frequently used dictionary by TUR 
students after the Cambridge Online Dictionary, and as shown in Fig-
ure 2 it includes translations for language-specific idiomatic expres-
sions such as “Ellerine sağlık”. This phrase, whose literal translation is 
“Health to your hands”, is a speech act that native speakers of Turkish 
use to compliment and express gratitude to their interlocutors simul-
taneously. Entries related to such phrases are included in Tureng, and 
if the speakers of the language think that their translations, definitions, 
and explanations should be broadened and/or refined, they can do that 
via a specific tab/function on the platform (see Figure 2). This, in turn, 
means that language learners have dictionaries on which they can rely 
Table 4: Online Dictionaries used for language learning in P2
TUR B&H MAC POL ALL
Tools n % n % n % n % n %
1. 3. BAB.LA 1 2.0     1 0.5
2. 10. Cambridge (Online) Dictionary 13 21.7 2 4.3 15 8.1
3. 13. Diki 6 20.0 6 3.2
4. 20. English idioms and phrases 1 2.2     1 0.5
5. 29. Glosbe 5 10.9 1 3.3 6 3.2
6. 39. Linguee 1 3.3 1 0.5
7. 41.
Longman (Online) 
Dictionary 1 1.7 1 2.2     2 1.1
8. 51. One Look Thesaurus (online) 2 3.3         2 1.1
9. 52. Online dictionaries 4 6.7 12 26.1 9 30.0 25 13.6
10. 54. Oxford Online Dictionary 3 5.0 4 8.7     7 3.8
11. 55. Ozdic 2 3.3         2 1.1
12. 58. Pons 2 6.7 2 1.1
13. 64. Relatedwords.org 1 1.7 1 0.5
14. 69. SpanishDict 1 2.0 1 0.5
15. 75. TheFreeDictionary 1 1.7     1 0.5
16. 76. Tureng (online dictionary) 7 11.7     7 3.8
17. 82. Urban Dictionary 1 2.0 1 0.5
18. 88. Word Reference 1 3.3 1 0.5
ALL 34 56.7 25 54.3 3 6.1 20 66.7 82 44.3
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for translating phrases that they frequently use in their first language, 
want to use in their target language texts, but usually are not found in 
other dictionaries. Such dictionaries save time and maybe allow them 
to complete their work faster.
Another essential characteristic of some of the dictionaries listed 
by the students was that they were based on and/or benefited from the 
research done in corpus linguistics (e.g., Ozdic, Relatedwords.org, Bab.
la, English Idioms and phrases). These new generation dictionaries are 
based on available corpora (e.g., the British National Corpus), and are 
regularly updated using internet searches to ensure “the most up-to-
date usage for fast changing areas of language”2. Another advantage 
of these dictionaries is that they present easily searchable information 
related to collocations which are words or phrases that are 
often used with another word or phrase, in a way that sounds cor-
rect to people who have spoken the language all their lives but might 
not be expected from the meaning, e.g., “a hard frost” but not “a strong 
frost” in English. (Cambridge Online Dictionary)3
Figure 2: TURENG Dictionary.4 
Such information is essential for language learners, since research 
shows that even advanced learners of English have problems master-
ing collocations (Laufer and Waldman, 2011). 
2 https://ozdic.com/
3 https://dictionary.cambridge.org/dictionary/english/collocation?q=collocations
4 https://tureng.com/en/turkish-english 
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In addition to the above, this new generation of dictionaries (e.g., 
Ozdic) present the material in context with grammar and register infor-
mation (e.g., daily/informal vs academic vs formal writing) as well as 
natural word combinations and alternatives. All of these help learners 
write using more “native-like language”, and they are able to access 
and check that information quickly and for free. 
All these features of the online dictionaries combined with the 
effects of “forced partial or complete isolation” during the second 
COVID-19 online learning period can explain the sharp increase in 
the use of these sources. Deprived of access to their teachers, peers 
and libraries, students had to find new, fast and reliable means to 
help them with the tasks at hand. Rundell (2014, p. 1) argues that 
“with easy access to numerous free reference sites, users search-
ing for lexical information have a huge variety of options”, and they 
choose online dictionaries because they include all the information 
contained in paper dictionaries but also materials that go “far beyond 
the traditional focus of ‘the dictionary’” (Rundell, 2014, p. 6). That is, 
they include, 
language games, pedagogically-oriented videos, downloadable 
teaching materials, a weekly column on new words, and an active blog 
with regular contributions on a variety of language issues from both 
Macmillan’s own editors and over a hundred guest bloggers. (Rundell, 
2014, p. 6).
Stated differently, online dictionaries include many essential fea-
tures that paper dictionaries, peers and lecturers provided in some way 
or another during the face-to-face teaching periods. 
Besides the listed advantages of online dictionaries for language 
learners, another reason for using such a considerable number and 
wide variety of these resources might be the heavy course load, and 
the high number of homework projects assigned to students during the 
semesters taught online. In a study conducted in TUR, B&H and RNM 
(Delibegović Džanić et  al., in press), students complained about the 
much heavier workload with the online teaching model, and how they 
struggled to complete their assignments even though they were study-
ing much harder (see Example 3). 
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Crowdsourcing and language learning habits and practices...
Example 3: TUR Student 20
I was studying regularly, but now, it is hard for me to focus on 
my homework not only because it is online, but also I have more 
course load than before. It is hard for me to catch up with all of the 
courses.
During the online teaching periods, students were deprived of the 
systems they knew well and worked well for them (i.e., face-to-face 
classes where they worked closely with their lecturers and peers). They 
were on their own, and had a greater workload to deal with. Rundell 
(2014) and Trinh et al. (2021, p. 29) compared paper and digital dic-
tionaries and argued that one specific advantage of the latter is that 
their “users can access and search large amounts of information quick-
ly”. Similarly, Li and Xu (2015) maintain that online resources would 
gradually replace bulky and outdated paper dictionaries, because with 
digital dictionaries the information retrieval rate is fast and using them 
is less time-consuming.
In short, during the first COVID-19 period the students did not use 
online dictionaries, and perhaps did not even know about many of them. 
However, it looks as if the combination of factors such as lack of access 
to known and reliable sources and an increase in workload during the 
second COVID-19 period forced students to look for new resources 
that would help them complete their assignments in a quick, reliable 
and high-quality manner, and thus they turned to online dictionaries. 
4.3 Professional Development and Collaboration Platforms 
(PDCP)
Platforms that aim to or can support (pre-service) language teachers 
with their development as educators were included in the ‘professional 
development and collaboration platform’ (PDCP) category. These plat-
forms had either one, a combination of or all of the content and charac-
teristics listed below:
(i) Include resources (e.g., lessons, videos, interactive learning mod-
ules, texts) that directly support users in acquiring knowledge and 
skills.
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(ii) Allow users to build online courses on various topics.
(iii) Contain course development tools that platform users can use to 
upload materials that foreign language learners might find help-
ful (e.g., texts, audios, videos, PowerPoint presentations, PDFs, ZIP 
files, source code for developers).
(iv) Allow users to engage and interact with each other via online dis-
cussion boards.
In P2, students listed 28 different PDCP platforms, which formed 
15% of all crowdsourcing resources (see Table 3 and Appendix B). 
Apart from Wikipedia and Fiszkoteka, none of the remaining 26 PDCP 
were listed in our P1 study. Among the 28 platforms, TUR students 
stated that they used 15 (e.g., Udemy (N=2), Wordwall (N=2) and Fan-
dom (N=2)), POL informants six (e.g., Anki (N=2), Fiszkoteka), the B&H 
(e.g., Eng Vid, FunEasyLearn) and RNM (e.g., Coursera, Flocabulary) 
groups five each.
There were a large number of platforms listed in P2 (N=28), but apart 
from Wikipedia (N=32, 17.3%), which the participants in all four countries 
named, each of the other resources were only mentioned by informants 
from one country. In our opinion, this emphasizes once more not only the 
richness of such resources (Chen et al., 2020; Kansal et al., 2021), but 
also the search of students for materials that best suit their needs. It thus 
looks as if the second COVID-19 period was a period of self-discovery, 
context discovery and switching from a teacher dependent to a more au-
tonomous learner profile (also see Hatipoğlu et al., 2022).
In TUR, B&H, and POL the students were pre-service language 
teachers, and they all had to create high-quality work rapidly and by 
themselves in the second and third COVID-19 semesters. Each country 
followed, however, different rules and regulations regarding teaching 
policies at the tertiary level (Miloshevska et al., 2020) and practicum 
courses (Ersin et al., 2020; Krajka, 2021) during the lockdown periods. 
The participants in our study were also in a unique position, since while 
trying to expand their knowledge and English skills they also had to 
think about the best resources to help them develop the most suitable 
materials for the students in their practicum classes. Additionally, there 
were native language and cultural differences between the participant 
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groups and the students with whom they were expected to interact in 
their school practice classes. Therefore, each group of students were 
on their own journey of discovery. They had to assess and understand 
what was required from them in their unique contexts, and then search 
for and identify the resources that catered best to their needs. Because 
of the lack of recommended platforms by the various ministries of edu-
cation (Krajka, 2021), it is likely that the novice users of crowdsourcing 
platforms did not get it right the first time and had to search for and find 
something that would better fit their needs. Hence the large number of 
platforms listed in this category. 
A closer look at the platforms employed by the students showed 
that they were varied in quality (i.e., from the most general to the more 
specific ones), content, information presentation, teaching and assess-
ment styles and practices. The first category (i.e., General Resources) 
of PDCP included electronic libraries and encyclopedias (Wikipedia), 
where students could find entries on numerous topics, academic and 
non-academic journals, and educational and general-interest books. 
The materials from these platforms were then either used in students’ 
projects or as texts that could be taken and adapted to the needs of 
students in their practicum classes as they were of different ages and 
had different proficiency levels. 
The second group of PDCP were the ‘Job specialized platforms’ 
that allow users to create courses and materials tailored to their stu-
dents’ needs. These are platforms like Udemy, where educators have a 
comprehensive collection of tools (e.g., videos, source code for devel-
opers, PowerPoint presentations, PDFs, audio, ZIP files and any oth-
er content that learners might find helpful) that they can use to build 
online courses on specific topics. These platforms also allow course/
material creators to engage and interact with their students and col-
leagues via online discussion boards. 
The richest sub-group of PDCP was Group 3, which included tools 
with which materials for more specialized purposes could be created. 
These aim at developing particular types of knowledge (grammar, 
vocabulary) or skills for foreign language learners (speaking, writ-
ing, test-taking skills), and include platforms such as Worldwall,5 
5 https://wordwall.net/
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Spike Notes,6 Easy Languages,7 engVid,8 FunEasyLearn,9 Coursera,10 
Flocabulary,11 Vocaroo,12 Fiszkoteka13 (for the full list see Appendix B). 
The list of platforms in this group was the longest, proving once again 
that students were trying to find the ones that fit their needs the best.
What is more, depending on the severity of the pandemic in the 
examined countries and the level of access of the students to the inter-
net and the required equipment (e.g., PC computers, laptops, smart-
phones), the various ministries of education planned and organized ed-
ucation in K-12 and tertiary education differently. In TUR, for instance, 
after it became clear that a considerable number of K-12 students had 
either no or limited access to the internet, in addition to strengthening 
the infrastructure of the already existing Educational Informatics Net-
work (EBA) and introducing EBA-TV, the Ministry of National Education 
collaborated with the Turkish Radio and Television Corporation (TRT) 
and started airing the K-12 lessons on TRT channels (Özer, 2020). This 
change in policy forced pre-service teachers to prepare different ma-
terials for different modes of practicum applications, which in turn re-
quired the usage of a bigger number of crowdsourcing materials. 
4.4 Game-based Platforms
Kahoot was the only game-based platform listed by the students in 
P1 (Miloshevska et al., 2021). It was a popular tool in all of the stud-
ied countries. Overall, it was used by 63% of the participants, but it 
was a particularly popular platform in POL and TUR, where respec-
tively, 93.1% and 83.7% of the students stated that they had used it 
to learn languages (see Appendix A). 
In P2, game-based platforms were the fourth most frequently 
used resource, while in P1 they formed only 13.9% of all mentioned 
resources (see Table 3). For P2 the participants listed nine platforms, 
6 https://www.sparknotes.com/
7 https://www.easy-languages.org/
8 https://www.engvid.com/
9 https://www.funeasylearn.com/
10 https://www.coursera.org/
11 https://www.flocabulary.com/
12 https://vocaroo.com/
13 https://fiszkoteka.pl/
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among which Kahoot was the most frequently mentioned one once 
again. However, when compared with P1, it can be seen that both over-
all and for individual countries, Kahoot’s popularity dropped signifi-
cantly. In P2, it was used overall by 3.7 times fewer students and by 
only 25% of TUR, 20% of the POL, 17.4% of the B&H and only 6.1% of 
RNM students (see Table 5). 
Table 5: Game-based platforms used in P2
TUR B&H RNM POL ALL
Tools n % n % n % n % n %
1. 28. Gamepedia 1 1.7 1 2.2 2 1.1
2. 34. Kahoot 15 25 8 17.4 3 6.1 6 20 32 17.3
3. 44. Minecraft 1 2.2 1 2 2 1.1
4. 53. Online games 1 1.7 5 10.9 11 22.4 2 6.7 19 9
5. 60. Quizizz 1 1.7 1 0.5
6. 67. Scrabble 3 6.5 3 1.6
7. 71. Steam language games 1 1.7 1 0.5
8. 83. Video games 2 3.3 2 4.1 4 2.2
9. 89. Word search (Puzzles) 1 2.2 1 0.5
TOTAL 21 35 19 41.3 17 34.7 8 26.7 65 35.1
Another sub-category listed by participants in all four countries 
was the generic ‘online games’ group (N=19.9%). The category with 
the third highest percentage was a generic one, too – ‘video games’ 
(N=4, 2.2%). The presence of these two categories in the collected cor-
pus was interpreted as the respondents saying “there are many online 
and video games that we use but not one, in particular, that is worth 
mentioning here”. The remaining six games were mentioned by three 
or fewer students. 
It has been known for a while now that online and video games 
can be effective language-learning tools (Hung, 2019; McNeil, 2020; 
Thorne and Reinhardt, 2008), since they offer benefits such as engag-
ing dialogues, the opportunity to listen to various accents in English/
the target language, exposure to a variety of grammar structures, new 
vocabulary, stress relief, and also the possibility of making new friends 
all at once. Therefore, in a study done before the COVID-19 pandemic, 
Hung (2019) argued that the “use of learning games in educational 
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contexts has expanded significantly, leading to the emergence of 
game-based learning as a recognized field of study” (p. 89). However, 
it looks as if, at least for this specific group of students, the situation 
changed with the outbreak of the pandemic. The changed conditions 
led to the replacement of online or video games with online diction-
aries, grammar-checking programs, (digital) TV channels and social 
media platforms. In our opinion, this shift happened because the lat-
ter group of platforms responded to the overwhelmed students’ needs 
faster, and provided a richer set of materials. 
4.5. (Digital) TV Channels and News Media Websites
In P1, YouTube and ‘movies and books’ were entries listed by one B&H 
and one TUR student, respectively, while ‘news media websites’ were 
not mentioned at all. Stated differently, ‘(digital) TV channels and news 
media websites’ (TVC&NMW) was an almost non-existent category in 
the pre-COVID-19 study. The picture changed in P2 when a total of 
twelve TVC&NMW were listed 62 times (13.2% of all mentioned re-
sources) (see Table 6). Overall, 33.5% of the participants stated that 
they used one or a combination of these to learn languages. 
When the lists of (digital) TV channels and news websites were 
compared, we saw that TV channels were used much more frequently. 
They formed 89% of the TVC&NMW resources (N=55), and websites 
comprised the remaining 11% (N=7). The most popular digital chan-
nel, as well as a resource in this group, was YouTube. It was listed in 
all four countries and by 16.8% (N=31) of all students. There might 
be two reasons why YouTube, which was mentioned only once in P1, 
became such a popular resource during the pandemic. First, with its 
ever-growing content, YouTube has turned into an enormously rich li-
brary where users can easily find incredible amounts of information 
presented through multimodal means (Bloom and Johnston, 2010). In 
a period when not all teachers and institutions could supply all of the 
needed materials to their students, YouTube became a great alterna-
tive or supplement to books and lectures. Second, as early as 2013, 
Clarkson, in her book entitled Usage of Social Network Sites amongst 
University Students, argued that millions of people use platforms such 
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as Facebook and YouTube to connect with each other based on shared 
interests, political views, or activities. That is, in a period of social isola-
tion, YouTube became a safe space for learning communities “where 
everyone has a voice [and] anyone can contribute” (Educase Learning 
Initiative, 2006, p. 2). 
Other popular resources in the TVC&NMW group were Netflix (N=9, 
4.9%) and the generic category ‘movies’ (N=7, 3.8%). When discussing 
Netflix, some students listed generic categories such as ‘movies and/or 
series on Netflix’ while others specifically mentioned series like A Life 
on Our Planet, Explained, and History 101 as beneficial resources for 
foreign language development. When discussing the benefits of watch-
ing movies and soap operas, Bhusal et al. (2020) argued that besides 
being “very good time passing activities”, they could also be good mo-
tivators and, when related to our areas of study, further help with learn-
ing some additional content. They can also help foreign language learn-
ers with their vocabulary, pronunciation and listening comprehension. 
What is interesting about the specifically mentioned programs on 
Netflix is that they are typically short (e.g., Explained is less than 20 min 
long) but tackle some key topics (e.g., A Life on Our Planet follows David 
Table 6: TV Channels and New Media Websites used in P2 TV Channels and 
New Media Websites used in P2
TUR B&H RNM POL ALL
Tools n % N % n % n % n %
1. 5. BBC Learning English 1 1.7 1 2.0 1 3.3 3 1.6
2. 16. DW Deutsch lernen 2 3.3 2 1.1
3. 46. Movies 7 14.3 7 3.8
4. 48. Netflix 7 11.7 2 4.1 9 4.9
5. 49. News Websites 1 1.7 1 0.5
6. 57. Podcasts 2 3.3 2 1.1
7. 73. Ted Talks 1 1.7 1 2.2 2 1.1
8. 78. TV5monde 2 3.3 2 1.1
9. 79. Twitch.tv 1 2.0 1 0.5
10. 84. Voice of America (VOA) 1 1.7 1 0.5
11. 91. Younglish 1 1.7 1 0.5
12. 92. YouTube 14 23.3 9 19.6 6 12.2 2 6.7 31 16.8
ALL 32 53.3 10 21.7 17 34.7 3 10.0 62 33.5
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Attenborough, who maps the sharp decrease in our planet’s biodiver-
sity; Explained focuses on issues such as money, the mind, and voting) 
that are also usually taught in foreign language classes. As such, one 
reason for the popularity of those programs could be their versatility. 
That is, by watching these programs, language learners can improve 
their target language knowledge, while pre-service language teachers 
might also use them as teaching materials in their practicum classes.
Four news media websites (including BBC Learning English and Voice 
of America) were mentioned by the students in P2, and they formed 
3.8% (N=7) of all resources. The number of these sites is relatively 
small, but keeping in mind that they were not mentioned at all in P1, it is 
encouraging to know that the students were searching for and exploring 
new resources that have been proven to help others with their foreign 
language proficiency development. In a study conducted with Indone-
sian students learning English at the tertiary level, Barella and Linarih 
(2020) asked participants to listen to the news on various news web-
sites as an extensive listening activity twice a week, and to keep listening 
logs where they note the names of the websites, the types and lengths 
of the news shows, and ask and answer questions related to the con-
tent of the material they listened to. Similarly to the participants in our 
study, Indonesian students found VOA Learning English and BBC News, 
as well as the CNN and National Geographic websites, useful sources in 
helping them develop their foreign language skills. Ninety percent of the 
students in Barella and Linarih’s (2020 study stated that learning English 
while listening to the news increased their motivation and made learning 
fun. They also argued that the extensive listening exercises helped im-
prove their listening and speaking skills and expanded their vocabulary. 
A close look at the data also showed that there were important dif-
ferences between the four studied countries in the use of TVC&NMW. 
Within the TUR group, 53.3% of all students stated that they had used 
TVC&NMW to learn languages, and listed 10 of the 12 resources in this 
group. In the remaining three countries, both the number of tools listed 
and the percentages of the students who employed them were much 
lower. The RNM group listed three sources, while the B&H and POL 
groups listed only two. When the percentages of the students helped 
by these tools are compared, it can be seen that 34.7% of the RNM, 
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21.7% of B&H and only 10% of POL students listed those resources. 
Once again, it can be seen that online teaching had a different effects 
in the examined countries.
4.6 Translation and Grammar Monitoring Programs
Writing in a foreign language is a complex and challenging task (Uluçay 
and Hatipoğlu, 2017). To write acceptable texts in their non-native lan-
guage, students must know the target language’s spelling, punctua-
tion, and grammar (Hatipoğlu and Algi, 2018). They must also master 
the register and genre-specific vocabulary and use them appropriately 
(e.g., collocations, idioms, proverbs). Finally, after creating the first 
draft, they must revise, reorganize, and edit their texts, keeping in mind 
language and culture-specific rhetoric rules (Bakry and Alsamadani, 
2015; Sokolik, 2003).
Because of all these difficulties associated with writing in a foreign 
language, and due to the increased rate of communication in English in 
the last few decades, several companies and research groups have cre-
ated and developed various pieces of software that can help learners 
of foreign languages with their grammar, translation and paraphrasing 
in their target language. Some popular programs are Google Translate, 
Grammarly, and ReversoContext.
Table 7: Translation and Grammar Monitoring Tools used in P2
TUR B&H RNM POL ALL
Tools n % n % n % n % n %
1. 11. Conjugato 1 3.3 1 0.5
2. 30. Google Translate 8 13.3 16 34.8 16 32.7 40 21.6
3. 31. Grammarly 5 8.3 3 6.1 1 3.3 9 4.9
4. 59. Quillbot  
(paraphrasing tool) 1 2.0 1 0.5
5. 65. ReversoContext 6 20.0 6 3.2
6. 77. Turnitin (feedback) 1 1.7 1 0.5
ALL 14 23.3 16 34.8 20 40.8 8 26.7 58 31.4
However, in P1, Grammarly and Google Translate were listed by 
just one student and from only one country (TUR). None of the other 
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translation and computer-mediated corrective feedback digital tools 
were mentioned. That is, before and during the first emergency COV-
ID-19 period, such tools were not among the ones students in the 
studied countries were (often) using to learn or improve their foreign 
languages. This was not the case in P2 (see Table 7), when, overall, 
31.4% of students started utilizing translation and corrective feedback 
tools to improve their target languages. In the second study, those 
tools were listed by 40.8% of RNM, 34.8% of B&H, 26.7% of POL and 
23.3% of TUR students. The participants in the second study listed six 
tools, and Google Translate was the most popular one in TUR, B&H and 
RNM. The preferred translation tool for the POL students was Reverso-
Context, which was listed by 20% of them. 
One explanation for why students started using translation pro-
grams more during the second COVID-19 period, as mentioned above, 
could be the need to create good-quality papers in a short time, and 
research in the field shows that such programs can help in this regard. 
Tsai (2019) worked with native speakers of Chinese learning English 
and asked them to (1) write an essay in Chinese, (2) draft the same es-
say in English, (3) translate the Chinese essay into English using Google 
Translate. When the self-written and Google translated texts of the stu-
dents were compared, it was found that the ones created using Google 
Translate “presented a number of components of significantly higher 
writing quality than those of students’ SW (self-written) texts, by having 
more words, fewer mistakes in spelling and grammar, and fewer errors 
per words” (Tsai, 2019, p. 510). There were also more advanced-level 
words in the texts created with Google Translate. 
The most popular grammar monitoring program in the examined 
countries was Grammarly, which was listed by TUR, RNM and POL stu-
dents (N=9, 4.9%). Grammarly is a cloud-based typing assistant that 
identifies duplicate content and reviews grammar, vocabulary, me-
chanics (spelling, punctuation errors), as well as language style and 
delivery mistakes (Bailey and Lee, 2020; Barrot, 2020). One of the 
more critical advantages of this program mentioned in the literature is 
that it reduces the errors related to “vocabulary usages (diction), lan-
guage use (grammar), and mechanics of writing (spelling and punc-
tuation)” (Ghufron and Rosyida, 2018, p. 395; also see Bailey & Lee, 
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2020; Barrot, 2020). However, it is usually found to be less effective 
in improving “the content and organization of students’ EFL writing” 
(Ghufron and Rosyida, 2018, p. 395). It thus looks as if the participants 
in our study employed Grammarly to quickly clean up their texts with 
regard to problems related to vocabulary, grammar usage, spelling and 
punctuation in order to concentrate more on the content and organi-
zation of their work, as well as on issues stemming from the possible 
influence of their cultural and first language norms of writing. 
What is more, in a study done with Indonesian English education 
study program students, it was shown that when used to teach read-
ing and writing in English as a foreign language, Grammarly, in com-
bination with digital tools such as Telegram, WhatsApp, Google Meet, 
YouTube, and a Plagiarism Checker, made a positive contribution not 
only to the development of the students’ proficiency in their target 
language, but also to the improvement of their knowledge related to 
these new digital tools and their self-esteem and belief in themselves 
(Setyowati et al., 2021). 
The lockdown periods during COVID-19 took students away from 
the known and conventional face-to-face classrooms and pushed them 
into the technology-based online instruction environment. With the un-
certainty of when the pandemic would end, they had two options: to 
give up and freeze their semesters, or to adapt as quickly as possible 
and continue fighting and learning. From the information gathered in 
the current study, it looks as if many of the students chose the latter 
approach. 
4.7 Social Media Messaging Apps
The ‘social media messaging apps’ (SMMA) category was a non-exist-
ent category in our first study. None of the 211 students in P1 men-
tioned any SMMA as tools they had used to learn foreign languages. 
In P2, the participants listed SMMA six times, accounting for 3.2% of 
all resources in the second study (see Table 8). Still, the number of 
learners who found them helpful in supporting their target language 
development was relatively small compared to the other categories in 
the current study. 
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Another important fact related to the use of this category is that, 
once again, the bulk of the students who treated them as foreign lan-
guage learning tools were from TUR (four out of six students, 67%). 
Only one student from B&H and one from POL stated that they used 
Reddit.com and Discord, respectively, while none of the students from 
the RNM listed any SMMA. One possible reason for the observed dif-
ference could be the more positive approach to using SMMA in educa-
tional settings and teacher education in Turkey in the last two decades. 
Such applications are seen as an intelligent employment of existing 
technologies in the classroom (Mendez et al., 2009, p.1), and it is be-
lieved that teacher education can benefit from such applications in 
two ways. First, SMMA can be used to enhance (pre- and in-service) 
teachers’ learning and preparation for the job, and second, in language 
classrooms, where social media tools could make the learning environ-
ment more engaging and benefit the teaching of the target language 
(Albion, 2008). Balçıkanlı (2010), who examined the effects of social 
networking on pre-service English teachers’ metacognitive awareness 
and teaching practices in Turkey, found that they both were positively 
affected. 
Table 8: Messaging Apps used in P2
TUR B&H RNM POL ALL
Messaging Apps n % n % n % n % n %
1. 8. Bottled 1 1.7 1 0.5
2. 14. Discord 1 3.3 1 0.5
3. 56. Plotagon 1 1.7 1 0.5
4. 63. Reddit.com 1 1.7 1 2.2 2 1.1
5. 68. Slowly (Twitter app) 1 1.7 1 0.5
ALL 4 6.8 1 2.2 1 3.3 6 3.2
The introduction of this new group of tools among the resources 
that students use to learn foreign languages could also be seen as a 
support for Chic and Benson’s (2020) claim that online media aware-
ness was particularly high during the COVID-19 pandemic, since most 
of the world had to adopt digital, online means of working. Language 
learners, like others, had to start reading, writing and learning online 
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(i.e., they had to start doing things differently), because this was the 
only way of accessing the information they needed during this time. 
What is more, they saw the SMMA, as Barlett-Brag (2006, p. 3) pre-
dicted and described, as a “range of applications that augments group 
interactions and shared spaces for collaboration, social connections, 
and aggregates information exchanges in a web-based environment”. 
Once again, students showed flexibility and initiative. When the world 
required them to change, young people analyzed the situation correctly 
and adapted accordingly. 
5 Conclusions
Education systems around the world have changed because of the 
COVID-19 pandemic, but what about the learning of foreign languages 
in this context? 
To contribute to answering a part of this important question, the 
current study focused on four distinct countries – TUR, B&H, RNM and 
POL – and tried to find answers to the following two research questions: 
(1) What digital crowdsourcing resources did students in TUR, B&H, 
RNM and POL know about and use to learn foreign languages in the 
pre- and first- COVID-19 (P1) versus late COVID-19 periods (P2)?
(2) Were there any changes in the frequencies, attitudes, contexts of 
use, and habits related to crowdsourcing materials of language 
learners in TUR, B&H, RNM and POL in P2 when compared to P1? 
The results of the study show that in general terms the answers 
to these questions are, respectively, “many and varied” and “yes”. The 
attitudes, knowledge and use of crowdsourcing materials by language 
learners changed from the beginning towards the later phases of the 
pandemic. Overall, the study’s findings show that the shift from face-
to-face to online learning because of COVID-19 significantly affected 
the development and use of crowdsourcing materials in the studied 
countries.
In parallel to some earlier studies in the field (e.g., Krishnan et al., 
2020), our findings show that the second COVID-19 period (P2) was 
marked by the use of a much richer range of digital resources when 
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compared with P1. This, in our opinion, points to three facts. One, as 
claimed in some previous studies (Chen et  al., 2020; Kansal et  al., 
2021), the number of such resources increased, and the students 
could find, test, and select the ones that fit their needs better. Second, 
with the experience they gained in P1, students became more skillful 
in searching for, finding and using such resources. Learners now had 
a stronger technological literacy and could use not just one or two but 
many digital platforms. Three, the students became more autonomous 
learners, able to better understand their specific contexts and what 
was expected from them in these (i.e., create high-quality work within 
a short period of time on your own). There was a realization that they 
had to switch to a self-regulated learning program because they were 
the only people who knew what they needed. Therefore, they had to 
plan and monitor their own actions. They had to search for, find and 
use the platforms that they thought best fit their needs; and, in the 
end, they had to reflect on the outcomes of their actions. Since dif-
ferent countries had different expectations from their students, each 
group needed to follow different paths. Therefore, only a limited num-
ber of the listed resources overlapped among the examined countries. 
The results once again showed students’ ability to read their contexts 
well, and their success in adapting accordingly.
It was also seen that there was not only an increase in the number 
of resources used, but there was also a change in the students’ expec-
tations from the platforms and, as a result, in their features. Resources 
that were popular in P1 (e.g., Wikipedia, Kahoot) became less popular 
in P2, and the ones that were not mentioned at all or very rarely men-
tioned in P1 came to the fore (e.g., online dictionaries, YouTube, social 
media platforms). There was a general shift from the more general re-
sources to the more needs and country specific ones (e.g., the use of 
DIKI by POL and Tureng by TUR students). The ones that presented 
more tailor-made opportunities for the personal development of the 
students were the ones that were selected. 
It looks as if the students’ attitudes towards and expectations from 
crowdsourcing platforms also changed in P2. They were not only good 
sources of information for the students but also safe spaces where us-
ers were able to connect with people with similar interests and views, 
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and jointly work on different projects (i.e., they were spaces where 
“communities of practices” were formed, see Wenger, 1998). The study 
also seems to support the claim (Schunk and Zimmerman, 2001) that 
social interaction is a prerequisite for an increase in student motivation 
and progress in self-regulated learning. Platforms that did not allow 
for such collaboration lost popularity (e.g., Duolingo in P2). The results 
also showed that a combination of factors such as isolation, lack of ac-
cess to familiar resources (e.g., teachers, peers, university libraries), 
increase in workload, and lack of support on the part of institutions 
might have led to this shift. 
The study also found that when it comes to the use of crowdsourc-
ing materials, the changes observed in TUR were much more notice-
able than in B&H, RNM and POL. When compared with P1, in P2, TUR 
students listed the widest variety and biggest number of digital tools 
among the four groups of students. Factors that might have contrib-
uted to the observed differences could be varying policies related to 
the education practices in K-12 and tertiary education in the exam-
ined countries, and decisions related to practicum classes, workload 
requirements, and cultural differences. 
It is hoped that the findings of the study will serve as potential 
guidelines for language teachers who plan to incorporate crowdsourc-
ing activities into their in- and out-of-class activities in the future. 
However, they might also provide essential feedback to both groups 
of platform creators: the ones who aim to design resources that are 
valid cross-culturally and those who are seeking to create platforms 
that would cater to language learners with more specialized needs and 
interests. It is believed that incorporating crowdsourcing resources in 
language curricula can provide students with more in- and out-of-class 
collaboration opportunities and more active language learning, which, 
in turn, will lead to the development of more independent, active and 
confident language learners. 
Despite the careful collection and analysis of the data sets dis-
cussed in this study, it should be mentioned that this research was 
based on questionnaire data gathered from relatively small and unequal 
(e.g., across countries and genders) samples of students in TUR, B&H, 
RNM and POL. Therefore, studies where bigger samples and additional 
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data collection tools (e.g., interviews, observations, exam results and 
student projects) are employed should be conducted to enhance our 
understanding of the real changes in the use of crowdsourcing materi-
als for foreign language learning, not only in our four countries but also 
in others that had similar experiences during the COVID-19 pandemic. 
Studies where the viewpoints of university lecturers, teachers, univer-
sity/school administrators or other stakeholders are examined should 
also be done so that we get a more detailed and realistic picture relat-
ed to the shifts and/or changes that occurred with regard to the use of 
crowdsourcing tools in before and after COVID-19 in foreign language 
teaching and learning.
References
Akat, M., & Karataş, K. (2020). Psychological effects of COVID-19 pandemic 
on society and its reflections on education. Electronic Turkish Studies, 
15(4), 1–13. doi: 10.7827/TurkishStudies.44336 
Albion, P. (2008). Web 2.0 in teacher education: two imperatives 
for action. Computers in the Schools, 25(3/4), 181–198. doi: 
10.1080/07380560802368173
Ali, Z. (2022). 21st-century learning: Understanding the language learn-
ing strategies with technology literacy among L2 learners. Journal 
of Nusantara Studies (JONUS), 7(2), 202–220. doi: 10.24200/jonus.
vol7iss2pp202-220
Arhar Holdt, Š., Zviel-Girshin, R., Gajek, E., Durán-Muñoz, I., Bago, P.; 
Fort, K.; Hatipoğlu, Ç., …, & Zanasi, L. (2020). Language Teach-
ers and Crowdsourcing: Insights from a Cross-European Survey. 
Rasprave, 46(1), 1–28. Retrieved from https://hrcak.srce.hr/index.
php?show=clanak&id_clanak_jezik=353213 
Bailey, D., & Lee, A. R. (2020). An Exploratory Study of Grammarly in the Lan-
guage Learning Context: An Analysis of Test-Based, Textbook-Based and 
Facebook Corpora. TESOL International Journal, 15(2), 4–27. Retrieved 
from https://www.tesol-international-journal.com/ 
Bakry, M. S., & Alsamadani, H. A. (2015). Improving the persuasive essay writ-
ing of students of Arabic as a foreign language (AFL): Effects of self-reg-
ulated strategy development. Procedia-Social and Behavioral Sciences, 
182, 89–97. doi: 10.1016/j.sbspro.2015.04.742 
171
Crowdsourcing and language learning habits and practices...
Balçıkanlı, C. (2010). The Effects of Social Networking on Pre-service English 
Teachers’ Metacognitive Awareness and Teaching Practice. Unpublished 
Ph.D. Dissertation. Gazi University.
Baranova, T., Kobicheva, A., & Tokareva, E. (2021). Total transition to online 
learning: students’ and teachers’ motivation and attitudes. In D. Bylieva, 
A. Nordmann, O. Shipunova & V. Volkova (Eds.), Knowledge in the Infor-
mation Society (pp. 301–310). Springer, Cham. https://link.springer.com/
content/pdf/10.1007/978-3-030-65857-1.pdf 
Barella, Y., & Linarsih, A. (2020). Extensive listening practice in EFL classroom 
with variety of news websites. Pedagogy: Journal of English Language 
Teaching, 8(1), 43–50. doi: 10.32332/pedagogy.v8i1.1961
Barrot, J. S. (2020). Integrating technology into ESL/EFL writ-
ing through Grammarly. RELC Journal, 0033688220966632. doi: 
10.1177/0033688220966632
Bartlett-Bragg, A. (2006). Reflections on pedagogy: Reframing practice to fos-
ter informal learning with social software. Retrieved from http://matchsz.
inf.elte.hu/tt/docs/Anne20Bartlett-Bragg.pdf 
Bax, S. (2003). CALL – past, present and future. System, 31, 13–28. doi: 
10.1016/S0346-251X(02)00071-4
Baytiheh, H. (2018). Online learning during post-earthquake school closures. 
Disaster Prevention and Management, 27(1), 215–227. doi: 10.1108/
DPM-07-2017-0173 
Bhusal, S., Niroula, A., & Kafle, R. (2020). Quarantine: A Period of Self-discov-
ery and Motivation as Medical Student. JNMA: Journal of the Nepal Medi-
cal Association, 58(227), 536–539. doi: 10.31729/jnma.5005
Bloom, K., & Johnston, K. M. (2010). Digging into YouTube videos: Using media 
literacy and participatory culture to promote cross-cultural understand-
ing. Journal of Media Literacy Education, 2(2), 113–123. doi: 10.23860/
jmle-2-2-3
Can Daşkın, N., & Hatipoğlu, Ç. (2019). A proverb learned is a proverb earned: 
Proverb instruction in EFL classrooms. Eurasian Journal of Applied Lin-
guistics, 5(1), 57–88. doi: 10.32601/ejal.543781
Chen, Z., & Luo, B. (2014). Quasi-Crowdsourcing Testing for Educational Pro-
jects. In Companion Proceedings of the 36th International Conference on 
Software Engineering (pp. 272–275). ACM Press. Retrieved from https://
dl.acm.org/doi/pdf/10.1145/2591062.2591153 
Chen, T., Peng, L., Jing, B., Wu, C., Yang, J., & Cong, G. (2020). The impact of the 
COVID-19 pandemic on user experience with online education platforms 
in China. Sustainability, 12(18), 7329, 1–31. doi: 10.3390/su12187329
172
Slovenščina 2.0, 2022 (2) | Articles
Chik, A., & Benson, P. (2020). Commentary: Digital language and learning 
in the time of cronavirus. Linguistics and Education, 62, 100873. doi: 
10.1016/j.linged.2019.100750
Clarkson, K. (2013). Usage of Social Network Sites amongst University Stu-
dents. Grin Verlag.
Council of Europe. (2001). Common European framework of reference for lan-
guages: Learning, teaching, assessment. Cambridge University.
Çebi, A. (2018). Teachers’ Perceptions Toward Technology Integration into the 
Language Teaching Practices. Journal of Narrative and Language Studies, 
6(11), 150–177.
Delibegović Džanić, N., Hatipoğlu, Ç., Milosevska, L., & Gajek, E. (in press). 
Has online learning changed the way we teach and study?: Student 
evaluation of teachers’ pedagogical skills during the first COVID-19 pe-
riod and potential change in their learning habits. Folia Linguistica et 
Litteraria.
DIKI. Słownik Angielsko-Polski, Słownik Angielski Online. Retrieved from 
www.diki.pl
Educase Learning Initiative. (2006). 7 Things You Should Know About You-
Tube. Retrieved from https://library.educause.edu/-/media/files/li-
brary/2006/9/eli7018-pdf.pdf 
Ersin, P., Atay, D., & Mede, E. (2020). Boosting preservice teachers’ compe-
tence and online teaching readiness through e-practicum during the 
COVID-19 outbreak. International Journal of TESOL Studies, 2(2), 112-
124. https://doi.org/10.46451/ijts.2020.09.09 
Estellés-Arolas, E., Navarro-Giner, R., & González-Ladrón-de-Guevara, F. 
(2015). Crowdsourcing Fundamentals: Definition and Typology. In F. J. 
Garrigos-Simon, I. Gil-Pechuán & S. Estelles-Miguel (Eds.), Advances in 
Crowdsourcing (pp. 33–48). Springer.
Farasat, A., Nikolaev, A., Miller, S., & Gopalsamy, R. (2017). Crowdlearning: 
Towards Collaborative Problem-Posing at Scale. In Proceedings of the 
Fourth ACM Conference on Learning@ Scale (pp. 221–224). ACM Press. 
doi: 10.1145/3051457.3053990
Gajek, E. (2020). Crowdsourcing in language learning as a continuation of 
CALL in varied technological, social, and ethical contexts. In K. M. Fred-
eriksen, S. Larsen, L. Bradley & S. Thouësny (Eds.), CALL for widening 
participation: Short papers from EUROCALL 2020 (pp. 75–80). Research-
publishing.net. Retrieved from https://research-publishing.net/publica-
tion/978-2-490057-81-8.pdf 
173
Crowdsourcing and language learning habits and practices...
Ghufron, M. A., & Rosyida, F. (2018). The role of Grammarly in assessing Eng-
lish as a Foreign Language (EFL) writing. Lingua Cultura, 12(4), 395–403. 
doi: 10.21512/lc.v12i4.4582 
Goel, D. (2017). Because learning should be Chimple: How storytellers and 
artists can help kids read and write. Retrieved from http://www.edexlive.
com/people/2017/oct/11/because-learningshould-be-chimple-how-
storytellers-and-artists-can-help-kids-read-and-write-1326)
Hampel, R., & Stickler, U. (2005). New skills for new classrooms: Training tu-
tors to teach languages online. Computer Assisted Language Learning, 
18(4), 311–326. doi: 10.1080/09588220500335455
Hatipoğlu, Ç., & Algi, S. (2018). Catch a tiger by the toe: Modal hedges in EFL 
argumentative paragraphs. Educational Sciences: Theory and Practice, 
18(4), 957–982. doi: 10.12738/estp.2018.4.0373 
Hatipoğlu, Ç., Gajek, E., Milosevska, L., & Delibegović Džanić, N. (2020). 
Crowdsourcing for Widening Participation and Learning Opportunities: A 
view from pre-service language teachers’ window. In K. M. Frederiksen, S. 
Larsen, L. Bradley & S. Thouësny (Eds.), CALL for widening participation: 
Short papers from EUROCALL 2020 (pp. 81–87). Retrieved from https://
research-publishing.net/publication/978-2-490057-81-8.pdf 
Hatipoğlu, Ç., Gajek, E., Milosevska, L., & Delibegović Džanić, N. (2021). Stu-
dent evaluation of teachers’ pedagogical skills during the first COVID-19 
period. In N. Zoghlami, C. Brudermann, C. Sarré, M. Grosbois, L. Bradley & 
S. Thouësny (Eds.), CALL and professionalisation: Short papers from EU-
ROCALL 2021 (pp. 119–125). Retrieved from https://research-publish-
ing.net/publication/978-2-490057-97-9.pdf
Hatipoğlu, Ç., Gajek, E., Delibegović Džanić, N., & Milosevska, L. (2022). Com-
parative analysis of students’ learning in the first and second semester of 
COVID-19 related online education in Türkiye, Poland, Republic of North 
Macedonia and Bosnia and Herzegovina. In B. Arnbjörnsdóttir, B. Bédi, 
L. Bradley, K. Friðriksdóttir, H. Garðarsdóttir, S. Thouësny & M. J. Whelp-
ton (Eds.), Intelligent CALL, granular systems and learner data: Short pa-
pers from EUROCALL 2022 (pp. 154–161). Retrieved from https://doi.
org/10.14705/rpnet.2022.61.1451
Hickling, S., Bhatti, A., Arena, G., Kite, J., Denny, J., Spencer, N. L., & Bowles, 
D. C. (2021). Adapting to teaching during a pandemic: Pedagogical ad-
justments for the next semester of teaching during COVID-19 and fu-
ture online learning. Pedagogy in Health Promotion, 7(2), 95–102. doi: 
10.1177/2373379920987264
174
Slovenščina 2.0, 2022 (2) | Articles
Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6), 1–4.
Howe, J. (2008). Crowdsourcing: How the power of the crowd is driving the 
future of business. Random House.
Hui, J. S., Gerber, E. M., & Dow, S. P. (2014). Crowd-based design activi-
ties: helping students connect with users online. In Proceedings of the 
2014 conference on Designing Interactive Systems (pp. 875–884). doi: 
10.1145/2598510.2598538
Hung, H. T., Yang, J. C., Hwang, G. J., Chu, H. C., & Wang, C. C. (2018). A scop-
ing review of research on digital game-based language learning. Comput-
ers & Education, 126, 89–104. doi: 10.1016/j.compedu.2018.07.001
Jiang, Y., Schlagwein, D., & Benatallah, B. (2018). A review of crowdsourcing 
for education: State of the art of literature and practice. PACIS 2018 Pro-
ceedings (p. 180). Retrieved from https://aisel.aisnet.org/pacis2018/180
Jin, L., & Deifell, E. (2013). Foreign language learners’ use and perception of 
online dictionaries: A survey study. Journal of Online Learning and Teach-
ing, 9(4), 515–533.
Kansal, A. K., Gautam, J., Chintalapudi, N., Jain, S., & Battineni, G. (2021). 
Google Trend Analysis and Paradigm Shift of Online Education Platforms 
during the COVID-19 Pandemic. Infectious Disease Reports, 13, 418–
428. doi: 10.3390/idr13020040
King, A. J., Gehl, R. W., Grossman, D., & Jensen, J. D. (2013). Skin self-ex-
aminations and visual identification of atypical nevi: Comparing individual 
and crowdsourcing approaches. Cancer Epidemiology, 37(6), 979–984. 
doi: 10.1016/j.canep.2013.09.004.
Köse, T., Çimen, E., & Mede, E. (2016). Perceptions of EFL learners about using 
an online tool for vocabulary learning in EFL classrooms: A Pilot Project in 
Turkey. Procedia-Social and Behavioural Sciences, 232(4), 362–372. doi: 
10.1016/j.sbspro.2016.10.051
Krajka, J. (2021). Teaching Grammar and Vocabulary in COVID-19 Times: Ap-
proaches Used in Online Teaching in Polish Schools during a Pandemic. 
JALT CALL Journal, 17(2), 112–134. doi: 10.29140/jaltcall.v17n2.379
Krishnan, I. A., Ching, H. S., Ramalingam, S., Maruthai, E., Kandasamy, P., De 
Mello, G., Munian, S., & Ling, W. W. (2020). Challenges of learning English 
in 21st century: Online vs. Traditional during covid-19. Malaysian Journal 
of Social Sciences and Humanities (MJSSH), 5(9), 1–15. doi: 10.47405/
mjssh.v5i9.494 
Laufer, B., & Waldman, T. (2011). Verb-noun collocations in second language 
writing: A corpus analysis of learners’ English. Language Learning, 61(2), 
647–672. doi: 10.1111/j.1467-9922.2010.00621.x
175
Crowdsourcing and language learning habits and practices...
Li, L., & Xu, H. (2015). Using an Online Dictionary for Identifying the Mean-
ings of Verb Phrases by Chinese EFL Learners. Lexikos, 25, 191–209. doi: 
10.5788/25-1-1295
Lyding, V., Nicolas, L., Bédi, B., & Fort, K. (2018). Introducing the European 
network for combining language learning and crowdsourcing techniques 
(enetcollect). In P. Taalas, J. Jalkanen, L. Bradley & S. Thouesny (Eds.), Fu-
ture-proof CALL: language learning as exploration and encounters (Short 
papers from EUROCALL 2018) (pp. 176–181). Research-Publishing.net.
McNeil, L. (2020). Implementing digital game-enhanced pedagogy: Support-
ive and impeding language awareness and discourse participation phe-
nomena. ReCALL, 32(1), 106–124. doi: 10.1017/S095834401900017X
Mendez, J. P., Curry, J., Mwavita, M., Kennedy, K., Weinland, K., & Bainbridge, 
K. (2009). To friend or not to friend: Academic interaction on Facebook. 
International Journal of Instructional Technology & Distance Learning, 
6(9), 33–47. 
Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis: An Expand-
ed Sourcebook. SAGE Publications. 
Miloshevska, L., Gajek, E., Delibegović Džanić, N., & Hatipoğlu, Ç. (2020). 
Emergency online learning during the first Covid-19 period: Students’ 
perspectives from Bosnia and Herzegovina, North Macedonia, Poland and 
Turkey. Explorations in English Language and Linguistics (ExELL), 8(2), 
101–143. doi: 10.2478/exell-2021-0002
Miloshevska, L., Delibegović Džanić N., Hatipoğlu Ç., & Gajek E. (2021). Crowd-
sourcing for language learning in Turkey, Bosnia and Herzegovina, Repub-
lic of North Macedonia and Poland. Journal of Narrative and Language 
Studies, 9(16), 106–121. Retrieved from https://nalans.com/index.php/
nalans/article/view/391
Mospan, N. (2018). Mobile teaching and learning English–A multinational per-
spective. Teaching English with Technology, 18(3), 105–125. http://ce-
jsh.icm.edu.pl/cejsh/element/bwmeta1.element.desklight-263522eb-
dbc5-4ee6-b561-519e55b8ca57 
Nadhifah, U. N., & Puspitasari, D. (2021). Learning English Through Duolingo: 
Narrating Students’ Experience During Covid-19 Pandemic Time. Ethical 
Lingua: Journal of Language Teaching and Literature, 8(1), 302–310. doi: 
10.30605/25409190.280
Odo, D. M. (2016). Crowdsourced Language Learning: Lessons for TESOL from 
Online Language-Learning Enthusiasts. English Teaching Forum, 54(4). 
14–23. Retrieved from https://files.eric.ed.gov/fulltext/EJ1123197.pdf.
176
Slovenščina 2.0, 2022 (2) | Articles
Özer, M. (2020). Educational policy actions by the Ministry of National Educa-
tion in the times of COVID-19 pandemic in Turkey. Kastamonu Eğitim Der-
gisi [Kastamonu Education Journal], 28(3), 1124–1129. doi: 10.24106/
kefdergi.722280
Pellegrino, J. W., & Hilton, M. L. (Eds). (2012). Education for Life and Work: 
Developing Transferable Knowledge and Skills in the Twenty-First Century. 
National Academies Press.
Rafiee, M., & Abbasian-Naghneh, S. (2019). E-learning: development 
of a model to assess the acceptance and readiness of technology 
among language learners. Computer Assisted Language Learning. doi: 
10.1080/09588221.2019.1640255
Reimers, F., Schleicher, A., Saavedra, J., & Tuominen, S. (2020). Supporting 
the continuation of teaching and learning during the COVID-19 Pandem-
ic. OECD, 1(1), 1–38. Retrieved from https://globaled.gse.harvard.edu/
files/geii/files/supporting_the_continuation_of_teaching.pdf 
Rodosthenous, C., Lyding, V., König, A., Horbacauskiene, J., Katinskaia, A., Ul 
Hassan, U., Isaak, N., Sangati, F., & Nicolas, L. (2019). Designing a pro-
totype architecture for crowdsourcing language resources. Retrieved 
from https://helda.helsinki.fi/bitstream/handle/10138/313258/paper4.
pdf?sequence=1
Rundell, M. (2014). Macmillan English Dictionary: The End Of Print? Slovenščina 
2.0, 2(2), 1–14. Retrieved from http://www.trojina.org/slovenscina2.0/
arhiv/2014/2/Slo2.0_2014_2_02.pdf 
See, L., Schepaschenko, D., Lesiv, M., McCallum, I., Fritz, S., & Comber, A. 
(2014). Building a hybrid land cover map with crowdsourcing and geo-
graphically weighted regression. ISPRS Journal of Photogrammetry and 
Remote Sensing. doi: 10.1016/j.isprsjprs.2014.06.016.
Setyowati, L., Sukmawan, S., & El-Sulukkiyah, A. A. (2021). Learning from 
home during pandemic: A blended learning for reading to write activity in 
EFL setting. JEES (Journal of English Educators Society), 6(1), 9–17. doi: 
10.21070/jees.v6i1.662
Shaikh, U. U., Karim, S., & Asif, Z. (2017). Re-Thinking Vygotsky: Apply-
ing Social Constructivism to Asynchronous Online Courses utilising the 
Power of Crowdsourcing. In Proceedings of the 21st Pacific Asia Confer-
ence on Information Systems (p. 233). Langkawi. http://aisel.aisnet.org/
pacis2017/233
Sokolik, M. (2003). Writing. In D. Nunan (Ed.), Practical English language 
teaching (PELT) (pp. 87–88). New York: McGraw Hill.
177
Crowdsourcing and language learning habits and practices...
Solemon, B., Ariffin, I., Din, M. M., & Anwar, R. M. (2013). A review of the uses 
of crowdsourcing in higher education. International Journal of Asian So-
cial Science, 3(9), 2066-2073. Retrieved from https://archive.aessweb.
com/index.php/5007/article/view/2564
Soleymani, M., & Larson, M. (2013). Crowdsourcing for multimedia research (pp. 
1111–1112). ACM Press, New York. doi: 10.1145/2502081.2502234.
Thorne, S. L., & Reinhardt, J. (2008). “Bridging activities”, new media litera-
cies, and advanced foreign language proficiency. CALICO Journal, 25(3), 
558–572. doi: 10.1558/cj.v25i3.558-572
Toy, F. (2019). The Effects of Quizlet on Students’ and EFL Teachers’ Percep-
tions on Vocabulary Learning/Teaching Process. MA Thesis, Süleyman 
Demirel University, Turkey. 
Trinh, T. L. A., Tran, T. K. N., Vo, T. B. N., & Huynh, T. T. S. (2021). The difference 
effects of paper dictionaries vs. online dictionaries. AsiaCALL Online Jour-
nal, 12(3), 28–38. Retrieved from https://asiacall.info/acoj/index.php/
journal/article/view/34
Trung, T., Hoang, A. D., Nguyen, T. T., Dinh, V. H., Nguyen, Y. C., & Pham, H. 
H. (2020). Dataset of Vietnamese student’s learning habits during COV-
ID-19. Data in Brief, 30, 105682, 1–7. doi: 10.1016/j.dib.2020.105682
Tsai, Shu-Chiao. (2019). Using google translate in EFL drafts: a preliminary 
investigation, Computer Assisted Language Learning, 32(5/6), 510–526. 
doi: 10.1080/09588221.2018.1527361
Tureng Online Dictionary. Retrieved from https://tureng.com/tr/turkce-ingilizce
Uluçay, Ç., & Hatipoğlu, Ç. (2017). Cause markers in Turkish cause paragraphs. 
In Çiler Hatipoğlu, Erdem Akbaş & Yasemin Bayyurt (Eds.), Metadiscourse 
across Genres: Uncovering Textual and Interactional Aspects of Texts (pp. 
223–249). Frankfurt: Peter Lang.
Waicekawsky, L., Laurenti, L., & Yuvero, F. (2020). Teaching ESP online dur-
ing the COVID-19 pandemic: An account of Argentinian students on this 
teaching modality. In SHS Web of Conferences, (Vol. 88, p. 02002). EDP 
Sciences. doi: 10.1051/shsconf/20208802002
Wang, L. (2016). Employing Wikibook Project in a Linguistics Course to Pro-
mote Peer Teaching and Learning. Education and Information Technolo-
gies, 21(2), 453–470. doi: 10.1007/s10639-014-9332-x
Weld, D. S., Adar, E., Chilton, L. B., Hoffmann, R., Horvitz, E., Koch, M., ..., & 
Mausam, M. (2012, July). Personalised Online Education-A Crowd-
sourcing Challenge. In HCOMP@ AAAI. Retrieved from https://cond.org/
hcomp12.pdf
178
Slovenščina 2.0, 2022 (2) | Articles
Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. 
Cambridge University Press.
Xu, H., Wu, Y., & Hamari, J. (2022). What determines the successfulness of a 
crowdsourcing campaign: A study on the relationships between indica-
tors of trustworthiness, popularity, and success. Journal of Business Re-
search, 139, 484–495. doi: 10.1016/j.jbusres.2021.09.032
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2001). Self-regulated learning and 
academic achievement: Theoretical perspectives. Routledge.
Množičenje ter navade in prakse učenja jezikov v Turčiji, Bosni 
in Hercegovini, Republiki Severni Makedoniji in na Poljskem v 
predpandemskem in pandemskem obdobju
Priljubljenost spletnih množičenjskih platform za poučevanje in učenje jezikov 
je pred pandemijo COVID-19 počasi naraščala. Študije, izvedene v Turčiji, Bo-
sni in Hercegovini, Republiki Severni Makedoniji in na Poljskem, so pokazale, 
da so jih učitelji uporabljali tako kot orodje pri pouku in izven njega. Po drugi 
strani pa so jih učenci uporabljali kot pomoč pri izpopolnitvi svojih spretnosti 
in znanja ciljnih jezikov ter da bi postali bolj avtonomni. Izobraževalni sistemi 
po vsem svetu ter ustaljene prakse poučevanja in učenja pa so se vendar spre-
menili s pandemijo covid-19. Ta raziskava si prizadeva odkriti, ali so se med 
pandemijo COVID-19 spremenila stališča, konteksti uporabe, frekventnost in 
navade učencev jezikov v Turčiji, Bosni in Hercegovini, Republiki Severni Ma-
kedoniji in na Poljskem, in če »DA«, kako.
Da bi primerjali uporabo orodij za množičenje pred in med pandemijo co-
vid-19 pri učencih jezika v omenjenih štirih državah, smo ponovno uporabili 
medkulturno ustrezen vprašalnik, ki smo ga pred tem že uporabili v obdobju 
pred pandemijo. Zbrane podatke smo kvalitativno in kvantitativno preučili, da 
bi odkrili tudi najmanjša odstopanja. Postavili smo hipotezo, da so platforme 
za množičenje postale bolj razširjene med pandemijo zaradi znatnih spre-
memb, povezanih s poučevanjem in učenjem jezikov. Hipoteza je temeljila na 
ugotovitvah raziskave, ki je pokazala, da so bili učitelji v Turčiji, Bosni in Her-
cegovini, Republiki Severni Makedoniji in na Poljskem, podobno kot njihovi ko-
legi po svetu, prisiljeni uporabljati skoraj vsa digitalna orodja, ki so jih imeli na 
voljo, zlasti v kriznem obdobju selitve poučevanja na splet pomladi leta 2020. 
Obenem so bili učenci jezikov prisiljeni samostojno uporabljati številna orodja 
in platforme množičenja, da bi sledili zahtevam izobraževalnih ustanov.
Rezultati so pokazali, da je prehod z učenja v živo na spletno učenje zaradi 
covida-19 pomembno vplival na razvoj platform za množičenje po vsem svetu 
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in na uporabo virov za množičenje v državah, vključenih v raziskavo. Opaziti je 
bilo, da se ni povečalo le število uporabljenih virov, temveč so se spremenile 
tudi funkcije uporabljenih platform (tj. od bolj splošnih k bolj »prilagojenim 
potrebam in državam»). Rezultati so tudi pokazali, da je preplet dejavnikov, 
kot so sprememba načina poučevanja, manjša interakcija z učitelji in vrstniki, 
večja delovna obremenitev in pomanjkanje stalne podpore s strani izobraže-
valnih ustanov, privedel do tega, da so učenci sami prevzeli odgovornost za 
svoje učenje. Spoznali so, da so edini, ki vedo, kaj potrebujejo, in da so edini, 
ki si lahko pomagajo, zato so začeli iskati in uporabljati platforme, ki so najbolj 
ustrezale njihovim zahtevam. Ker so bila pričakovanja in potrebe učencev v 
preučevanih državah različna, so se število, pogostost in lastnosti priljubljenih 
platform od države do države spreminjali.
Upamo, da bodo izsledki raziskave služili kot morebitne smernice za 
učence in učitelje jezikov, ki nameravajo v svoje dejavnosti v razredu in zunaj 
njega vključiti dejavnosti množičenja. Rezultati bi obenem lahko predstavlja-
li pomembne povratne informacije za ustvarjalce platform, ki si prizadevajo 
oblikovati vire, ki so medkulturno ustrezni, hkrati pa izpolnjujejo bolj posebne 
zahteve učencev jezikov v specifičnih okoliščinah. Menimo, da lahko vključi-
tev množičenja v jezikovne učne načrte učencem omogoči več priložnosti za 
sodelovanje v razredu in zunaj njega ter učinkovitejše učenje jezika, kar bo 
posledično privedlo do razvoja bolj samostojnih, aktivnih in samozavestnih 
učencev jezika.
Ključne besede: množičenje, učenje jezikov, COVID-19, obdobje pred pande-
mijo, obdobje po pandemiji
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Appendix A: Period 1: Crowdsourcing sites/tools used to learn 
foreign languages 
Crowdsourcing tools
TUR B&H RNM POL ALL
% n % n % n % n % n
1 Wikipedia 37 86 40 58.0 29 70.7 52 89.7 158 74.9
2 Kahoot 36 83.7 31 44.9 12 29.3 54 93.1 133 63.0
3 Duolingo 23 53.5 40 58.0 20 48.8 47 81.0 130 61.6
4 Khan Academy 9 20.9 8 11.6 23 56.1 9 15.5 49 23.2
5 Memrise 10 23.3 7 10.1 3 7.3 23 39.7 43 20.4
6 Busuu 9 20.9 3 4.3 2 4.9 7 12.1 21 10.0
7 Quizlet 19 32.8 19 9.0
8 Storybird 4 9.3 8 11.6 2 4.9 14 6.6
9 Writeandimprove.com 1 2.3 5 7.2 2 4.9 8 3.8
10 Anki 6 10.3 6 2.8
11 Speakandimprove.com 1 2.3 1 0.5
12 Grammarly 1 2.3 1 0.5
13 Movies and books 1 2.3 1 0.5
14 Rosetta Stone 1 2.3 1 0.5
15 Voscreen 1 2.3 1 0.5
16 Insta.ling 1 1.7 1 0.5
17 Wordreference 1 1.7 1 0.5
18 Fiszkoteka 1 1.7 1 0.5
19 Lingo Hut 1 1.7 1 0.5
20 Kanji Study 1 1.7 1 0.5
21 Tandem language app 1 1.7 1 0.5
22 Flocabulary 1 1.4 1 0.5
23 Drops 1 1.4 1 0.5
24 English Club TV 1 1.4 1 0.5
25 Google translate 1 1.4 1 0.5
26 YouTube 1 1.4 1 0.5
27 None of them 0 0 10 14.5 4 9.8 0 0.0 14 6.6
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Appendix B: Period 2: Crowdsourcing sites/tools used to learn 
foreign languages in alphabetical order
Crowdsourcing tools
TUR B&H RNM POL ALL
n % n % n % n % n %
1 activelylearn.com 1 1.7     1 0.5
2 Anki     2 6.7 2 0.9
3 BAB.LA       1 2.0     1 0.5
4 Babble 1 1.7           1 0.5
5 BBC Learning English 1 1.7   1 2.0 1 3.3 3 1.4
6 blogs   1 2.0     1 0.5
7 Books   4 8.2     4 1.9
8 Bottled 1 1.7           1 0.5
9 Busuu 2 3.3           2 0.9
10 Cambridge (Online) Dictio-nary 13 21.7 2 4.3         15 7.1
11 Conjugato             1 3.3 1 0.5
12 Coursera         1 2.0     1 0.5
13 diki             6 20.0 6 2.8
14 Discord             1 3.3 1 0.5
15 Duolingo 26 43.3 16 34.8 19 38.8 14 46.7 75 35.5
16 DW Deutsch lernen 2 3.3             2 0.9
17 Easy Languages 1 1.7             1 0.5
18 EDX 1 1.7             1 0.5
19 Eng Vid     1 2.2         1 0.5
20 English idioms and phrases     1 2.2         1 0.5
21 eTutor             1 3.3 1 0.5
22 Fandom 2 3.3             2 0.9
23 Fiszkoteka             1 3.3 1 0.5
24 Flocabulary         1 2.0     1 0.5
25 Forums 1 1.7             1 0.5
26 francaisfacile 1 1.7             1 0.5
27 FunEasyLearn     1 2.2         1 0.5
28 Gamepedia 1 1.7 1 2.2         2 0.9
29 Glosbe     5 10.9     1 3.3 6 2.8
30 Google translate 8 13.3 16 34.8 16 32.7     40 19.0
31 Grammarly 5 8.3     3 6.1 1 3.3 9 4.3
32 Hello talk 1 1.7             1 0.5
182
Slovenščina 2.0, 2022 (2) | Articles
Crowdsourcing tools
TUR B&H RNM POL ALL
n % n % n % n % n %
33 isl collective.com 1 1.7             1 0.5
34 Kahoot 15 25.0 8 17.4 3 6.1 6 20.0 32 15.2
35 Khan Academy 4 6.7     1 2.0     5 2.4
36 Learn Spanish 1 1.7             1 0.5
37 Lingodeer 1 1.7             1 0.5
38 Lingua 1 1.7             1 0.5
39 Linguee             1 3.3 1 0.5
40 Lingvist         1 2.0     1 0.5
41 Longman Dictionary 1 1.7 1 2.2         2 0.9
42 Memrise 1 1.7     2 4.1     3 1.4
43 Mentimeter 1 1.7             1 0.5
44 Minecraft     1 2.2 1 2.0     2 0.9
45 mondly             1 3.3 1 0.5
46 Movies         7 14.3     7 3.3
47 Nearpod 1 1.7             1 0.5
48
Netflix (e.g., series like  
History 101, Explained,  
movies, etc.)
7 11.7     2 4.1     9 4.3
49 News websites 1 1.7             1 0.5
50 None of them 1 1.7 3 6.5         4 1.9
51 One Look Thesaurus (on-line) 2 3.3             2 0.9
52 Online dictionaries 4 6.7 12 26.1     9 30.0 25 11.8
53 Online games 1 1.7 5 10.9 11 22.4 2 6.7 19 9.0
54 Oxford Online Dictionary 3 5.0 4 8.7         7 3.3
55 Ozdic 2 3.3             2 0.9
56 Plotagon 1 1.7             1 0.5
57 Podcasts 2 3.3             2 0.9
58 Pons             2 6.7 2 0.9
59 Quillbot (paraphrasing tool)         1 2.0     1 0.5
60 Quizizz 1 1.7             1 0.5
61 Quizlet 3 5.0     2 4.1 21 70.0 26 12.3
62 Reading power 1 1.7             1 0.5
63 Reddit.com 1 1.7 1 2.2         2 0.9
64 Relatedwords.org 1 1.7             1 0.5
65 ReversoContext             6 20.0 6 2.8
183
Crowdsourcing and language learning habits and practices...
Crowdsourcing tools
TUR B&H RNM POL ALL
n % n % n % n % n %
66 Rosetta Stone 1 1.7     1 2.0     2 0.9
67 Scrabble     3 6.5         3 1.4
68 Slowly (Twitter app) 1 1.7             1 0.5
69 SpanishDict         1 2.0     1 0.5
70 Spike Notes 1 1.7             1 0.5
71 Steam language games 1 1.7             1 0.5
72 Teamspeak             1 3.3 1 0.5
73 Ted Talks 1 1.7 1 2.2         2 0.9
74 Test English 1 1.7 1 0.5
75 TheFreeDictionary 1 1.7 1 0.5
76 Tureng (Online Dictionary) 7 11.7 7 3.3
77 Turnitin (Feedback) 1 1.7 1 0.5
78 TV5monde 2 3.3 2 0.9
79 Twitch.tv     1 2.0 1 0.5
80 Udemy 2 3.3 2 0.9
81 Uncharted 1 1.7 1 0.5
82 Urban Dictionary     1 2.0 1 0.5
83 Video games 2 3.3 2 4.1 4 1.9
84 VOA (Voice of America) 1 1.7     1 0.5
85 Vocaroo     1 3.3 1 0.5
86 Websites 5 10.9         5 2.4
87 Wikipedia 5 8.3 10 21.7 15 30.6 2 6.7 32 15.2
88 Word reference 1 3.3 1 0.5
89 Word search 1 2.2 1 0.5
90 Wordwall 2 3.3 2 0.9
91 Younglish 1 1.7 1 0.5
92 YouTube 14 23.3 9 19.6 6 12.2 2 6.7 31 14.7
93 Zlibrary 1 2.2 1 0.5
ALL 171 108 105 84 468