=Paper=
{{Paper
|id=Vol-2390/PaperA1
|storemode=property
|title=In Search of the State of Language Learning Online in Europe
|pdfUrl=https://ceur-ws.org/Vol-2390/PaperA1.pdf
|volume=Vol-2390
|authors=Michal Bodorik,Branislav Bédi
}}
==In Search of the State of Language Learning Online in Europe==
https://ceur-ws.org/Vol-2390/PaperA1.pdf
In Search of the State of Language Learning Online in Europe
Michal Bodorík1, Branislav Bédi2
Trnava University1, University of Iceland2
Priemyselná 4, Trnava, Slovakia1
Sæmundargötu 2, Reykjavík, Iceland2
michal.bodorik@truni.sk1, branislav@hi.is2
Abstract
Language education currently benefits greatly from the Internet and other new technologies in that it allows instant sharing of materials
and ideas. Both the learners and the teachers gain instant access to a variety of options suitable to their level of L2 practice. The focus
here is to describe the initial stage of a larger research plan that aims at examining the state of freely accessible websites for language
learning in thirty-three languages used in Europe and that from a user (learner) point of view. In this first stage, Content Analysis was
used in order to find out in which categories the language learning websites have commonality. It is important to come up with a valid
set of categories in order to carry out a further in-depth analysis planned for the second stage of the research. The initial results advocate
thirteen different categories and indicate that crowdsourcing is not involved in any of them. This suggests that this method of sharing
and creating language learning content and other information seems to be popular throughout commercial online courses, such as Babel,
Busuu, and Duolingo. The free online language learning courses usually do not include such a feature.
Keywords: Content Analysis, Crowdsourcing, Language Learning Online
1. Introduction They analyse the use of various recent applications and
One of the currently applied trends in L2 education is the practices in blended learning amongst educators but do not
implementation of technology from various perspectives of focus on crowdsourcing. As a result, and without further
use. The application of computers and smart mobile conclusion based on their analysis, they only recommend
devices has played a key role for the last couple of decades. using such tools in order to motivate others to incorporate
In this context, the computer assisted language-learning such techniques and technology in their L2 practices. More
(CALL) approach has brought about many positives, such recently, Kukushka-Hulme and Viberg (2017) similarly
as creating a stress-free learning environment; supporting focused on MALL in the context of collaborative language
the development of various learning strategies, e.g. learning. In their qualitative review of online publications
individual learning; enabling the learning outside of the of a five-year span between 2012 -2016, they conclude that
traditional classroom; providing instant feedback; such technologies may help learners to become exposed to
monitoring learners’ progress; and promoting exploratory shared materials about authentic local discourse and that
and global learning (Coghlan 2014; Dina and Ciornei 2013; done in a global collaborative manner online. The authors
Egbert et al. 2002; Pokrivčáková et al. 2014). Similarly the use the term collaborative work rather than crowdsourcing,
use of current tools and technologies in L2 education can even though some features described refer rather to the
have similarly a very positive impact on both, the learners latter term. Social context is very important in L2 learning,
and the teachers. The Internet has also become relevant in especially with the reference to the exposure and use of the
the context of CALL as more and more technical tools target language. The use of technology and related tools in
support the online mode as opposed to the offline method. language learning does not only depend on their
Moreover, access to social networks that offer a wide availability and accessibility but also on the digital skills of
variety of free L2 resources that are being crowdsourced, both teachers and learners. Collaborative work within
e.g. YouTube and Wikipedia, has become more relevant specific classroom tasks, or crowdsourcing activities
today than ever before. The reason for that may be the ease incorporated within available online tools or learner
of use, a huge amount of available data shared by others, activities in MALL can be beneficial, however, according
and instant availability. Following Jiang’s et al. (2018) to Kukushka-Hulme and Viberg (2017), there are still gaps
definition, today with the use of technology and Internet, in how mobile learning should be designed. The above-
Crowdsourcing for Education (CfE) represents a type of related research represents only a short review of relevant
online activity in which an educator, or an educational studies; other studies related to websites or platforms could
organization, proposes to help with learning or teaching of not be found at present.
a group of individuals via a flexible open call.
The present paper focuses on an existing content analysis 2. Methodology
of existing language learning resources found online. The The first step of this research refers to defining the language
analysis is conducted from a user (learner) point of view learning resources online. The term L2 websites has been
and includes various websites or platforms in the teaching preferred to platforms for reasons presented in the results
of thirty-three languages from thirty-eight countries in section. When browsing the Internet, the initial search was
Europe that are currently being represented in the COST based on L2 websites that offer teaching of those languages
Action enetCollect (Lyding et al., 2018). Similar research belonging to the member states of the Action. All research
has not yet been conducted. The most recent research including typing keywords into the online browser Google
similar to this was conducted by Bárcena et al. (2015) and Chrome was conducted in the English language, as it was a
concerns the use of mobile language learning (MALL) in practical way of putting ourselves in the shoes of a general
formal and non-formal education. L2 learner searching for websites to learn various European
languages.
EnetCollect WG3 & WG5 Meeting, 24-25 October 2018, Leiden, Netherlands 10
It also allowed us to give more or less an equal chance to layout/design; 6) skills; 7) focus; 8) task typology; 9)
all websites to appear in the search. A link and the name of language of instruction; 10) levels; 11) access; 12)
website was copied and pasted into an Excel sheet. As an crowdsourcing element, and 13) additional information.
example for the Dutch language, these keywords were These categories have been found based on the research of
used: ‘learn Dutch online’ or ‘learning Dutch online’ or the collected data.
‘Dutch language online’ and those results that included The first category labelled as ‘country’ refers to each
relevant links were collected. The entire procedure took country that is a member of the Action. Included are:
place in the form of a desktop research carried out between Austria, Belgium, Bosnia and Herzegovina, Bulgaria,
17 and 31 August 2018. Croatia, Cyprus, Czech Republic, Denmark, Estonia,
Content Analysis was chosen as the research tool suitable Finland, France, FYR Macedonia, Germany, Greece,
for analysing online websites with free L2 content. This Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania,
method offers many benefits for the investigator but the Luxembourg, Malta, Montenegro, Netherlands, Poland,
most relevant feature is the competence to reduce written Portugal, Romania, Serbia, Slovakia, Slovenia, Spain,
data (Cohen et al., 2011). This means that any surveyed Sweden, Switzerland, Turkey and the United Kingdom.
material/content is reduced so that it is manageable in The second category labelled as ‘language’ represents the
smaller portions. Similarly, Krippendorff (2004, p. 42) official language(s) spoken in each country. Seven of the
states: “The ability to process large volumes of text in surveyed countries have more than one official language
content analysis is paid for by the explicitness of the (Belgium, Cyprus, Finland, Ireland, Luxembourg, Malta
method’s procedures, which, if clearly stated, can be and Switzerland) and therefore these languages were
applied repeatedly, by many coders or by computer ascribed to this country but the online websites were
software.” The method of Content Analysis lessens the data analyzed only once per language. The third category refers
by classification of words and texts into much fewer to those responsible for the websites and their content,
categories. When carrying out a qualitative data analysis, whether it is a private person, an organization or an
as is the case in this research, it is important to specify theeducational facility. Some of these providers at the time of
coding based on the response of the collected data. This the survey allowed completely free entrance into the tasks,
feature is defined by Cohen et al. (2011) as “the ascription some offered the first few lessons or classes to work with
of a category label to a piece of data, that is either decided for free but later required the user to register or buy a
in advance or in response to the data that have been membership. Partial results have already shown that the
collected” (p. 559). For this reason no other pre-defined majority of these were run by educational organisations and
labelling system from other research was used because we university projects. In four cases (Bosnian, Finish, German,
aimed at originality of this study which should offer Hungarian language) the learning resources were designed
practical categories for labelling and coding. by a private person. For Latvian, Lithuanian and Maltese
languages no proper online sites were found. The fourth
3. Preliminary Results category was designed to save the links of individual
websites and so to prove the evidence of the analyzed
The process revealed that there are several types of online
language learning resources that can be categorised as content. Some countries/languages had more websites that
provided various learning tasks and access to online
follows: a) online L2 learning platforms that offer multiple
content for both language learners and teachers. Still others
languages; b) online websites that usually focus on one
language; c) YouTube courses; d) various mobile had no free websites for educational purposes or had paid
private offers. The fifth category marked as the ‘layout’ or
applications, e) software; f) games; g) private online
‘design’ was purposed to look at the structure of the online
lessons; h) Skype L2 instructions (usually paid), or other
tools similar to Skype; and i) online dictionaries with or platforms. In particular it was to examine how the language
content was divided, i.e. whether it was introduced in
without L2 explanations. Based on the initial results of
categories, chapters, courses, levels, lessons, modules,
various categories, it was clear that due to time and work
constraints, only one particular category could have been sections or smaller units. The analysis has revealed that the
most common format of task structuring were lessons and
analysed in depth, i.e. L2 websites. Despite the fact that
sections. The sixth category was based on which of the L2
some online L2 platforms, e.g. Babbel, Busuu, and
Duolingo, offered teaching more than one language, i.e. skills: speaking, writing, listening, reading, was chosen by
the website for the most practice. Nearly all websites in this
they had uniformity in the types of tasks and the learner
analysis focused on the practice of listening, reading and
could choose from variety of languages, these were not
included in our analysis because they had a freemium writing. Only two cases incorporated the practise of
speaking skills. The seventh category labelled as ‘focus’
access which allowed initial free use of some lessons or
was oriented toward the pedagogical aim of language
tasks but further on a premium was requested for use. The
research thus proceeded with the analysis of L2 websites. activities, such as grammar, translation, vocabulary,
pronunciation, culture. Here, it has been spotted that the
Such websites offer a large number of tasks and additional
leading position represents the practice of vocabulary,
activities to enhance the learners’ language skills. It was
therefore necessary to categorise these tasks according to followed by grammar and pronunciation. The eighth
category represents the typology of tasks, such as fill in, fill
certain criteria. As mentioned above, when analysing
in the blanks, match, listen and repeat, read, read aloud,
content, the features, or labels, for each category need to be
established from the analysis itself. Consequently, further repeat, select, speak aloud, translate, word order, write
what you hear and so forth. From the data it is evident that
categories were added when the content of each website
the most frequently applied task was the filling of gaps.
was thoroughly analysed. As a result, the following thirteen
categories of generic CALL resources were created: 1) The ninth category was labelled as the language of
instruction. English was the main language of use. Some
country; 2) official language(s); 3) provider; 4) link; 5)
sites also offered the target language as the tool for
EnetCollect WG3 & WG5 Meeting, 24-25 October 2018, Leiden, Netherlands 11
instruction as well as for practicing the exercises. In sixteen 6. Bibliographical References
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5. Acknowledgements
This article is based upon work from COST Action
enetCollect (CA 16105European Network for Combining
Language Learning with Crowdsourcing Techniques)
supported by COST (European Cooperation in Science and
Technology).
EnetCollect WG3 & WG5 Meeting, 24-25 October 2018, Leiden, Netherlands 12