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 cases the online language websites offered more than one Bárcena, E. et al. (2015).State of the art of language language of instruction. The tenth category created within learning design using mobiletechnology: sample apps the content analysis was marked as level in order to find out and some critical reflection. In F. Helm, L. Bradley, M. whether it was for beginners or advanced learners. Some of Guarda, &S. Thouësny (Eds),Critical CALL – these sites provided classification according to the Proceedings of the 2015 EUROCALL Conference, Common European Framework of Reference for Padova, Italy, pages 36-43. Dublin: Languages: Learning, Teaching, Assessment, specifically Researchpublishing.net. Available at: levels detected usually referred to A1, A2, B1. There were http://dx.doi.org/10.14705/rpnet.2015.000307. also cases that offered difficulty of tasks by levels, such as Coghlan, N. (2014). Benefits of Computer Aided Language beginner, elementary, intermediate, and advanced. The Learning. ESL – lounge blog. Available at : eleventh category was labelled as ‘access’, which helped to http://www.esl-lounge.com/blog/174/benefits-of- categorise free access to websites. Over 83% of all included computer-aided-language-learning. learning resources were of free access. The rest of them had Cohen, L., Manion, L. and Morrison, K. (2011). Research some minor requirements for use. The twelfth category was Methods in Education. Routledge, New York, 7th edition. added based on the aim of this research, i.e. to find out Dina, A.and Ciornei, S. (2013). The Advantages and whether some of the websites use a crowdsourcing element. Disadvantages of Computer Assisted Language Learning In regard to this the survey has demonstrated that the and Teaching for Foreign Languages. Procedia - Social crowdsourcing features were not enabled within the content and Behavioral Sciences 76, pages 248 – 252, doi: of inspected language websites from the user’s point of 10.1016/j.sbspro.2013.04.107. view. The last, thirteenth, category refers to the analysis of Egbert, J., Paulus, T. M.and Nakamichi, Y.(2002).The the content and is labelled as ‘additional information’. Impact Of CALL Instruction on Classroom Computer Here, the researcher could add remarks regarding Use: A Foundation for Rethinking Technology in additional material or bonuses offered by websites. These Teacher Education. Language Learning & Technology categories were recorded in an Excel spreadsheet for 6(3), pages 108-126. further analysis. The preliminary results are presented in Kippendorff, K. (2004). Content Analysis An Introduction the next chapter. to Its Methodology. SAGE Publications, California, 2nd edition. 4. Concluding Remarks and Future Work Kukuska-Hulme, A. and Viberg, O. (2017). Mobile The present paper has discussed an ongoing effort to find collaborative language learning : State of the art. British the state of language learning websites in thirty-eight Journal of Education Technology 49(2), pages 207-2018. countries in Europe. This research forms the initial part of Jiang, Y., Schlagwein, D. and Benatallah, B. (2018). A a larger work in progress, which aims at two objectives, 1) Review on Crowdsourcing for Education: State of the to analyse the free L2 European websites to detect common Art of Literature and Practice.Proceedings of Twenty- categories and whether they include some form of Second Pacific-Asia Conference on Information Systems crowdsourcing, and 2) an in-depth analysis of each (PACIS 2018), pages180-194, Japan category. This article concludes the first objective. Lyding, V., Nicolas, L., Bédi, B. and Fort, K. Crowdsourcing has not been found in any of the analysed (2018).Introducing the European NETwork for free online resources. This suggests that there is a COmbining Language LEarning and Crowdsourcing difference between free and freemium (commercial) online Techniques (enetCollect). In P. Taalas, J. Jalkanen, L. resources, i.e. paid or partially paid L2 platforms are likely Bradley, & S. Thouësny (Eds), Future-proof CALL : to include crowdsourcing. Further work suggests a second language learning as exploration and encounters – short coder, i.e. another researcher conducting the same research papers from EUROCALL 2018 (pp. 176-181). Research- and comparing results in order to double-check the publishing.net. classification of categories, and possibly expand the list and Pokrivčáková, S.,Babocká, M., Bereczky, K., Bodorík, M., to carry on a deeper analysis of each category. After Bozdoğan, D., Dombeva, L., Froldová, V., Gondová, D., completing the second stage, we will be able to further Hanesová, D., Hurajová, L., Leung, P., Luprichová, J., describe the attributes of free L2 learning websites in Sepešiová, M., Straková, Z., Šimonová, I., Trníková, J., Europe by providing examples and an overview of content Xerri, D., and Zavalarit, K. (2014). CALL and Foreign analysis based on the thirteen categories described in this Language Education : e-textbook for foreign language article. teachers. Nitra, Constantine the Philosopher University. 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