8
Self‐Paced Language Learning Using Online Platforms

PANAGIOTIS ARVANITIS

Introduction

At a time of digital convergence, a vast array of software platforms, sophisticated technologies, and services – such as blogs, social networking applications, wikis, learning management systems, online games, 3D role‐play games, virtual worlds, and simulations – are overwhelming our everyday lives. Several of these new “emerging” digital technologies affect both the learning of young users/learners and the training of older users/teachers. In recent years, information and communication technologies (ICT) have been an important, almost integral part of the daily lives of millions of people. Having already brought about significant economic, social, and cultural changes, they also offer an ever‐increasing momentum for the whole educational process. As predicted since 1997 by M. Dertouzos, former director of MIT's Computer Lab, the new “information market” has not only changed the role of schools, universities and the educational community, “but even the teachers themselves and scholars” (Dertouzos 1997, p. 183). ICTs have the potential to provide technological solutions, effective methods, and best practices, all integrated into the learning process by dynamically expanding the skills of both learner and teacher in the acquisition of knowledge.

Vast technological changes have contributed to this development, including the emergence of internet technologies, services, and possibilities. From the simple, static webpages of the early web, to the later dynamic websites that allowed interactive, two‐way communication, to today's modern complex social networking environments and websites that offer a multitude of “software as a service” solutions, the path has converged toward digital unification and globalization. The transformation of the internet has created a new and powerful environment of increased social interaction, in which users collaborate, communicate, create content and use services free of both cost and, to a certain extent, central control.

Language learning and technology

In the last decades, long‐term and in‐depth studies in the fields of linguistics, didactics of foreign language‐cultures, sociolinguistics, semiotics, pragmatics, and communication studies have led to the redefinition of the traditional way of teaching and learning a foreign language.

Though popular through the first half of the twentieth century, two fundamental teaching issues, linguistic structures and learner's linguistic ability, rapidly gave up their place to other themes: the functional use of the language and how different communicative circumstances and situations shape it. Halliday's (1978) and Hymes's (1984) contributions to the prevalence and adoption of the communicative approach to foreign language‐culture teaching was decisive and unambiguous, and their work led to the redefinition of the teaching objectives and materials used, and more generally, to the principles of the didactic practice.

In this approach, emphasis is placed on the learners' communicative competencies, as well as on the vital role that communication plays in second language (L2) or foreign language (FL) teaching. Today, it is clear that communication in a foreign language, achieved through multimodal texts in which different linguistic elements function, and linguistic elements cannot be considered as isolated teaching objectives, but as functional communicative structures. ICTs, with their dual nature, acting both as complex technological infrastructures and as software applications, can support a variety of cognitive processes and therefore can be considered important learning tools. The idea of supporting learning, and in a broader sense education, using new technologies brings together scientific areas that previously would have been unrelated to each other, such as that of ICTs and that of language teaching. From the early 1990s, in the field of foreign language teaching and learning, the initial attempts to introduce ICTs into the foreign language class (Jones and Fortescue 1987, p. 12) led to the development and later scientific autonomy of a new research field called computer‐assisted language learning (CALL) (Hardisty and Windeatt 1989; Higgins 1988).

In its first steps, the field of CALL mainly concerned itself with the methodology of teaching foreign languages and the uses of technology, as well as the development of software tools capable of supporting learners' production and understanding of the taught language. The first commercial attempts to connect ICTs with foreign language teaching and learning resulted in the appearance of several software applications that made use of the emerging multimedia technology aimed at a diverse audience interested in learning a foreign language. These software applications were targeted at different audiences, ranging from the early stages of schooling to adults, and various levels of language skill. In most cases, the following could be observed (Arvanitis 1998):

  • Several software applications had a tourist‐oriented use and approached the target language through a traveler‐centered concept.
  • Didactic theories were absent, while the methodological approaches were often fragmentary and incomplete.
  • In some cases, the applications' interfaces were unclear, complex and unhelpful, especially for younger and less‐experienced computer users.
  • There was a redundancy in the use of multimedia capabilities (images, sounds, videos) without an explicit purpose or a systematic teaching goal.
  • They were concerned mostly with written and oral comprehension. Therefore, repetitive exercises to develop a limited vocabulary were used.
  • They required minimal self‐enrollment by the user, thus making the user more passive.
  • In most cases, the content of these media was artificially created dialogues between native speakers of a foreign language, and therefore did not constitute authentic material that would allow learners to be engaged or to be exposed to a more natural communicative situation. It could also be noted that they were electronic rewritings of older teaching methods with multimedia elements, or in other words, the realization of old didactic approaches and practices presented with modern ICT tools.

In 1990, in the field of foreign language teaching, the question of design and creation of suitable teaching materials was critical. Foreign language teachers, and even more so learners, were often unable to access authentic material of the foreign language that they had to teach or learn. In addition, media that are today considered a given and are an integral part of contemporary studies, such as podcasts (Nurmukhamedov and Sadler 2011), YouTube videos, or video‐chat tools (Eröz‐Tugˇa and Sadler 2009) did not even exist. Therefore, both teachers and learners often had difficulties accessing authentic sources of speech, whether written or spoken, and had particular difficulty accessing native speakers of the target language.

Further research establishes the fact that a language‐culture is not only a broad set of morphosyntactic rules or a complex vocabulary system, but also several other social and cultural indicators. There are elements of linguistic codes that could not be transmitted through simple teaching methods and which are particularly relevant to intercultural communication, a fascinating issue in the teaching of a foreign language‐culture. Furthermore, problems with cultural representation concerning cultural authenticity and accuracy can arise (Shaughnessy 2003). Therefore, this linguistic knowledge is hard to incorporate into teaching methods, even with electronic forms.

However, according to the principles of the communicative and task‐based approach methods and the didactic proposals that have been formulated in them, language teaching must involve the production of learning materials, adapted to the specific needs of learners.

The next period of CALL began with the fast Web 2.0 expansion, and soon extended to the use of blogs, wikis, social networking platforms, podcasting tools, audio and video tools, folksonomies, Web 2.0 applications, virtual worlds, and interactive whiteboards (Davies et al. 2010).

All of the above technological developments and achievements have led in the last two decades not only to the emergence and practical application of many theories (e.g. behaviorist learning, constructivist learning, situated learning, sociocultural theory of learning, informal, and lifelong learning) but also to significant changes in both the content and methodology of the language education (El Hariry 2015). They have also led to greater and stronger integration of ICTs into the language teaching and learning processes, not as a simple tool but as an inexhaustible repository of language‐learning materials, since much of the learner's linguistic and social skills now requires contact with modern digital multimodal and multimedia texts.

The consolidation of the scientific field of CALL coincided with the general recognition that multimedia technologies should be understood as a generic platform for the integration of already existing communication media, while also being a platform one can use with relative ease and low cost to design, implement, and share, autonomous or semi‐autonomous interactive language teaching environments. This finding was reinforced by the general learning and didactic approaches that were developed, which have as their common denominator the learner's greatest involvement in real learning communicative and interactive language‐learning situations. A greater impetus in this direction led to the appearance of mobile learning (Mlearning, M‐learning, or mLearning) and mobile‐assisted language learning (MALL), which are briefly examined in the next paragraphs.

The era of mobile learning

Although it is beyond the scope of this chapter to mention all the attempts that have been made over the years that have contributed to the evolution of the field of ICTs and language teaching and learning, there will be a brief discussion regarding the most significant changes that have been made as a result of the appearance and establishment of mobile devices. With the appearance of the first personal digital assistant (PDA) devices in the late 1980s, CALL ushered in the new era of mobile learning. Initially, these PDAs – early precursors to current, modern mobile devices – had monochrome screens, weak processors and low memory capacity and did not have much of today's functionality. However, their widespread use has helped to develop and diffuse language‐related applications, such as elementary electronic dictionaries, small phrase books, grammars and, later, e‐books.

During the first half of 2007, the technological landscape of mobile devices changed rapidly with the appearance of Apple's first mobile device, the iPhone, based on Apple's iOS operating system; this was followed by the appearance of the Android operating system in September 2008. These two platforms were soon followed by Microsoft's Windows version for mobile devices systems.

The new devices had even more powerful processors, large high‐resolution responsive touch screens, large internal memory and storage capacities, large virtual keyboards, front and rear cameras, fast and improved cellular and Wi‐Fi connectivity, early cloud integration and connectivity, and other enhanced capabilities such as accelerometer, compass and global positioning system (GPS). The emergence of these operating systems and the devices based on them enabled for the first time the use and integration of mobile devices as both phones and small computers with a large set of capabilities.

These hardware developments were milestones in the first m‐learning period (Churchill et al. 2016; Pachler et al. 2010), since they enabled the development of a series of modern web‐based and mobile apps. Soon, m‐learning became a rapidly expanding field (Pachler et al. 2010) and furthermore, as Cook et al. mentioned, gained “increasing importance in what was frequently referred to as ‘informal’ (as opposed to ‘formal’) learning” (Cook et al. 2008).

An important related bibliography of that period can be found in the writings of Kukulska‐Hulme and Traxler (2005), Traxler and Kukulska‐Hulme (2005), Traxler (2007), Vavoula et al. (2009), Ally (2009), and O'Malley et al. (2005), and later in the books of Pachler et al. (2010), Churchill et al. (2016), and Farr and Murray (2016). But as mentioned by Pachler et al. “much of the work that is currently being done on mobile learning all over the world tends to remain ephemeral and comparatively scattered as it is still often only reported in the form of unpublished presentations or papers at conferences or it is documented in reports published fairly unsystematically on blogs or websites” (Pachler et al. 2010).

The era of mobile assisted language learning (MALL)

The rapid development and commercialization of mobile apps brought about significant scientific interest in exploring their use and potential in language teaching and learning. The establishment of m‐learning attracted researchers and practitioners from the field of CALL and, as Burston mentioned, MALL is equally capable of supporting more innovative constructivist, collaborative, learner‐centered instruction (Burston 2014). Many researchers such as Kukulska‐Hulme and Shield (2008), Chinnery (2006), Siskin (2009), and Godwin‐Jones (2011) addressed the new issues that arose in this field. Other studies focused on some of the theoretical issues of m‐learning, such as Thornton and Houser (2005) and Kress and Pachler (2007), and there were several case studies such as those by Kukulska‐Hulme and Traxler (2005), Metcalf and DeMarco (2006), and Stockwell (2008, 2013). We must also mention Burston's interesting work selecting an annotated bibliography of implementation studies between 1994 and 2012 (Burston 2013) and his interesting meta‐analysis of learning outcomes (Burston 2015; Bozdoğan 2015).

Web‐based and mobile platforms

In foreign language teaching and learning, we come across a variety of CALL or MALL software applications or environments that are partly or wholly computer‐aided instructional and teaching platforms. These platforms are respectively partly or wholly responsible for providing linguistic information, teaching a specific module or a whole foreign language course. They also include processes of assessing the language competencies and skills that students have acquired by completing each module or unit. These software platforms, environments, and applications are often divided, and depending on their usefulness and purpose in the language‐learning process divide into two categories: drill and practice, and tutoring systems (Paterson and Strickland 1986). The didactic design of the first is often based on the behavioral theory developed since the 1970s, while the latter is in the didactic model of the communication approach of language. The most modern version of language teaching and learning software follows the model of instructional design.

Today, there are a lot of free or paid web‐based platforms, mobile apps, or mixed environments that deal with language learning, provide partial or complete language courses, and are used by millions of users worldwide. According to PCMag online (www.pcmag.com), some of them (in alphabetical order) are Babbel, Duolingo, Fluenz, Living Language, Pimsleur, Rocket Languages, Rosetta Stone, Transparent Language Online, Yabla. Additionally, we can also find on the web other software tools dedicated to language learning: Busuu, HelloTalk, Leaf, LingQ, Memrise, MindSnacks, TripLingo, and many others.

The area of language‐learning software tools is continuously changing as any other sector of technology and economy are; therefore, it is not uncommon to have mergers or acquisitions between language‐learning software companies. A typical example is the acquisition of the Livemocha platform by Rosetta Stone Business Wire (2013) and the sudden closure of it in 2016 (Vuong 2016). These changes obviously caused a series of problems for their loyal users, but unfortunately, there are no studies or surveys that have documented and examined the corresponding issues.

For this section, a set of five different web‐based platforms and mobile apps have been selected: Duolingo, Babbel, Rosetta Stone, Busuu, Memrise; and two of them, Duolingo and Babbel, are studied more thoroughly in the research study section of this chapter. The Busuu and Memrise platforms have interesting characteristics that are different from the others, and are discussed here. By starting a short web search, we can find which of these web‐based platforms present significant web traffic over time and how popular they are. In order to examine and compare the global search interest among the five platforms the Google Trends service (trends.google.com) was used. This provided us with comparative data regarding the “interest by region” terms. The tool shows the platform (term) that was ranked highest in each region during a specified time frame. Values are scaled from 0 to 100, where 100 is the region with peak popularity, a value of 50 is the region where the term is half as popular, and a value of 0 means that term was less than 1% as popular as the peak. In Table 8.1, we report the results for the first 45 countries of a list of 100 in total. The results were quite interesting.

As shown in Table 8.1, Duolingo has achieved its highest values in the Latin American countries and many European countries. Babbel's high values were in some European countries (Switzerland, Germany, France), and Rosetta Stone in United States, Colombia, and other Latin American countries. A low but significant presence in all the above countries is Memrise. In countries, such as Russia or some Asian countries, all platforms appear at much lower values in the rest of the table. Also, due to a lack of data availability, semi‐free traffic estimator tools such as Similarweb (www.similarweb.com) or Alexa (www.alexa.com) were used.

It must be noted that these websites' traffic estimators use several algorithms to calculate sites' rankings, and any research should only compare readings from the same tool, in the same period.

The use of SimilarWeb tool also provided us with interesting results regarding:

  • total visits: the sum of all visits during a specific period,
  • average visit duration: the average amount of time visitors spends in a session,

    Table 8.1 Interest by region (according to Google Trends).

    Time frame: (April 28, 2017–April 28, 2018)
    ID Country Duolingo Babbel Rosetta Stone Busuu Memrise
    1 Guatemala 100 <1
    2 Colombia 85 2 10 1 2
    3 Venezuela 48 2 1 <1 1
    4 El Salvador 45 <1
    5 Mexico 44 2 2 <1 1
    6 Dominican Rep. 37 3 4 <1
    7 Honduras 37 5
    8 Nicaragua 37
    9 Peru 32 3 4 <1 1
    10 Ecuador 31 1 2 <1 1
    11 Panama 27 4 <1
    12 Hungary 26 1 1 2
    13 Ireland 25 3 2 2
    14 Canada 24 3 5 <1 1
    15 Costa Rica 23 3 <1
    16 Czechia 22 1 2
    17 Spain 20 4 1 <1 1
    18 Chile 18 2 1 <1 1
    19 Brazil 18 2 1 <1 1
    20 Romania 17 1 1 1
    21 New Zealand 16 2 2 2
    22 Bolivia 16 2 <1
    23 Uruguay 16 2
    24 Switzerland 15 13 2 <1 1
    25 Portugal 15 3 <1 1
    26 Argentina 15 1 1 <1 <1
    27 United States 14 1 6 <1 1
    28 Norway 14 1 1 2
    29 Greece 14 1 1 1
    30 Vietnam 14 2 <1 3
    31 France 13 6 1 <1 1
    32 Netherlands 13 5 2 <1 3
    33 Denmark 13 1 1 1
    34 Serbia 13 3 2
    35 Belgium 12 7 1 <1 1
    36 United Kingdom 12 2 3 <1 9
    37 Morocco 12 1 3 <1 2
    38 Algeria 12 1 2 <1 2
    39 Slovakia 12 1
    40 Poland 11 <1 1 <1 4
    41 Italy 11 7 1 <1 1
    42 Australia 11 2 2 2
    43 Croatia 11 1
    44 Austria 10 14 1 2
    45 Germany 9 16 2 <1 1

    Table 8.2 Traffic estimators' results (according to SimilarWeb).

    Time frame: (April 28, 2017–April 28, 2018)
    Duolingo Babbel Rosetta Stone Busuu Memrise
    Total visits 65.31 M 13.89 M 2.79 M 3.29 M 10.04 M
    Avg. visit duration 00:10:07 00:03:36 00:04:38 00:06:37 00:09:41
    Pages per visit 6.97 3.78 3.03 14.89 7.69
    Bounce rate 44.51% 39.31% 54.81% 46.73% 40.05%
  • pages per visit: the average number of pages visitors view on a site,
  • bounce rate: the percentage of visitors who enter a site and then leave, for each one of the examined websites.

As shown in Table 8.2, the platforms Duolingo and Babbel have a large number of total visits and are in first and second places, with Memrise in third place. Additionally, the average visit duration for Duolingo and Memrise platforms have rates that are close to one another. Babbel has the lowest bounce rate. In addition to these services, simple web search techniques were used to locate reports and research regarding these web platforms and mobile apps. The search revealed a plethora of information and references regarding the use and functionality of these platforms. There are a considerable number of articles in online non‐scientific journals, newspapers, special websites or web blogs that occasionally deal with the topic of learning a new language using these platforms. Indicatively we can mention a few: GuruFocus 2017; Guardian 2015; Airline Industry Information 2012; Johnson 2013, and others. Furthermore, information can be acquired through many YouTube informative or comparative videos. Conclusively, the above brief web search analysis revealed that there is a whole world full of language resources and online self‐paced platforms dedicated to language learning.

Duolingo

Duolingo is an online, free, award‐winning, self‐paced, language‐learning application. It is available both as a web‐based platform and as a downloadable mobile app for iOS, Android, and Windows operating systems. Duolingo launched in 2011 and as of April 2018, offers 35 different language courses. This number often varies if we assume that the site's main display language is not always English. In these cases, the number of available taught languages is significantly lower, e.g. from Spanish as primary language nine, from French five, from German three, from Russian four. In the Duolingo platform, the user/learner can create an account, edit his/her profile, choose a target language to learn and then proceed to study with specific language activities. The learner has the option to start from scratch or take a short “placement test” and let the system determine his/her language competence level.

A typical learner's experience consists of the study of short thematic areas and then proceeds to more advanced topics. Duolingo uses the terms language course, lesson, skill, and language tree to explain its structure and methodological approach to learners.

Language course is the taught language. Each of Duolingo's language courses is made up of a set of specific skills. “A course's skill tree or language tree is the organization of the course's skills into rows representing the order of course progression” (Duolingo 2018). Every language tree begins with some basics skills that provide the vocabulary to begin constructing simple sentences and to start learning a language. A Duolingo skill teaches a set of thematically or grammatically related words or concepts. Skills consist of lessons, meaning a set of activities that teach the core aspects of a skill. Each lesson can contain various activities, such as (Duolingo 2018):

  • Translation of a sentence in user's native language.
  • Transcription of audio of a sentence.
  • Speaking a sentence into learner's computer's microphone.
  • Fill in the blank.
  • Multiple‐choice translation in target/user's native language.
  • Picture flashcard translation.
  • Picture flashcard matching, to match a word in the target language.
  • Sentence shuffle, to rearrange a phrase in order to form a correct sentence.

As Teske mentioned, “these activities focus on translation, pronunciation, vocabulary, listening comprehension, and spelling skills” (Teske 2017, p. 397). The learner must complete all the skills on each row of the tree in order to proceed to the next row. Some other interesting elements of the platform are:

  • A goal‐setting tool that helps the learner to set learning goals in a specific period.
  • “Make it stick!” A tool for writing, speaking, or using flashcards to practice vocabulary and phrases.
  • A daily challenge tool.
  • Strength and measures skills tool that indicates which learned words and skills a learner needs to practice.
  • Other visual aid elements, such as an achievements indicator, an XP (experience points) indicator, colored indications of right/wrong answers, highlighted texts for new words or grammar issues, and an indicator of “lingots,” a Duolingo virtual currency which is used to reward people for various accomplishments related to language learning and translation (Duolingo 2018).

In an in‐depth and interesting analysis, Settles and Meeder describe some aspects of the platform's functionality and mechanics. According to them, Duolingo “combines point‐reward incentives with implicit instruction (DeKeyser 2008), mastery learning (Block et al. 1971), explanations (Fahy 2004), and other best practices” (Settles and Meeder 2016, p. 1849). Also, in the same paper, the authors describe how the platform contains, for each language, a corpus (large database of available exercises) and a lexeme tagger for automatically tagging and indexing the corpus and providing corrective feedback to the learner (Fahy 2004). Additionally, Duolingo uses a complex student model, spaced repetition algorithms and a mixture model of short‐term learning curves (Streeter 2015) which help the learner to masters all of the target words being taught in a session (Settles and Meeder 2016).

As noted by Teske, “previous researches have explored the validity or the effectiveness of Duolingo's platform. Amongst them: Garcia (Garcia 2013) has examined the effectiveness of web translations using the system's Immersion Tab (a feature no longer available), Ye (Ye 2014) has examined the validity, reliability, and concordance of the Duolingo English Test” (Teske 2017, p. 395). Furthermore, Vesselinov and Grego examined the effectiveness of the site for language learning (Vesselinov and Grego 2012), and Martinelli the effectiveness on Italian pronunciation feature (Martinelli 2009). A recent bibliography review of web applications used to learn a foreign language shows that Duolingo has been linked to the development of high‐impact educational researches (Abaunza and José Rodríguez‐Conde 2016).

Other researchers mention Duolingo either as part of a case study in a language classroom experience (Munday 2016) or as a case study of a bilingual learning app (Ahmed 2016).

In her analysis, Teske argues that many repetitive activities and the grammar focus activities are based in the audiolingual method (ALM) and the grammar‐translation method. These “may be appropriate for teaching vocabulary or grammar points but they lack the context needed for learners to produce the L2 in authentic contexts” (Teske 2017, p. 399). Therefore, she suggests that Duolingo may be more effective as a supplementary language‐learning tool.

Babbel

Babbel is an online, award‐winning, self‐paced, language‐learning web platform and downloadable mobile app for the iOS and Android operating systems. Babbel has a freemium, subscription‐based model, in which the user downloads the app for free, can proceed to study a language, but after the first courses, needs to pay a monthly or yearly fee to unlock all the features. The platform launched in 2008 and as of April 2018 offers language courses in 14 different languages (Bauer and Rathje 2014). Babbel, like the Busuu platform, examined below, uses the scale of the Common European Framework of Reference for Languages (CEFR 2001). The CEFR is widely accepted as the European standard for describing and grading language ability on a six‐point scale: Basic user (A1, A2), Independent user (B1, B2) and Proficient user (C1, C2). Babbel uses CEFR to describe courses by level of difficulty.

In Babbel, each course is divided into four main lessons: vocabulary, dialogue, grammar, and a final review. At the beginning of each lesson, the platform shows a set of new words and new grammar concepts. They are followed by a set of activities (matching, multiple choice or fill in the blank). These words and the grammar concepts taught are combined in short dialogues. In this way, Babbel tries to build some basic conversational skills. An interesting feature of the platform is the ability to converse with native speakers, other members of the Babbel community. This feature is also found on the Busuu platform.

Rosetta Stone

Rosetta Stone is one of the oldest and longest‐lasting platforms among those we are studying. It launched in the mid‐1990s and has gradually received several improvements and enhancements in new editions. Rosetta Stone is an online, award‐winning, self‐paced, language‐learning platform and downloadable mobile app for the iOS and Android operating systems. The platform uses an online subscription‐based model, in which the user pays every 3, 6, 12, or 24 months. In return, the user has full access to language courses of his/her choice plus the ability to use the iOS or Android apps. The platform offers a limited advertising demo and provides a short description of the language‐learning process for over 24 languages.

Rosetta Stone's approach to teaching a language was called comprehension approach, or natural approach in the mid‐1990s (Stoltzfus 1997), but today is referred to as dynamic immersion. By these terms, we mean a sort of immersive approach in which the software “gradually introducing sights and sounds, words, sentences, conversations, and concepts in a way that's supposed to accelerate the learning process” (Allan 2017). An interesting original feature of Rosetta Stone's software is that it uses only the target language through the language‐learning process, and not the user's native language.

Rosetta Stone has occupied many researchers for a long period of time. Amongst the research is that of Graff, on Rosetta Stone's effectiveness on improving English pronunciation (Graff 2006), and a multiple case study by Daniel about teacher perceptions and the factors that contribute to the successful implementation of Rosetta Stone for English language learners (Daniel 2008). There is also Rockman Et Al.'s Rosetta Stone Evaluation Report, in which the researchers conclude that the Rosetta Stone solution is both an effective and efficient means of learning Spanish as a foreign language (Rockman 2009). In the same period Vesselinov presented his report about Rosetta Stone's effectiveness, in which, after 55 hours of study a significant percentage of users (80%–90%) “agreed that Rosetta Stone Spanish software was easy to use, very helpful, enjoyable, and very satisfactory and that they will recommend this software to others” (Vesselinov 2009).

In more recent studies, we must mention the analytical review of Santos about Portuguese (Brazil) levels 1, 2, and 3 (Santos 2011), the work of Nielson, which explores the way adult learners use language‐learning software in the workplace (Nielson 2011), and the work of Ikonta and Ugonna about the impact of Rosetta Stone on English as a second language (ESL) students' proficiency in English language (Ikonta and Ugonna 2015). In some cases, the platform causes a scientific dialogue. This is the case of Lord's study, in which the author discussed Rosetta Stone and learner proficiency outcomes (Lord 2015); this resulted in a response from the Rosetta Stone team (Bowles et al. 2015) and a subsequent interesting dialogue. Finally, in his study, Krashen argues that “Rosetta Stone presents a tepid version of comprehensible input, that the evidence so far provides only modest support for its effectiveness and that studies do not agree about users' reactions” (Krashen 2013).

Memrise

Memrise appeared in late 2010 as a self‐paced language‐learning platform, for both web and mobile use. It looks like a flashcard‐based software that uses multimedia flashcards (texts and images) as memory aids. The software is primarily focused on the memorization of words and therefore uses various combinations of text, images, graphics, and sounds. As Henry pointed out the “application gamifies the learning process, by awarding points and reputation as learners progress, completes the different set of activities and develop their knowledge” (Henry 2013). Memrise uses sophisticated tools based on the “spaced repetition” technique. According to this technique, in which users tend to remember things more effectively if they use spaced repetition practice, the software assists in the rote‐memorization of information that is arranged in question‐answer pairs (Mounteney 2018). As observed by Godwin‐Jones “this and other similar programs (e.g., Anki) incorporate spaced repetition with intelligent automatic reminders, all‐device cloud‐based stack synchronization, crowd‐sourced graphics mnemonics, and multimedia integration” (Godwin‐Jones 2017, p. 5).

Some researchers criticize the use of the spaced‐repetition technique in the field of language learning because they think that there is not much room for learners to come into direct contact with the taught language in real‐life contexts. Consequently, learners lose their ability to produce language in similar contexts (Koike and Klee, 2013). Other researchers argue that using this technique, these types of software tools come to contribute and help vocabulary learning and oral comprehension and production.

Busuu

Busuu is an online, award‐winning, self‐paced, language‐learning web platform and downloadable mobile app for iOS and Android operating systems. It started in 2008 as a language‐learning web platform and in 2010 launched a mobile app. Busuu is completely different among the studied language‐learning apps. It is based on a more social‐based approach to learning a foreign language. It uses the social networking connectivity model simultaneously with a self‐study language learning‐oriented process and can be categorized as a social network site for language learning (SNSLL) like the ended Livemocha or CoffeeBreak Languages (www.radiolingua.com).

We could classify Busuu and similar efforts as online communities that involve the use of Web 2.0 software tools and which bring together people who are interested in learning languages. As Stevenson and Liu mentioned, “the main purpose of these online communities is to share and learn new languages through social interaction” (Stevenson and Liu 2010, p. 234). Loiseau et al. suggest it is rather communities of interest for language learning, supported by the social media with main characteristics the user interaction, the authentic communication, the online participation, the collective effort and the mutual assistance (Loiseau et al. 2011).

In Busuu, the user starts by learning a new language and during this process, the user may request the help of other native speakers of the language being learned who are also members of the Busuu online community. There are two types of online membership: standard and premium. In the paid premium membership users have access to features such as grammar units, official certificates, vocabulary trainer, offline mode, and conversations.

The study material for each language is divided into thematic lessons, and each lesson is based on communicative scenarios. Lessons often contain vocabulary and grammar practices, pronunciation exercises, interactive activities and quizzes and conversational practice with other native speakers, all members of the community. Learners and native speakers can converse via asynchronous voice recording methods or via text messaging. Each contribution of a native speaker to a learner's effort, and therefore to the learning community, is valued and rewarded by a system of berry points. More berries are automatically displayed on a native speaker's profile when he/she offers more services to the community (e.g. chat with learners, commenting on writing activities, correction of phrases, moderating an active group of learners, etc.).

In these ways, learners act both as tutors and students. This raises a question: Do users develop language skills through conversations and one‐on‐one communication even though neither of the interlocutors is a language teacher? As Liu et al. mentioned, “despite much enthusiasm about the potential of SNSs, there is little evidence‐based research on their use as teaching and learning tools for second language learners (Clark and Gruba 2010; Stevenson and Liu 2010; Zourou 2012)” (Liu et al. 2013, p. 3). In his interesting Busuu efficacy study, Vesselinov shows that overall 84% of the total of 196 participants improved their written proficiency in Spanish, and that the majority of users thought that Busuu was easy to use (86%), helpful (84%), and enjoyable (78%), and they were satisfied with it (74%) (Vesselinov 2016).

General remarks

The review we have conducted has revealed that all five self‐paced platforms and mobile apps examined can be used, to a greater or lesser extent, for foreign language learning. In particular, two of them – Duolingo and Babbel – present interesting specifications as shown in Table 8.3. In the following “Research Study” section of this chapter, we will examine these two platforms in more detail using the basic principles and guidelines of CEFR. Thus, we can observe the language competencies developed by Duolingo and Babbel (see Table 8.4). With regard to language activities, according to CEFR, both platforms present similar characteristics except for the development of oral/written interaction and oral/written mediation, as shown in Table 8.5.

Research study

The following research study started as an idea for a short assignment for both undergraduate and postgraduate students of the School of French Language and Literature of the Aristotle University of Thessaloniki, Greece, but soon turned into a more complex and interesting research study. The main scope was to familiarize students with the use and functions of some web‐based self‐paced platforms and mobile apps for language learning. The study mainly focuses on students' perception regarding two online language‐learning environments (Duolingo and Babbel) based on a set of specific criteria. These criteria were learners' motivation, learners' autonomy, and learners' self‐assessment and feedback. Data for the study included a survey of 45 undergraduate students and 38 postgraduate students. Additionally, all students produced a short individual report.

Table 8.3 Some interesting specifications of Duolingo and Babbel.

Specifications Duolingo Babbel
Languages offered 35 14
Single user engagement Yes Yes
Quiz type Instant feedback/Informative Instant feedback/Informative
Delivery method (Synchronous/Asynchronous) Asynchronous Asynchronous
Activity's instruction Yes Yes
Activities' typology Translate with a word puzzle
Transcribe audio in a foreign language
Transcribe audio in the native language
Pronounce word
Translate foreign language to native language
Multiple choice
Pronounce word
Image multiple choice
First letter multiple choice
Letter order
Cloze test
Listen and multiple choice
Multiple choice
True/False
Correct answer
Timer Yes Yes
Multimodal types Text / Image / Video/ Audio Text / Image / Video/ Audio
Shuffle answers Yes Yes
Overall quiz evaluation Yes Yes

Table 8.4 Language competencies developed.

Communicative language competencies (CEFR) Duolingo Babbel
Linguistic competence
Lexical competence Yes Yes
Grammatical competence Yes Yes
Semantic competence Yes Yes
Phonological competence No No
Orthographic competence Yes Yes
Sociolinguistic competence
Sociolinguistic competence (markers of social relations, rules of politeness, expressions of popular wisdom, dialect and accent) Yes (Limited) Yes (Limited)
Pragmatic competence
Discursive competence Yes Yes
Functional competence Yes Yes

Table 8.5 Language activities developed.

Language activities (CEFR) Duolingo Babbel
Aural reception (listening) Yes No
Visual reception (reading) Yes Yes
Audio‐visual reception Yes Yes
Oral production (speaking) Yes Yes
Written production (writing) Yes Yes
Oral interaction No No
Written interaction No No
Oral mediation No No
Written mediation No No

As Settles and Meeder indicate, Duolingo “uses a playfully illustrated, gamified design” (Settles and Meeder 2016, p. 1849). This formulation, along with the frequent use of expressions such as “joyful,” “playful,” “helpful,” “enjoyable,” “pleasant,” “gamified,” “with a beautiful reward system,” “with a point, earned system,” etc. used by many researchers when referring to these software tools, leads us to a working hypothesis. In several cases, modern language‐learning applications have acquired features that appear in serious games, that is, games that are used for educational purposes and employ instructional games design approaches and techniques. So, there is a particular interest in the design of the apps' environment, and their ludic character. It would therefore not be arbitrary to suggest that these applications are closely linked to the field of digital games. In such a ludic field, the concepts of learner motivation, learner autonomy, and learner self‐assessment, as well as the feedback that the learner receives from the applications, are of great importance.

Autonomy, motivation, self‐assessment, and feedback

As Prensky had already noticed, regarding digital game‐based learning, “fun is the most powerful source of motivation for learning because when learning is done under pleasant conditions, the learner understands easier concepts, activates him/her self and tries more” (Prensky 2007, p. 111). Electronic games are enjoyable and entertaining, so they boost the motivation for learning.

According to Malone the motivation for learning depends on the importance that the learner attributes to the ultimate purpose of an activity, the clarity of the purpose, and the individual objectives (Malone 1981). Therefore, motivation is a fundamental element of learner involvement in any type of learning task (Malone and Lepper 1987).

These thoughts support the idea that “in order to provide motivation for learning, a learning environment or tool must offer a challenge, stimulate learner's imagination and curiosity, provide a sense of satisfaction and control, maintain user's attention and show consistency between educational objectives and content” (Krystalli et al. 2014). On the other hand, from the learner's point of view, the goal of learning a foreign language is to gain the highest degree of linguistic autonomy so that one can successfully cope with all communicative situations which require the knowledge of the target language (Krystalli 2011). As Germain and Netten noticed, the linguistic autonomy defined as the learner's ability to “take language initiatives and spontaneously use new phrases in an authentic state of communication in the foreign language” (Germain and Netten 2004, p. 4).

The learner's autonomy notion (Holec 1981, 1991), introduced to the language didactics with the “advent of the communicative and student‐centered approaches, and these principles soon were adopted by the Council of Europe in the Framework 4 Living languages (1971–1981), and since then is one of fundamental principles of the Council of Europe” (Krystalli et al. 2012). So, the learner's autonomy arises from the learner's freedom to make decisions in an app/game and the ability to evaluate his strategies according to positive or negative feedback provided. In this sense, the learner's autonomy is a crucial element to the use and for the development of any language‐learning app. Consequently, the app/game must provide a self‐assessment system that enables the learner to distinguish success from failure (Sanchez 2013, p. 10). The learner should also be given the opportunity to identify and understand his weaknesses in order to intensify their efforts to develop skills according to his personal linguistic needs. According to Malone and Lepper, this performance feedback provides an ongoing challenge and helps to maintain motivation when it is: clear, regular, constructive, and encouraging (Malone and Lepper 1987).

Methodology and data collection

With the above concepts in mind, we decided to use an already constructed assessment tool for online language‐learning computer games and examine these applications with some of its criteria. The proposed tool is based on a theoretical evaluation framework whose criteria are, at the same time, criteria for instructional games design aimed at learning/teaching a foreign language (Krystalli 2011). It is structured around five notions: learner autonomy, self‐assessment and feedback, learner motivation, consistency of educational objectives and language skills, and credibility.

From this model and its 50 criteria, we chose a set of 24 criteria, as shown in Table 8.6.

The profile of the target group of undergraduate students was as follows: students of the School of French Language and Literature of the Aristotle University of Thessaloniki, who are in the fifth semester of their eight‐semester studies. According to their statement, they already knew French as the first foreign language and additional English at levels between B2 and C2. The profile of the target group of postgraduate students was as follows:university students of different foreign language departments (English, French, German, Italian, Russian). According to their statement, they already knew English at level B2–C1 and a second foreign language also at level B2–C1. It should be noted that all students, to a greater or lesser extent, during their studies, had attended university courses in linguistics, language didactics, pragmatics, semiotics, lexicology, sociolinguistics, and technology in language learning. Therefore, due to their subject matter, one can claim to have an increased interest in foreign languages and especially the way they are taught.

Both groups of users were asked to choose an application between Duolingo, Babbel, Rosetta Stone, and then to choose a language totally unknown to them but which they would be very interested in learning.

Users had to choose one of the suggested platforms and then engage themselves in a language‐learning process of the new, unknown language for seven days, recording their daily course routine as well as the positive or negative issues they encountered. After browsing the various platforms for one day, all users ended up exclusively dealing with two of them. From 45 undergraduate students, 34 chose Duolingo and 11 Babbel platforms, and from 38 postgraduate students, 31 chose Duolingo and 7 Babbel. Nineteen students wanted to use the Rosetta Stone platform, but unfortunately, the short demo that was provided in the website did not meet the time frame requirements. We should also note that Babbel does not provide all of its features in the free version. Table 8.7 shows the students' language selection according to their studies level. At the end of seven days, users filled in a short criteria table, delivered an Excel report with their daily course routine, and submitted an annotated report commenting on the issues they encountered. The table's 24 questions, were divided into three parts: Part A (learner's autonomy) with six criteria, Part B (self‐assessment and feedback) with seven criteria and Part C (learning motivation) with 11 criteria, as shown in Table 8.6. For all criteria, the possible choices were: Yes, No, and Not Applicable or Not Available (N/A).

Table 8.6 Short criteria table.

Part Criteria Yes No N/A
Part A
Learner autonomy
a1. Definition of educational objectives
a2. Definition of content
a3. Definition of domains
a4. Definition of language proficiency level
a5. Clearly defined instructions
a6. Easy access to each level's instructions
Part B
Self‐assessment and feedback
b1. Score/other indication in each level
b2. Final score indication
b3. Final score/status storage
b4. Point subtraction for each wrong answer
b5. Point subtraction when the learner asks support
b6. Appearance of the correct answer in the wrong player's answer
b7. Feedback for right/wrong answers
Part C
Learning motivation
c1. Clearly defined the final goal of the app
c2. Graded levels of difficulty
c3. Restriction of level changing
c4. Limitation of time to reply
c5. Variation of the app's speed
c6. Visual/acoustic reward
c7. Reward for access to the next level
c8. Visual/acoustic penalization
c9. Variable/fixed reinforcement
c10. Good graphics quality
c11. Good sound quality

Findings and students' comments

Due to this chapter's limitation, a detailed presentation of all the findings is not feasible here. However, we can summarize the main points of interest. In the Part A criteria regarding learner autonomy, both undergraduate students and postgraduate students gave similar responses, especially for criteria a5 (clearly defined instructions) and a6 (Easy access to each level's instructions). However, postgraduate students were more able to recognize educational objectives (a1), content (a2), and domains (a3). This can be explained by the longer duration and the level of their studies. In the criteria of part B regarding self‐assessment and feedback provided by applications, both groups of students sufficiently identified those cases where they considered that they received satisfactory feedback. Οnce again, the percentages of postgraduate students appears higher. Postgraduate students seem to have a higher perception and awareness about self‐assessment and feedback as opposed to undergraduate students who have high rates of N/A responses.

Table 8.7 Undergraduate and postgraduate students' language selection.

Language selected Undergraduate students Postgraduate students
Duolingo Babbel Duolingo Babbel
French 5 1
Italian 8 3 6 1
Japanese 1 2
Spanish 21 6 16 4
Turkish 4 2 2 1
34 11 31 7

In the criteria of part C regarding learning motivation, both groups of students seem to recognize and diagnose some ludic features of applications, such as visual/acoustic rewards, or graphics and sound quality efficiently, that they consider being motivational factors for learning. In the submitted reports commenting on the issues they encountered, almost all students found the language‐learning process very interesting and pleasant. The following are some of the student's comments on Duolingo:

  • “I started my language learning experience, but it seemed quite difficult for me because there was any starting theoretical framework on grammar. The positive thing is that there were an immediate evaluation and feedback of what you did.”
  • “It seemed to me that it was more favored to memorize and memorize some. It made me feel like I had some vivid images of words.”
  • “I found that it is difficult to follow the Italian language course from English as the main language. I had to think at the same time in three languages (Italian, English, Greek), and after a while, that seemed to be extremely tiring.”
  • “I found that the typing method is slow and sometimes I do not want to deal with activities that require typing.”
  • “Using Duolingo, the understanding of the formation of adjectives in the Turkish language without any prior theoretical knowledge was almost impossible. The software showed me the right responses, but after many attempts, I simply memorized the correct answer and proceeded. In some modules such as the formation of plural names the learning was very obvious to me.”
  • “Despite Duolingo's addictive gamifying elements, its attractive interface and the sense of accomplishment you get through the levels and the reward points, there are some shortcomings, such as lack of real‐life communicative situations. I think that the focus is on mastering sounding structural blocks rather than developing learners conversational skills.”

Comments about Babbel:

  • “I have liked Babbel' s approach very much, but unfortunately it provides only a short demo of each language course. So, I have decided to pay for a month in order to complete this assignment.”
  • “Using Babbel, I liked the way that introduces the learner to the grammar of the language taught and how it links the grammatical phenomena between them.”
  • “I used the program mainly on my mobile phone, and it seemed very easy, even I could not repeat the phrases because I was in the subway.”
  • “I had used Rosetta Stone in the past, for one semester, to learn Spanish. I find that Babbel has some similar features, it seems to me a severe application and I like the way it gives much information about grammar.”
  • “As far as I am concerned, Babbel was very interesting, but I think that such learning materials cannot be used by absolute language beginners or cannot be used as primary learning materials in a language learning process since you never learn the language fundamentals.”

Conclusion

The primary goal of this chapter was to review some of the mainstream online language‐learning environments, both web‐based and mobile applications, and then discuss some of their features from a learner's perspective. Another goal was to determine whether these applications can reinforce, motivate, and engage language learners, due to their playful nature. Our initial research has shown that there are a significant number of language teaching/learning web platforms and mobile applications that are used by millions of users worldwide. Our bibliographic research has also shown that there is a great academic interest in how these web and mobile apps work, how they can be evaluated and whether they contribute to the development of linguistic competencies and learners' autonomy. Rosetta Stone, Babbel, and Duolingo are among the most‐examined applications, and they have been linked to the development of high‐impact educational researches.

The short review we conducted in five selected self‐paced platforms and mobile apps revealed that, to a greater or lesser extent, all are entirely dedicated to foreign language learning and present some interesting features with both pros and cons for their use as applications for language learning. All examined platforms show ludic features that refer quite closely to digital games, or a gamified approach to learning, doing and practicing, which seem to increase user's interest, engagement, and motivation. It must be noted that in some cases, some functions of the web‐based versions were not integrated or fully implemented into the mobile apps, or the free, limited versions of them do not let users decide easily which tool is suitable for their learning profile. By further examining the communicative language competences and activities, developed especially by the Duolingo and Babbel platforms, on the basis of the Common European Framework of Reference for Languages, we can observe that they do not develop oral and written mediation, or oral or written interaction, activities challenging to implement in digital environments.

The research study we conducted focuses on students' perceptions regarding two main online language‐learning environments (Duolingo and Babbel) based on a set of 24 specific criteria. The findings revealed that both groups of students found the language‐learning process very interesting and pleasant and as future language teachers were able to recognize most of the educational objectives, content, and domains which they encountered during the use of the platforms. Furthermore, both groups of students have identified those cases that they considered to have received feedback. They also consider that some ludic features of applications, such as visual/acoustic rewards, or graphics and sound quality, are motivational factors for learning.

The language teaching/learning web platforms and mobile applications are constantly evolving. Many of them are rapidly refreshing and adding new features and capabilities that existing technology is already making available; others are evolving at a slower pace and a few are completely shutting down. At this point, we should note the lack of evaluation models to assess the efficiency and performance of this software as educational tools, and that much work remains to be done in this direction. Another exciting direction of research will be the development of modern instructional design models for language teaching and learning software tools. However, the problem of how to learn a foreign language‐culture (two concepts that are inseparable) (Halliday 1978; Moirand 1984), has occupied and continues to concern all those involved in the foreign language learning and teaching processes. Having that in mind, we must emphasize the considerable time, effort, and money that have been spent in the development and improvement of such web platforms and mobile apps, and at the same time we should note the critical role they are playing in allowing worldwide users with economic, social, or personal difficulties to come into contact with other languages and cultures. Finally, self‐paced language‐learning web platforms and mobile apps with their ludic character seem to be suitable for use in low levels of language skills and they can act both as complementary and combinational tools in teaching/learning a foreign language.

REFERENCES

  1. Abaunza, G. and José Rodríguez‐Conde, M. (2016). Bibliographic review on web applications used to learn a foreign language. In: Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality ‐ TEEM'16, 229–234. New York, USA: ACM Press https://doi.org/10.1145/3012430.3012522.
  2. Ahmed, H.B.E. (2016). Duolingo as a bilingual learning app: a case study. Arab World English Journal 7 (2): 255–267.
  3. Airline Industry Information (2012). Rosetta Stone language‐learning solution selected by Gatwick airport as employee development tool. In: Airline Industry Information. Coventry: Normans Media Ltd. https://search.proquest.com/docview/929468512.
  4. Allan, P. (2017). Language learning showdown: Rosetta Stone vs. Duolingo. www.lifehacker.com.au/2017/01/language‐learning‐showdown‐rosetta‐stone‐vs‐duolingo.
  5. Ally, M. (2009). Mobile Learning. Transforming the Delivery of Education and Training, vol. 7. Athabasca University: AU Press.
  6. Arvanitis, P. (1998). Multimedia databases: design, implementation and use in language teaching applications. In: Proceedings of the 12th Symposium on Applied Linguistics, Department of English Language and Literature, Aristotle University of Thessaloniki (in Greek), 18–25. Thessaloniki.
  7. Bauer, D. and Rathje, B. (2014). Miriam Plieninger on language learning with Babbel. XRDS: Crossroads, The ACM Magazine for Students 21 (1): 58–59. https://doi.org/10.1145/2659895.
  8. Block, J.H., Airasian, P.W., Bloom, B.S., and Carroll Roll, J.B. (1971). Mastery Learning: Theory and Practice. New York: Holt, Rinehart, and Winston.
  9. Bowles, A.R., Frumkes, L.A., Harper, D., and Stone, L. (2015). Supporting language learning through technology: a response to Lord (2015). The Modern Language Journal 99 (3): 394–396. https://doi.org/10.1111/modl.12270.
  10. Bozdoğan, D. (2015). MALL revisited: current trends and pedagogical implications. Procedia ‐ Social and Behavioral Sciences.https://doi.org/10.1016/j.sbspro.2015.06.373.
  11. Bradin‐Siskin, C. (2009). Language learning applications for smartphones, or small can be beautiful. http://www.edvista.com/claire/pres/smartphones/#neallt
  12. Burston, J. (2013). Mobile‐assisted language learning: a selected annotated bibliography of implementation studies 1994–2012. Language Learning & Technology 17 (3): 157–225. https://doi.org/10.1080/0022027032000276961.
  13. Burston, J. (2014). MALL: the pedagogical challenges. Computer Assisted Language Learning 27 (4): 344–357. https://doi.org/10.1080/09588221.2014.914539.
  14. Burston, J. (2015). Twenty years of MALL project implementation: a meta‐analysis of learning outcomes. ReCALL 27 (1): 4–20. https://doi.org/10.1017/S0958344014000159.
  15. BusinessWire. (2013). Rosetta Stone acquires online language company Livemocha. (2 April). https://www.businesswire.com/news/home/20130402005459/en/Rosetta‐Stone‐Acquires‐Online‐Language‐Company‐Livemocha
  16. CEFR (2001). Common European Framework of Reference for Languages: Learning, Teaching, Assessment. Council of Europe https://www.coe.int/en/web/common‐european‐framework‐reference‐languages.
  17. Chinnery, M.G. (2006). Emerging technologies going to the MALL: mobile assisted language learning. Language Learning & Technology. 10 (1): 9–16.
  18. Churchill, D., Lu, J., Chiu, T.K.F., and Fox, B. (eds.) (2016). Mobile Learning Design. Theories and Application. Singapore: Springer https://doi.org/10.1007/978‐981‐10‐0027‐0.
  19. Clark, C. and Gruba, P. (2010). The use of social networking sites for foreign language learning: an autoethnographic study of Livemocha. In: Proceedings of the 2010 ASCILITE Conference “Curriculum Technology Transformation for an Unknown Future” (eds. C.H. Steel, M.J. Keppell, P. Gerbic and S. Housego), 164–173. Australasian Society for Computers in Learning in Tertiary Education. The University of Queensland www.ascilite.org.au/conferences/sydney10/procs/Cclark‐full.pd.
  20. Cook, J., Pachler, N., and Bradley, C. (2008). Bridging the gap? Mobile phones at the Interface between informal and formal learning. Journal of the Research Center for Educational Technology (RCET) 4 (1): 3–18.
  21. Daniel, M.S. (2008). Exploring Teacher Perceptions about the Factors that Contribute to the Successful Implementation of Rosetta Stone for English Language Learners: A Multiple Case Study. Walden University.
  22. Davies, G., Walker, R., Rendall, H., and Hewer, S. (2010). Introduction to computer assisted language learning (CALL). In: Information and Communications Technology for Language Teachers (ICT4LT) (ed. G. Davies), Module 1.4. Slough. Thames Valley University.
  23. DeKeyser, R. (2008). Implicit and explicit learning. In: Handbook of Second Language Acquisition, 313–348. Wiley.
  24. Dertouzos, M.L. (1997). What Will be: How the New World of Information Will Change our Lives. HarperEdge.
  25. Duolingo. (2018). Duolingo Wiki. https://duolingo.fandom.com/wiki/Duolingo_Wiki.
  26. El Hariry, A.N. (2015). Mobile phones as useful language learning tools. European Scientific Journal 11 (16): 298–317. https://doi.org/10.1108/09600031011093214.
  27. Eröz‐Tugˇa, B. and Sadler, R. (2009). Comparing six video chat tools: a critical evaluation by language teachers. Computers and Education 53 (3): 787–798. https://doi.org/10.1016/j.compedu.2009.04.017.
  28. Fahy, P.J. (2004). Media characteristics and online learning technology. In: Theory and Practice of Online Learning (eds. T. Anderson and F. Elloumi), 137–171. Athabasca University.
  29. Farr, F. and Murray, L. (eds.) (2016). The Routledge Handbook of Language Learning and Technology. Routledge.
  30. Garcia, I. (2013). Learning a language for free while translating the web. Does Duolingo work? International Journal of English Linguistics 3 (1): 19–25. https://doi.org/10.5539/ijel.v3n1p19.
  31. Germain, C. and Netten, J. (2004). Facteurs de Développement de l'autonomie Langagière En FLE/FLS. ALSIC 7: 55–69.
  32. Godwin‐Jones, R. (2011). Mobile apps for language learning. Language Learning & Technology 15 (2): 2–11.
  33. Godwin‐Jones, R. (2017). Smartphones and language learning. Language Learning & Technology 21 (2): 3–17. http://llt.msu.edu/issues/june2017/index.htm/Godwin‐Jones&id=EJ1142376.
  34. Graff, M. (2006). A Study of Rosetta Stone's Effectiveness on Improving English Pronunciation. Faculty of California State University Dominguez Hills.
  35. Guardian (2015). Learning the Duolingo ‐ How One App Speaks Volumes for Language Learning. The Guardian Online https://www.theguardian.com/business/2015/mar/08/learning‐the‐duolingo‐how‐one‐app‐speaks‐volumes‐for‐language‐learning.
  36. GuruFocus. (2017). “GuruFocus.Com: Rosetta Stone and SOURCENEXT Announce Strategic Partnership in Japan.” Newstex Finance & Accounting Blogshttps://search.proquest.com/docview/1898646674?accountid=8359.
  37. Halliday, M.A.K. (1978). Language as Social Semiotic. London: Edward Arnold.
  38. Hardisty, D. and Windeatt, S. (1989). Call. Oxford: Oxford University Press.
  39. Henry, A. (2013). Five best language learning tools. www.lifehacker.com.au/2013/10/five‐best‐language‐learning‐tools.
  40. Higgins, J. (1988). Language, Learners, and Computers. London: Longman.
  41. Holec, H. (1981). Autonomie de l'apprenant et l'apprentissage Des Langues. Strasbourg: Conseil de l'Europe.
  42. Holec, H. (1991). Autonomie de l'apprenant: De l'enseignement à l'apprentissage. Éducation Permanente 107.
  43. Hymes, D.H. (1984). Vers La Compétence de Communication. Paris: Hatier‐Crédif.
  44. Ikonta, N.R. and Ugonna, N.C. (2015). The impact of Rosetta Stone (Call) software on Esl students' proficiency in English language. International Journal of Arts & Sciences 8 (1): 349–361.
  45. Johnson (2013). Rosetta Stone. An in‐depth look at a widely touted bit of language‐learning software. The Economist (3 January) https://www.economist.com/johnson/2013/01/03/rosetta‐stone.
  46. Jones, C. and Fortescue, S. (1987). Using Computers in the Language Classroom. London: Longman.
  47. Koike, D. and Klee, C. (2013). Lingüística aplicada: Adquisición del español como segunda lengua. Hoboken, NJ: Wiley.
  48. Krashen, S.D. (2013). Rosetta Stone: does not provide compelling input, research reports at best suggestive, conflicting reports on users' attitudes. International Journal of Foreign Language Education 8 (1): 22–24.
  49. Kress, G. and Pachler, N. (2007). Thinking about the “M” in M‐Learning' from Mobile Learning, towards a Research Agenda (ed. N. Pachler). London: WLE Centre, Institute of Education.
  50. Krystalli, P. (2011). An Evaluation Model for Educational on‐Line Computer Games for Foreign Language Teaching and Learning. Aristotle University of Thessaloniki.
  51. Krystalli, P., Arvanitis, P., and Panagiotidis, P. (2012). An assessment tool for online language learning computer games. In Proceedings of London International Conference on Education (LICE 2012). London.
  52. Krystalli, P., Arvanitis, P., and Panagiotidis, P. (2014). Evaluating serious games for foreign language learning: an online grading and visualization tool. International Journal for Cross‐Disciplinary Subjects in Education 5 (1): 1564–1570. https://doi.org/10.20533/ijcdse.2042.6364.2014.0219.
  53. Kukulska‐Hulme, A. and Shield, L. (2008). An overview of mobile assisted language learning: from content delivery to supported collaboration and interaction. ReCALL 20 (3): 271–289.
  54. Kukulska‐Hulme, A. and Traxler, J. (2005). Introduction. In: Mobile Learning. A Handbook for Educators and Trainers. The Open and Flexible Learning Series (eds. A. Kukulska‐Hulme and J. Traxler), 1–6. London, New York: Routledge.
  55. Liu, M., Evans, M., Horwitz, E. et al. (2013). A study of the use of social network sites for language learning by university ESL students. In: Social Networking for Language Education (eds. M. Lamy and K. Zourou). NY: Palgrave Macmillan https://www.palgrave.com/la/book/9781137023360.
  56. Loiseau, M., Potolia, A., and Zourou, K. (2011). Communautés Web 2.0 d'apprenants de Langue Avec Parcours d'apprentissage: Rôles, Pédagogie et Rapports Au Contenu. In: Proceedings of EIAH'2011: A La Recherche des Convergences Entre les Acteurs des Environnements Informatiques Pour l'Apprentissage Humain (EIAH), 111–124. Université de Mons‐ Hainaut.
  57. Lord, G. (2015). I don't know how to use words in Spanish': Rosetta Stone and learner proficiency outcomes. The Modern Language Journal 99: 401–408.
  58. Malone, T.W. (1981). What makes things fun to learn? A study of intrinsically motivating computer games. Proceedings of the 3rd ACM SIGSMALL Symposium and the First SIGPC Symposium on Small Systems ‐ SIGSMALL '80. Pipeline 6 (2).
  59. Malone, T.W. and Lepper, M.R. (1987). Making learning fun: a taxonomy of intrinsic motivations for learning. In: Aptitude, Learning, and Instruction Volume 3: Conative and Affective Process Analyses (eds. R. Snow and M.J. Farr), 223–253. Hillsdale, NJ: Lawrence Erlbaum Associates Publishers.
  60. Martinelli, M. (2009). Effectiveness of Online Language Learning Software (Duolingo) on Italian Pronunciation Features: A Case Study. Faculty of the Graduate College of the Oklahoma State University.
  61. Metcalf, D.S. and DeMarco, J.M. (2006). mLearning: Mobile learning and performance in the palm of your hand, Amherst, MA: HRD Press, Inc.
  62. Moirand, S. (1984). Enseigner à Communiquer Εn Langue Étrangère. Paris: Editions Hachette.
  63. Mounteney, M. (2018). Spaced repetition learning systems (SRS). http://www.omniglot.com/language/srs.php (accessed May 30, 2018).
  64. Munday, P. (2016). The case for using Duolingo as part of the language classroom experience. RIED. Revista Iberoamericana de Educación a Distancia 19 (1): 83–101. https://doi.org/10.5944/ried.19.1.14581.
  65. Nielson, K.B. (2011). Self‐study with language learning software in the workplace: what happens? Language Learning & Technology 15 (3): 110–129. http://llt.msu.edu/issues/october2011/nielson.pdf.
  66. Nurmukhamedov, U. and Sadler, R. (2011). Podcasts in four categories: applications to language learning. In: Academic Podcasting and Mobile Assisted Language Learning: Applications and Outcomes, 176–195. IGI Global https://doi.org/10.4018/978‐1‐60960‐141‐6.ch011.
  67. O'Malley, C., Vavoula, G., Glew, J.P. et al. (2005). Guidelines for learning/teaching/tutoring in a mobile environment. [HAL ID: hal‐00696244f]
  68. Pachler, N., Bachmair, B., and Cook, J. (2010). Mobile Learning. Structures, Agency, Practices. Boston, MA: Springer US https://doi.org/10.1007/978‐1‐4419‐0585‐7.
  69. Paterson, W. and Strickland, J. (1986). Garbage In/Garbage Out: Evaluating computer software. Paper presented at the Annual Symposium of the New York College Learning Skills Association, Ellenville, NY, April 14, 1986.
  70. Prensky, M. (2007). Digital Game‐Based Learning. Minnesota: Paragon House.
  71. Rockman . (2009). Rosetta Stone Evaluation Report. San Francisco: Rockman Et Al.
  72. Sanchez, E. (2013). Key criteria for game design. A framework. In: Business Game‐Based Learning in Management Education (eds. N. Balsissin, S. Bettiol, S. Magrin and F. Nonino), 75–96. The Business Game.
  73. Santos, V.D.O. (2011). Rosetta Stone Portuguese (Brazil) Levesl 1, 2, & 3. CALICO Journal 29 (1): 177–194.
  74. Settles, B. and Meeder, B. (2016). A Trainable spaced repetition model for language learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics 1, 1848–1858. https://doi.org/10.18653/v1/P16‐1174.
  75. Shaughnessy, M. (2003). CALL, commercialism and culture: inherent software design conflicts and their results. ReCALL 15 (2): 251–268.
  76. Stevenson, M.P. and Liu, M. (2010). Learning a language with web 2.0: exploring the use of social networking features of foreign language learning websites. CALICO Journal 27 (2): 233–259.
  77. Stockwell, G. (2008). Investigating Learner Preparedness for and Usage Patterns of Mobile Learning. ReCALL 20 (3): 253–270.
  78. Stockwell, G. (2013). Tracking learner usage of mobile phones for language learning outside of the classroom. CALICO Journal 30, Learner‐Computer Interaction in Language Education: A Festschrift in Honor of Robert Fischer: 118–136.
  79. Stoltzfus, A. (1997). The learning theory behind the Rosetta Stone language library from Fairfield language technologies. Paper presented at Annual Meeting of the National Association for Bilingual Education. Albuquerque, NM.
  80. Streeter, M. (2015). Mixture Modeling of individual learning curves. Educational Data Mining: 45–52.
  81. Teske, K. (2017). Duolingo. CALICO Journal 34 (3): 393–402.
  82. Thornton, P. and Houser, C. (2005). Using mobile phones in English education in Japan. Journal of Computer Assisted Learning 21 (3): 217–228. Blackwell Publishing. doi:https://doi.org/10.1111/j.1365‐2729.2005.00129.x.
  83. Traxler, J. (2007). Defining, discussing, and evaluating Mobile learning: the moving finger writes and having writ. International Review of Research in Open and Distance Learning 8 (2) https://doi.org/10.19173/irrodl.v8i2.346.
  84. Traxler, J. and Kukulska‐Hulme, A. (2005). Evaluating Mobile learning: reflections on current practice. In: Proceedings of MLEARN 2005, October, 25–28. South Africa: Cape Town.
  85. Vavoula, G., Pachler, N., and Kukulska‐Hulme, A. (eds.) (2009). Researching Mobile Learning: Frameworks, Methods and Research Design. Oxford: Peter Lang.
  86. Vesselinov, R. (2009). Measuring the effectiveness of Rosetta Stone®. New York. http://resources.rosettastone.com/CDN/us/pdfs/Measuring_the_Effectiveness_RS‐5.pdf.
  87. Vesselinov, R., and Grego, J. (2012). Duolingo Effectiveness Study. City University of New York.
  88. Vesselinov, R., and Grego, J. (2016). The Busuu Efficacy Study. Final Report. http://comparelanguageapps.com/documentation/The_busuu_Study2016.pdf
  89. Vuong, Madeline. (2016). Livemocha language community officially closing, 3 years after Rosetta Stone acquisition. GeekWire (24 March). https://www.geekwire.com/2016/livemocha‐language‐community‐officially‐closing‐rosetta‐stone‐acquisition.
  90. Ye, F. (2014). Validity, Reliability, and Concordance of the Duolingo English Test. Technical Report, University of Pittsburgh. http://duolingo‐papers.s3.amazonaws.com/other/ye.testcenter14.pdf.
  91. Zourou, K. (2012). On the attractiveness of social media for language learning: a look at the state of the art. (ed. F. Demaizière and K. Zourou). ALSIC 15 (1) (Special issue “language learning and social media: (r)evolution?”). http://alsic.revues.org/2436.

INTERNET SOURCES

Babbel: www.babbel.com

Busuu: www.busuu.com

Duolingo: www.duolingo.com

Fluenz: https://fluenz.com

HelloTalk: www.hellotalk.com

LinguaLift: www.lingualift.com/

LingQ: www.lingq.com

Living Language: www.livinglanguage.com

Memrise: www.memrise.com

MindSnacks: www.mindsnacks.com

Pimsleur: www.pimsleur.com

Rocket Languages: www.rocketlanguages.com

Rosetta Stone: www.rosettastone.com

Transparent Language Online: www.transparent.com

TripLingo: www.triplingo.com

Yabla: www.yabla.com

..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
35.170.81.33