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Social networked game dynamics in web 2.0 language learning communities

Les mécanismes de jeu appuyés par les réseaux sociaux dans les communautés web 2.0 d'apprentissage des langues
Katerina Zourou et Marie-Noëlle Lamy

Résumés

Cette étude examine la dynamique de jeu qui s'observe dans certains réseaux d'apprentissage des langues, et son potentiel pour l'apprentissage linguistique et le soutien par les pairs. Notre terrain comprend quatre catégories de communautés d'apprentissage des langues. Les mécanismes de jeu inscrits dans la conception-même de l'activité constatée dans chacune de ces catégories font l'objet d'une triple interrogation concernant le rôle de l'apprenant, les comportements attendus et la posture d'apprentissage. Notre étude se situe dans le contexte du web 2.0 et cible les évolutions actuelles de la "collaboration collective" ("crowdsourcing", Howe, 2006) qui simultanément encourage l'investissement par l'apprenant d'un espace de liberté et mobilise le talent inhérent de la "foule", que toutefois peuvent exploiter ensuite des sociétés commerciales. Nous démontrons que les mécanismes du jeu précontraignent l'activité de l'utilisateur en facilitant chez les concepteurs des sites une approche subtilement incitative, qui n'est pas clairement déclarée à l'utilisateur au moment de son inscription. Nous montrons que c'est la dynamique de jeu qui induit toute l'activité en ligne, qu'elle peut promouvoir l'apprentissage, mais aussi profiter commercialement aux start-ups du web 2.0 qui sont à l'origine de ces communautés.

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1. Introduction

1The interest of the computer-assisted language learning (Call) community in digital games and game-based learning (GBL) is growing, as is demonstrated in two recent collective publications (Reinders, 2012; Cornillie et al., 2012). Due to their impact on online human activity, game mechanics (GM) have started to appear in non-game environments, generating the concept of gamification that can be defined as "[the incorporation of] game elements into a non-gaming software application to increase user experience and engagement" (Dominguez et al., 2013: 381). The background to gamification in language learning sites is external to Call, therefore we start our study with key background notions such as social networked game dynamics and crowdsourcing (Section 2). We then move on to the context of investigation, made up of web 2.0 language learning communities (Section 3). The aim of the study, the research questions and the conceptual framework follow. Section 4 deals with the analysis of game mechanics identified in the web 2.0 language learning communities under scrutiny. Our methodological approach is grounded in a critical appraisal of each type of game mechanics, which results in a discussion section (Section 5). The limitations of our study and a synthesis of findings are presented in Section 6, with an overall conclusion and future orientations in Section 7.

2. Key background notions

2Gamification, as the concept of designing and embedding motivational strategies leading users to deepen their engagement in digital environments, is a multi-faceted and broad phenomenon that is outlined in this section as a problem framing element. It is only discussed in conjunction with three associated concepts: firstly the incentivized dimension of gamification, with user activity influenced through GM, secondly the social networking feature spreading game activity through networks, and thirdly the crowdsourcing concept.

2.1. Digital gaming and gamification as a trigger of user networked activity

3A detailed definition of gamification is offered by Anderson and Rainie (2012: 2), who describe it

as an interactive online design that plays on people's competitive instincts and often incorporates the use of rewards to drive action – these include virtual rewards such as points, payments, badges, discounts, and "free" gifts; and status indicators such as friend counts, retweets, leader boards, achievement data, progress bars, and the ability to "level up".

4There are many types of GM (Wikipedia – Game mechanics, nd) and such a typology is outside the scope of this paper. However, it is important to note that GM are designed to induce user behaviours and attitudes in given digital environments. Their selection and arrangement is subject to a game design process that, among other elements, links selected GM to types of trigger and types of target group. An example of the sophisticated design that can be developed around GM, types of game motivators and expected behaviours is given by Zichermann and Cunningham (2011). In the figure below, different motivators apply to different target groups (an individual, his/her friends and a large group).

Figure 1 – Personal, friend and group motivators leveraged by game designers (Zichermann & Cunningham, 2011: 145).

Figure 1 – Personal, friend and group motivators leveraged by game designers (Zichermann & Cunningham, 2011: 145).
  • 1 http://stiet.cms.si.umich.edu/icd (...)

5More generally, GM design and implementation are part of the overall Incentive-Centred Design approach (ICD; Deterding et al., 2011) that can be broadly described as "the science of designing systems or institutions that align (…) individual incentives with overall (…) social goals"1. We therefore agree with Hamari and Koivisto (2013), who see an overlap between gamification and persuasive technologies (Fogg, 1999), as the former "attempts to influence behaviour in several domains by providing gameful experiences that subsequently can influence attitude and behaviour or affect customer loyalty or decision" (Hamari & Koivisto, 2013).

2.2. Social networks spreading game activity through digital and physical networks

6The strategy consisting of matching individual incentives to overall social goals in the definition of ICD above makes social networked activity the driving force of game-based design. Indeed, social networking and game dynamics are intertwined with social networking serving to spread and accelerate user game activity and its signs of engagement and recognition across networks of one's friends. From a design perspective, this is achieved through "architectures of participation", referring to "a built-in ethic of cooperation, in which the service acts primarily as an intelligent broker, connecting the edges to each other and harnessing the power of the users themselves" (O'Reilly, 2007: 22). For the author, the architectures of participation enable and expand network effects, a key concept that together with user participation and openness, compose the three key pillars of web 2.0 (for a discussion see Zourou, 2012).

7When it comes to GM embedded in a digital platform, it is recognized that network effects facilitated by web 2.0 technologies are instrumental in spreading the impact of gaming activity through social networks that are not only digital but also location-based (McKenzie, 2011), by means of location-aware features that are increasingly embedded in web 2.0 technologies. Location-based mobile social networks (the best known being Foursquare) are not totally unknown in the field of Call. Polyglot Club Meetings (nd), one of the communities scrutinised in this paper is a service offered to language learners participating in online language exchanges, who also meet offline. We address this issue in Section 5.

8Finally, network effects through which game-based activity is propagated are the driving force of value-making mechanisms. These are understood as profit-making activity although the symbolic value of gaming for users (e.g. through recognition, recommendation signs, etc.) is fundamental to gaming dynamics. For O'Reilly, network effects occur when a product or service becomes more valuable as the number of people using it increases. This has a clear marketing value, since "network effects from user contributions are the key to market dominance in the Web 2.0 era" (O'Reilly, 2007: 24). Therefore the value of gaming activity increases through network effects built on architectures of participation. This brings the crowdsourcing trend into the discussion.

2.3 Crowdsourcing practices built on social networking and game dynamics

9Engagement of individuals who voluntarily offer their knowledge to a knowledge seeker (an organisation, a company, etc.) is termed "crowdsourcing" by Howe (2006). Crowdsourcing implies favouring the commitment of highly motivated users in the production process. Depending on the context it can be seen not only as a movement towards massive user engagement in an unrestricted and collaborative manner, but also as a means by which companies exploit users' collective efforts of knowledge building, without a corresponding remuneration, "[by] tap[ping] the latent talent of the crowd" (Howe, 2006, see also Anderson & Rainie, 2012, for a review). GM are instrumental in triggering engagement and in increasing motivation in non-game environments. The risk of crowdsourcing is often the exploitation of the results of users' efforts and a profit-making mechanism in favour of the knowledge seeker, or, in Howe's (2006) terms

the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call.

10Ethical considerations concerning crowdsourcing practices based on GM are discussed in Section 5.

3. Context of investigation

11The field of our investigation consists of web 2.0 language learning communities. These are communities explicitly designed for language learning and technically supported by web 2.0 technologies. They are sometimes referred as "Social Network Sites for language learning (SNSLL)" for their similarities with mainstream SNS (Lamy & Zourou, 2013).

12These can be broadly broken down into four types, according to a typology initiated by Loiseau, Potolia and Zourou (2011) and further elaborated by Zourou (2012): structured web 2.0 language learning communities (A), language exchange sites (B), marketplaces (C), and language correction sites (D). Letters A to D refer to the overview of GM by type of community presented in Tables 1 and 2.

13The first category, structured language learning communities, embraces communities such as Livemocha (2007), Busuu (2008) and Babbel (2008), where learning materials are accompanied by structured learning pathways. The second category, marketplaces (B), refers to communities in which learners can look for language tutors online and language tutors can offer their services for a fee (Italki is an example). In the third category, language exchange sites (C), users meet for language socialization purposes in loose ways without being supported by any language learning materials (e.g. Lingofriends (2007), Polyglot Club (nd)). The first three categories belong to the 2011 typology by Loiseau et al. (2011), and a fourth category is added for the purposes of the present study.

14This fourth category is language correction sites (D), where learners submit written texts and native speakers correct them. The language learning dimension is not explicit since the requests for correction of texts do not necessarily fall into the category of productions deriving from language learning materials. Lang-8 (2006) is a typical example of this type of community.

15There is a growing number of studies approaching web 2.0 language learning communities from various research angles (Dixhoorn et al., 2010; Gruba & Clark, 2013; Harrison & Thomas, 2009; Harrison, 2013; Liaw, 2011; Liu et al., 2013; Potolia & Zourou, 2013; Stevenson & Liu, 2010; Zourou & Loiseau, 2013), although such game-based activity as may occur within these communities has not been analysed so far. This is related to the rapidly changing nature of these communities, in which GBL is one of the most significant developments. Briefly, considerable changes have taken place at almost all levels, from language content available (more languages available and more partnerships with content providers), to design (with more and more web 2.0 features), registration possibilities (also related to free and paid access spaces), status of users (with new statuses, such as professional teacher in Livemocha, group leader in Busuu, etc.). The number of users has also increased dramatically, with the two biggest communities, Livemocha, launched in 2007, and Busuu, launched in 2008, having reached fifteen million and twelve million users respectively in 2013.

16Technical and pedagogical developments reflect the burgeoning activity in these communities. They also reflect changing business models, since each community is led by a web 2.0 start-up supported by private funds. In the search for a viable business model that is grounded on learners' activity in these spaces, many of the changes operated in the communities can be justified by the for-profit activity of these companies. Therefore, the terms "community" and "company" refer to a single reality, viewed from different angles.

17Regarding our terminology in respect of user roles, in some communities native speakers take the role of L2 tutors (as in tandem learning), therefore we use the term "tutor" to refer to native speakers who provide feedback to learners without necessarily being qualified professional L2 teachers. The term "teacher" is used for professional L2 teachers who provide their services for a fee in these communities.

3.1. Aim of the study and research questions

18This paper critically examines game dynamics facilitated by social networks in several types of web 2.0 language learning communities, by exploring their links to crowdsourcing in the analysis of its potential for language learning. The aim of the present study is twofold.

19Firstly to offer a state of the art review of game-based design in the sample communities, since there is no research into GM in non-game digital language learning settings that sheds light on user roles, expected attitudes and behaviours. Therefore, we start by identifying which GM can be found among web 2.0 language learning communities.

20Secondly, the paper aims to explore how companies are able to harness the ideals of social media supported GM to further their commercial interests. The research questions addressed by this paper are as follows.

  • What kinds of social network-supported GM can be found and which of them potentially benefit language learning?

  • How do GM empower crowdsourcing practices and what are the linkages between commercial and language learning imperatives?

3.2. Conceptual framework

21A learning design approach (Conole, in Lamy, 2013a: 147) is used as the conceptual framework for our study. An emphasis on learning design aims to improve the design and support of learning interventions and to encourage the adoption of a more principled, design-based approach to teaching and learning processes (Conole, 2010). A look at the design choices makes it possible to bring to the surface the technological and pedagogical assumptions of the designer, in this case of the developers of the companies supporting the web 2.0 language learning communities under scrutiny. By investigating gaming design choices we offer a critical appraisal (Lamy & Goodfellow, 2010) of the potential for Call.

3.3. Critical analysis of the data

22Two types of data are used. Firstly, GM implemented in all communities are scrutinized, illustrating the design choices of developers among several types of game-based incentivized models of activity. They are analyzed with regard to their value for user activity, the way they are displayed in a user profile and interlinked with one another. Secondly, we examine the official discourse of communities as it appears on the community website, as a demonstration of intentions and expected uses of GM.

23From a methodological point of view, GM are identified across the four types of community, resulting in an inventory of all GM available by type of community (Table 1) and are then critically addressed in Section 5 in relation to five topics: the role of "teaching", the role of feedback as part of gaming, competitiveness, gaming rewards and their role in social networking, and finally crowdsourcing. In our critical analysis of the data we proceed by comparing the learner behaviours induced by GM with learner behaviours deemed beneficial to language learning within the field of Call.

4. Types of gaming mechanisms

24We now describe the five main gaming mechanisms (henceforth GM 1, GM 2, etc.) found on the communities' websites. These are: scoring systems, badges, challenges, rating systems and leaderboards. At the end of the section Table 1 summarizes their characteristics and functions.

4.1. GM 1: Scoring systems

4.1.1. GM 1a: Learner scores

25This feature provides numerical information, sometimes transparently calculated, sometimes not, and is specific to structured communities. Scores are most often expressed as "points", which are displayed in the learners' profiles. Several activities qualify for learner points, with variations among communities that are discussed again in Section 5 regarding their significance in terms of user attitudes. In Babbel for example, a learner score is displayed for a user's overall activity and for the last seven days of activity. However, no information is given on how the scores are calculated as the algorithm for calculation of the total number of points is not revealed.

Figure 2 – Learner score in Babbel.

Figure 2 – Learner score in Babbel.

26In contrast to Babbel, in Busuu and Livemocha score calculation is provided and activities that are not exclusively learning activities are taken into account. For the calculation of the learner score as shown in Figure 3, Busuu points ("berries") are accumulated through learning, tutoring and community activities. This refers to the roles a user is expected to play as a community member since the design of the GM – which consists in collecting points – determines the value of each of the community tasks. It is worth highlighting that a learner can earn 500 points for having achieved his or her (self-defined) learner goal (item 8 in Figure 3), which is a high number of points compared to the earning mechanism in the same figure.

Figure 3 – Busuu berries.

Figure 3 – Busuu berries.

27Although the method of calculation of "berries" in Busuu is transparent, Livemocha does not show which of the actions listed count towards the total score.

Figure 4 – Learner (and teacher) points system in Livemocha.

Figure 4 – Learner (and teacher) points system in Livemocha.

 

4.1.2. GM 1b: Teacher scores

28Teacher score systems appear in structured communities. Various gaming mechanisms exist in these environments, such as points and tokens. Tokens are a common gaming mechanism similar to a currency exchanged between members. There are two ways for teachers to acquire tokens: by earning them through investment in the community (e.g. through frequent review of learner input) or by purchasing them. The feature is based on a freemium business model, a term combining the two aspects, "free" and "premium", that is a basic offer accessible to all, leading some users to subscribe to a full – paid – offer (Dilger, 2010). The usefulness of this bankable token system is advertised as follows to Livemocha users interested in being L1 tutors.

  • 2 http://www.livemocha.com/help#prices-1 (...)

You can buy individual learning activities with tokens, or you can unlock access to all premium material with Gold Key. We've started out all members with 200 tokens so you can try a few activities for free. If you run out of tokens, you can earn more by reviewing submissions or you can buy a bundle of tokens2.

29In language learning communities tokens are used to give access to extra services (such as feedback by an "expert reviewer") or to otherwise paid learning content.

30Teacher points are materialized in the same way as learner points (Mochapoints in Livemocha and Busuu Berries in Busuu). This, as discussed above, is designed to encourage tutorial activity parallel to learner activity. In Livemocha, the Teacher Score is displayed on a user's page (Figure 5) in the form of a score as well as a "percentage" of "helpfulness" for learners to whom feedback was addressed.

Figure 5 – Teacher score and explanation of degree of helpfulness.

Figure 5 – Teacher score and explanation of degree of helpfulness.

31For the Teacher Score in the case of Mochapoints, it is unclear how Livemocha's algorithm identifies discrete points and calculates the global teacher score. However an indication of ways to obtain a teacher score is provided.

  • 3 http://www.livemocha.com/help/reputation (...)

You increase your Teacher Score and Mochapoints when you:
Provide helpful submission reviews: get more points if you are first.
Help with Translations: get more points if you vote on them too.
Create useful Flashcards.
Contribute Featured Tips: add a featured tip in the Learn section of a lesson.
Earn positive ratings: contributions with positive ratings earn more points3.

32Livemocha drives the tutoring activity further through four additional incentivized features of the gaming design. Firstly, by linking re-investment of teacher points earned to access to free learning materials, as shown below.

  • 4 http://www.livemocha.com/help/reputation (...)

You can earn exercises for free with Teacher Score points4!

Earn 300 or more Teacher Score Mochapoints and get instant access to Review, Write, and Speak exercises for all 101-202 courses in any language!

33Secondly, by encouraging tutors to "invest" in quality feedback, on the grounds that in return they will score more points (although no information is given on the number of points tutors may expect to gain):

  • 5 Same website as above.

Contributions with positive ratings from other Livemocha members will boost your Teacher Score5.

34Thirdly, through the introduction of tokens that are awarded to tutors whenever they review a learner's production. Livemocha explains what to do with tokens:

  • 6 Same website as above.

Tokens are a virtual currency you can use to buy items on Livemocha. Tokens are a great option if you prefer to pay as you go. You can buy tokens and dole them out for chunks of content whenever inspiration strikes6.

35Finally, a tutor can earn extra tokens if he/she is the first to correct a learner's production.

Figure 6 – Tokens associated with tutoring activity.

Figure 6 – Tokens associated with tutoring activity.

 

4.2. GM 2: Badges

36Collecting badges is a common activity in mainstream gaming (Antin & Churchill, 2011). In our sample, badges appear in structured learning communities. Badges corresponding to a given number of points (bottom of Figure 7) may be collected through various user activities. In Livemocha, badges are prominently displayed on a user profile, in a box called "achievements" (top of Figure 7).

Figure 7 – Display of badges on a user profile (Livemocha).

Figure 7 – Display of badges on a user profile (Livemocha).

37Details of a person's badges can be accessed by clicking the "More" link (Figure 8). In Livemocha, badges are accumulated through learning and tutoring activity only, whereas they can be earned by other means elsewhere, as shown in our comments on Busuu below. The badges that a user can earn are attributed respectively for: being among the top 10 or top 100 learners of a given language, being among the top 10 or top 100 tutors of a given language, being the learner (or teacher) of the week (or of the month).

Figure 8 – Display of badges on a user profile (after clicking "More").

Figure 8 – Display of badges on a user profile (after clicking "More").

38In Busuu, a more elaborate system of badges is in operation (Figure 9). Various types of engagement in the community qualify for badges, such as language learning activities (e.g. completing a course or taking a test), tutoring services (e.g. being a helpful corrector), social activity (e.g. being leader of a group) or being a person with a vast network of friends (having more than 50 friends). Badges are designed to highlight the value of not only learning and tutoring, as in Livemocha, but also engagement in the life of the community.

Figure 9 – Busuu badges.

Figure 9 – Busuu badges.

39Finally, alone among the communities observed, Busuu offers learners the option of forming sub-communities following their own interests, which allows user engagement in the life of these sub-communities to be demonstrated, thus for badges to also recognize this type of activity.

4.3. GM 3: Challenges

  • 7 http://blog.busuu.com/free-language-learning-challenge/ (...)

40Found only in structured language learning communities, "challenges" refer to learning and gaming incentives whereby a user may put a challenge to someone they know. For example they may "challenge a friend and see if [they] can both complete 3 new units in 24 hours"7. If users win the challenge they collect an extra 50 Busuu Berries, and if they fail they lose 50. The notion of challenges among friends is also indirectly linked to Busuu's way of exploiting the points system. By "ranking" the Busuu Berries (or points), the company provides the user with a tool for keeping an eye on their own and their friends' achievements. According to the Busuu site the function of berry-ranking is

  • 8 http://www.busuu.com/enc/help#19262199 (...)

[to] show [the] number of busuu-berries compared to those of your friends [and it serves to] (…) compete with your friends and see who the most active student is8.

41Data about the reach of this gaming mechanism and the degree of appropriation by users is not currently available.

Figure 10 – Busuu challenges.

Figure 10 – Busuu challenges.

 

4.4. GM 4: Rating systems

42Unlike scores, rating systems are qualitative indicators. Four rating mechanisms are discussed: a) the learner input rating system, b) the tutorial feedback rating system, c) a rating system followed by a text box for written comments, and d) a system of tutorial feedback rating by native speakers (intra-native feedback).

4.4.1. GM 4a: Rating of learner input

43Rating of a learner's production in his or her profile appears in all four categories of communities. In Figure 11 below from Livemocha, the learners' productions are listed, followed by the course within which the input was produced, and by the number of tutor comments. The tutor's rating is displayed next to each production.

Figure 11 – Rating of a learner's submissions visible on his or her profile.

Figure 11 – Rating of a learner's submissions visible on his or her profile.

44In some environments this simple star-based rating mechanism is enriched with written comments on learner's submissions, as explained next.

4.4.2. GM 4b: Rating of a learner input followed by feedback

45As found in language correction sites and in structured communities the star-based rating system may be enriched with a box for comments by language tutors. In the following example from Lang-8 (Figure 12), the environment is designed in such a way that a tutor may:

  • rate the production with stars (one star in this example);

  • provide feedback (second line starting with a tick);

  • provide linguistic comments by opening a box (see the comment bubble icon) where tutors may enter extra hints or explanations.

Figure 12 – Rating and providing written feedback on learner production (Livemocha).

Figure 12 – Rating and providing written feedback on learner production (Livemocha).

46Similarly oral learner production may be rated and also commented in an oral format. The following screen capture from a structured community shows the spot where a tutor would find a learner's video submission, and the way he/she would provide oral feedback (by clicking the audio icon).

Figure 13 – Rating and providing oral feedback on learner production (Livemocha).

Figure 13 – Rating and providing oral feedback on learner production (Livemocha).

4.4.3. GM 4c: Rating of tutor feedback

47In all community types, once a learner has received a correction, he or she may rate the quality of the feedback. Each community has different methods for the learner to give feedback on the usefulness of the correction: in Livemocha there are three degrees of rating: "no rating", "helpful" and "extremely helpful", in Busuu appreciation takes the form of stars (zero to five). These evaluations appear on the tutors' profiles and serve not only to let them know how their feedback is valued, but also to showcase their correcting skills.

Figure 14 – Rating of tutor reviews by learners.

Figure 14 – Rating of tutor reviews by learners.

48In the case of marketplaces, for example Italki, professional teachers offer their services for a fee. The number of learners as well as the rating of language lessons and of speaking practice by learners are displayed in teacher profiles, publicly available as shown on the left in Figure 15.

Figure 15 – A professional tutor's public profile in Italki.

Figure 15 – A professional tutor's public profile in Italki.

49By clicking on "View" in the left hand box on "Tutor's page" above, a user has access to the following details (Figure 16) about the professional tutor.

Figure 16 – Details of language lessons, students, sessions by student and quality of lessons rated by learners.

Figure 16 – Details of language lessons, students, sessions by student and quality of lessons rated by learners.

 

4.4.4. GM 4d: Intra-native speaker feedback

50In language correction sites it is possible for native speakers to assess the quality of feedback provided by other native speakers. In Lang-8 this is achieved through "native nods", displayed on a user profile. This offers further enhancement of the collective dimension of feedback practices.

Figure 17 – Points by native speakers in Lang-8.

Figure 17 – Points by native speakers in Lang-8.

 

4.5. GM 5: Leaderboards

51Leaderboards serve to measure the degree of completion of a course and are publicly displayed on user profiles. They are found in structured language learning communities, because only in this type of community is language learning content structured as complete courses and learning scenarios (Loiseau et al., 2011).

We've designed a motivational framework with three principles in mind: goal setting and tracking, competition and peer pressure. Let us show you how. We're now on my home page. Livemocha courses are a trimester in length and so we can tell you which task you need to complete this week and also those you've fallen behind on. (…) The Leaderboard lists all the current members enrolled in Spanish 101 as well as the points they've earned by completing exercises. [W]e all know the power of peer pressure. With the "recent activity" tab, you're able to monitor the progress your friends are making in their language learning goals, the exercises they're completing. (transcribed by the authors from the video demonstration, from minute 4:56 to minute 5:47)

  • 9 http://www.youtube.com/watch?v=8rHfrENQBqc (...)

52Leaderboards are set within a "motivational" framework, as the Livemocha team explain in their presentation at DEMOfall 2007 in San Diego9:

Figure 18 – Leaderboard in Livemocha.

Figure 18 – Leaderboard in Livemocha.

 

4.6. An overview of game mechanisms

53Table 1 summarizes the GMs examined above. In the section that follows, we move on to a critical discussion of the ways in which these GMs work, by the end of which we will be in a position to complete the table with comments on the overt as well as the underlying functions of these mechanisms.

Table 1 – Overview of gaming mechanisms.

Table 1 – Overview of gaming mechanisms.

 

5. Discussion

54Out of the foregoing inventory of gaming mechanisms in the communities under study, we have identified five critical issues, discussed in this section. They are: the role of "teaching" (as an addition to "learning") in these communities, the role of feedback as part of gaming, competitiveness, gaming rewards and their role in social networking, and finally crowdsourcing.

5.1. The role of gaming in teaching and learning

55We saw that several of the gaming mechanisms (GMs 1b, 4c and 4d) support the promotion of teaching through practices similar to those that promote learning (GMs 1a, 4a and 4b). For example in both Livemocha and Busuu, users qualify for learner points if they are prepared to act as L1 tutors. The design of the learner point systems in Busuu and Livemocha reflects different approaches in the understanding and orchestration of user activity in the two communities: a more collective and competition-oriented approach in Busuu and, in Livemocha, an approach which is more individual, with the priority given to more incentivized activities such as L1 tutoring. In both cases the design embodies a clear interconnection between learning and tutoring as a continuum in these environments.

  • 10 www.livemocha.com/help/reputation (...)

56In Livemocha the traditionally different roles of learner and teacher are designed to be interlinked, which fuels social presence and community recognition for any learner willing to serve as teacher. These points are displayed in the score, which acts as a reputational hook, augmenting the prestige of these particular community members, thus inducing them to extend their participation (and encouraging others by example to initiate theirs). While the emphasis on teaching may at first sight be seen as inconsistent with the primary reason a user joins the community (i.e. to learn a language), it is a safe assumption that the larger a body of active L1 teachers a community can boast among its members, the more attractive it will be to learners. The parallel designs of scores and ratings for learning and teaching therefore serves the commercial interests of companies. In Livemocha, this economic underlay is made explicit: learners are rewarded not only through enhancements to their reputations but also to their purchasing power, since the points they earn through their reviews of other learners' input and through these learners' return feedback ("Contributions with positive ratings from other Livemocha members will boost your Teacher Score"10) are a means of access to otherwise pay-to-view L2 learning content ("Earn 300 or more Teacher Score Mochapoints and get instant access to Review, Write, and Speak exercises for all 101-202 courses in any language!").

57There is one respect in which the continuum "educational-commercial" could be viewed in a positive educational light. For example whilst in Call learning and teaching are generally not placed on an equal footing, in some settings equality of status between learning and teaching is key, such as tandem learning carried out outside of, or as adjunct to, formal learning programmes. Informal learning in social networking sites (SNS) has also recently laid claim to being an egalitarian format within which to learn, through studies pointing to learning gains realized entirely through student-directed activity (Lamy, 2013b; Lamy & Zourou, 2013). The companies under study here may have ideological aspirations matching such developments, as might be seen in encouragements to self-develop such as Livemocha's:

  • 11 http://www.livemocha.com/tutor/languages/index?source=SIHP_GetStarted (...)

By rating and correcting another community member's submission, you are ensuring a productive learning environment for you and other community members11.

58However, we note that they position themselves as "learning communities", a label which may be chosen to avoid discouraging users, because correcting learners is a time-consuming and not always creative activity, best not advertised upfront. This assumption derives some strength from the observation that in the organisations' marketing spaces (i.e. their websites) tutoring is presented as a secondary activity. Only after joining the community do users discover the importance that the companies accord to teaching, sometimes expressed via almost coercive procedures, such as in the following example from Rosetta Stone's Rworld Community (nd). On initial registration, learners are requested to indicate the language(s) they know and their level of proficiency per language (Figure 19: "Add the languages you know and rate your proficiency so that you can interact with others in games and activities"). If the user does not indicate languages he speaks, the following message appears in a red insert, which bars his/her access to the site unless he/she shows willingness to actively help others with his or her L1:

The Rworld Community is designed for conversation practice. If you want to increase your communication skills, Rosetta Stone offers language-learning solutions for you. If you are ready to have a conversation in your new language, you may participate in Rworld.

Figure 19 – Registration screen at RWorld.

Figure 19 – Registration screen at RWorld.

59Despite the fact that a learner may wish to play the role of L1 tutor, the design of user participation imposes user roles that support a crowdsourcing model of activity (social network based user activity as a value making process for companies).

5.2. Gaming and feedback to students

60In good educational practice, feedback is provided in two ways: quantitatively, within a transparent marking scheme, and qualitatively, in as individualized a manner as resources allow. In the present study, we saw that the provision of information on progress is ubiquitous, through quantitative mechanisms (GM 1: scores) and qualitative ones (GM 4: ratings). We now examine the extent to which each of these two aspects complies with good educational practice.

61As we saw in the previous section, the scoring, or quantitative feedback, offered in the communities is sometimes transparent (Busuu, Livemocha), sometimes opaque (Babbel) and sometimes semi-transparent (see Livemocha in Figure 2, where "percentages" are provided but not fully explained). Scores in some systems (Babbel) sanction performance on learning activities whilst in others (Busuu, Livemocha) the score is a cumulation of diverse types of input, such as learning and teaching but also other community-building activities. The quality and consistency of quantitative feedback mechanisms is thus variable across the field, which in itself is not unlike the situation across traditional educational settings, where good practice is in evidence to varying degrees. However there is a big difference in privacy in the use of scores in conventional schools and in the online organisations under study. Whilst formal learning institutions devote considerable resources to ensure the confidentiality of scores, in online learning communities scores are public, and their public nature is integral to motivation through the link between visibility in social networks and gaming mechanisms based on reputation and competition. Additionally, any Internet user, whether a community member or not, can navigate through user profiles and scroll user activity in an unrestricted manner. Visibility of data (including data of one's friends) is a condition for registration in these communities, possibly deliberately designed to pull into the communities new users attracted by the social network based game dynamics.

62The qualitative feedback is made up of comments and/or corrections and is therefore necessarily transparent. The feedback provider also has to accept transparency: for example a teacher offering services for a fee and seeking to find customers among the users of Livemocha or Italki has to agree to his/her professional background and profile being made public (Figure 15), and also agrees to a public display of the number of users s/he has taught, the number of sessions s/he delivers, the identities of those s/he taught, and the ratings awarded by his/her students, as shown with Italki (Figure 16). Qualitative feedback provided to learners and displayed on tutor profiles is linked to transparency, as well as to accountability. This also happens in relation to intra-native speaker feedback (GM4d). As Potolia and Zourou (2013) argue transparency and accountability built through GM with a qualitative dimension (GM4) set the foundations for a more collective and trusted relationship that could counter-balance the lack of physical presence as a prerequisite for a trusted relation online (Harrison, 2013 ; Liu et al., 2013).

63Moving on to individualization, clearly in these environments qualitative feedback is designed to be individual: one professional teacher or one expert peer (that is, a native speaker), deals with one learner. Yet there is a collective effect: through the transparent mechanisms discussed above, learners have the power to assess the feedback they are offered. Thus the companies' strategy of using crowdsourcing to augment – for free – the volume of professional and expert input displayed on its pages benefits both the individual learner and the community. This does not guarantee quality, since both types of helpers may fail to provide the most pedagogically valuable responses: the professional teacher because he/she lacks a long-term overview of the learner's development, and the native speaker because he/she may not have the required reflective qualities needed for clarity of teaching. In what sense, then, might gaming mechanisms support learning through feedback? While the feedback mechanisms in the communities undoubtedly carry risks to learning (lack of transparency to learners about their scores, inexpert interventions, linguistic inaccuracies), game-based feedback mechanisms may nevertheless have value in that they stimulate both the production and the consultation of feedback, allowing for faster and more frequent contact between the learner and his/her own linguistic development, which in turn may turn the learner into a more committed community member and a more active learner. Verification of these hypotheses lies with longitudinal proficiency studies beyond the scope of this article.

5.3. Gaming and competitiveness

64Competitiveness pervades the GM across the communities, with structured language learning communities being those that embed the most competition-oriented GM such as GM1 and in particular GM3 and GM5 (see Table 1). This can be explained by the fact that the underlying business model of this type of community is based on uninterrupted user activity and engagement. Incentives to compete sustain user activity, while serving crowdsourcing practice.

65For example, we saw that tutors are rewarded with more tokens if they are "the first" to produce review input (section 4.1.2.). Some of the mechanisms such as challenges and leaderboards (GM3 and GM5) are explicitly designed to urge users to compete, mainly with their "friends". Sherry and Lucas (2006: 219) study of video gaming showed that "[p]layers enjoy the challenge of 'beating the game', but also of beating friends. For many, it is not enough to win the game; one's exploits must be known amongst one's friends". As with badges, then, the mechanism relies on a combination of motivators. In the case of challenges and leaderboards, these are competition ("winning", "beating") but also reputation ("exploits (…) known") and sociability (friends).

66One point to note in our context is the notion, expressed in the discourse of Busuu challenges and Livemocha leaderboards, that one's language learner competitors are one's friends, in other words that users of the communities share not just the fact of being registered with the same online organization, but have formed affective ties. Here again a characteristic of social networking (befriending) neatly marries up with a commercial advantage (presenting the organization as a sympathetic one where affective ties are fostered).

5.4. Gaming rewards and social networking

67One mechanism with a clear affiliation to gaming is GM2: badges. Very few analyses of badging mechanisms are available, which makes Antin and Churchill's (2011) short paper particularly interesting. They explain that "[a]warding badges has also become a key ingredient in 'gamifying' online social media experiences", citing "Wikipedia's Barnstars which allow users to award each other for doing valued work", as well as the Q&A site StackOverflow's system of badges that encourage productive participation, and Foursquare's implementation of badges to promote location-sharing via "check-ins" (Antin & Churchill, 2011: 2). They see badges as fulfilling five social psychological functions in social media contexts: goal setting, instruction, reputation, status/affirmation, and group identification. In the communities that we are studying, these five elements are in co-presence to various degrees and in synergy. For example "being among the top xx learners/teachers" of a language is a function that illustrates the interplay between Antin and Churchill's notions of reputation, affirmation and group identification. As they explain (2011: 3).

Badges assist reputation assessments at several levels. At a general level, examining another user's badges can provide a summary of interests and engagement levels, for example by indicating whether a user is a casual or fanatical community member. Like Boy Scout merit badges, in social media contexts badges can also provide information about a user's skill-set and expertise. By providing an encapsulated assessment of engagement, experience, and expertise, badges can be an invaluable tool for determining the trustworthiness of other people.

68Badges can be seen as an emblematic rewarding system for two reasons. Firstly, badges are more highly valued than points since they are built on the latter (bottom of Figure 7). Secondly the importance of badges in the game-based learning design of these communities is reflected in the sophisticated earning mechanisms put in place (Figures 7-9).

69In our context, the issue is trusting other community members to help further one's own language goals. For the clarity of the illustration that follows, we have numbered the Busuu badges in Figure 9 from 1 to 23. Of the 23 categories of Busuu badges, 9 refer to learning (badges number 11-19), 8 refer to tutoring activity (badges number 1-4 and 19-23), and 6 to community engagement (badges number 5-10). Thus, 39% of badges can be earned through learning activity, 34.7% through tutoring and 26% through community activity. This means that approximately one third of the badges can be earned through engagement in learning activities, the primary purpose of the community, while almost two thirds (60.7%) are earned through activity not related to learning. This indicates i) an emphasis on activities other than learning that are valued through badges and ii) the crowdsourcing practices widespread in the communities (see also next section).

70As is the case with the encouragements to teach discussed above, reward systems lead to active user engagement that has commercial benefits for the community owners, since the more engaged the users, the more sustainable the community. A similar conclusion was reached by Zourou and Loiseau (2013) who investigated factors favouring greater engagement in Livemocha's Culture space and concluded that the available social networking mechanisms were designed to sustain interaction and foster content sharing especially with newcomers. The similarities in the results of that study and the current one highlight the bi-directional relationship between social networking and GMs as driving user activity in these communities. It is further increased by location-based networking mechanisms like those elaborated by the language exchange site Polyglot Club with face-to-face "Meetings" (Polyglot Meetings, nd) instigated by the site but left to the initiative of learners to organize.

5.5. Gaming as a crowdsourcing mechanism

71Earlier we saw how crowdsourcing operated in the provision of feedback. Similarly, the practices reflected through the badge systems, show that value is ascribed to crowdsourcing, that is to engagement in services, such as translation of materials (Figure 9, badge number 9) or tutoring activities (Figure 8, badges number 1-4 and 19-22), in keeping with the importance that community developers give to user engagement as a vehicle for increased activity in the community. Crowdsourcing, a notion that is in some cases considered as a "generous" ideal that enhances social action" (boyd et al., 2010), plays out in some of these companies in ways that owe nothing to generosity and everything to industrial exploitation. One extreme example is Lang-8's "Premium User" system. One advantage that their paying customer status brings to Premium Users is the ability to "emphasize" their own journals on others' screens (bottom of Figure 20).

Figure 20 – Lang-8's Premium Users system.

Figure 20 – Lang-8's Premium Users system.

72Thus these journals are noticed and reviewed/corrected as a priority by native speakers who do so for a reward of teacher points but no fee. This also happens in Livemocha where instructions to the reviewer make it clear that the grading is for a paying customer, and are commanding in tone:

Note to Reviewers: This exercise is a Premium exercise called Role Play (…). Please watch the video and comment on the user's speech and pronunciation.

73In this economic triangle (company, customer, reviewer) the customer pays the company in order to profit from the time and skill invested by the reviewer, who receives very little if any part of the financial benefit. Worse, if tutors wish to exchange, for cash, the tokens they earned, they will have to part with a large number of tokens, which means that they will have to have previously invested a considerable amount of time in feedback provision.

6. Concluding remarks

6.1. Limitations

74There are two limitations in the approach undertaken in our paper. Firstly, the article does not provide evidence about learning outcomes related to the use of game mechanisms. Research into learning outcomes traditionally requires an experimental set-up, in which groups might be tested before and after a period of time learning with social networks. However all online language communities use GMs, making it difficult to accept that alternative settings such as tandem or telecollaboration could justifiably provide a basis for comparison.

75Secondly, we acknowledge the limits of carrying out our study without including methods to evidence the intentions of the designers of these game mechanisms, or indeed of the company owners. One suggestion for future research and development activity would be for closer collaboration between researchers and developers on gamification for participatory language learning (Zourou, 2013). We are aware of the implications in terms of resources and working cultures within faculties and schools, yet it seems to us that the very mechanisms of crowdsourcing reputed to be beneficial for users could be usefully harnessed in this way by researchers to help create more learner-centred gaming environments (Zourou, 2013).

6.2. Synthesis

76Our close examination of GMs has shown how the gamification in web 2.0 language learning communities promotes their commercial interests at the same time as it supports the users' learning interests (or, for teachers, their professional development). Indeed in more than one of these GMs, a mutually reinforcing mechanism has been shown to be at work between commercial and socio-educational benefits. We revisit Table 1 (see earlier) and offer a synthesis.

Table 2 – Synthesis of gaming mechanisms.

Gaming mechanisms

GM 1 and GM 3: scores and ratings

GM2, GM3 and GM5: badges, challenges and leaderboards

Potential language learning benefits

Egalitarian learning atmosphere, individualized feedback (frequent and fast), individual progress charting,

Emphasis on social learning, crowdsourcing, motivational (for those learners who enjoy competing)

Tie-in with commercial interests

Directly: provision of purchasing/bartering mechanisms

Indirectly: increased activity and counter-activity from all users of the site, leading to more purchasing as well as reputation enhancements and more registrations

Indirectly: increased activity and counter-activity from all users of the site, leading to more purchasing as well as reputation enhancements and more registrations

6.3. Conclusion

77Although our findings (summarized in 6.1. above) shed some light on the value of networked gaming activity in web 2.0 language learning communities and on the relationship between commercial and socio-educational benefits, several issues that challenge current educational practice remain open and call for further investigation. Does gaming activity in an informal learning context impact learning, engagement, recognition and collaboration practice in formal learning contexts? What are the implications of visibility and exposure of practice of a professional teacher for the teaching activity? How will location-based, online networked gaming activity in informal learning settings affect formal face-to-face and online settings?

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Références

Les liens externes étaient valides à la date de publication.

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Table des illustrations

Titre Figure 1 – Personal, friend and group motivators leveraged by game designers (Zichermann & Cunningham, 2011: 145).
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Titre Figure 2 – Learner score in Babbel.
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Titre Figure 3 – Busuu berries.
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Titre Figure 4 – Learner (and teacher) points system in Livemocha.
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Titre Figure 5 – Teacher score and explanation of degree of helpfulness.
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Titre Figure 6 – Tokens associated with tutoring activity.
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Titre Figure 7 – Display of badges on a user profile (Livemocha).
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Titre Figure 8 – Display of badges on a user profile (after clicking "More").
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Titre Figure 9 – Busuu badges.
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Titre Figure 10 – Busuu challenges.
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Titre Figure 11 – Rating of a learner's submissions visible on his or her profile.
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Titre Figure 12 – Rating and providing written feedback on learner production (Livemocha).
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Titre Figure 13 – Rating and providing oral feedback on learner production (Livemocha).
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Titre Figure 14 – Rating of tutor reviews by learners.
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Titre Figure 15 – A professional tutor's public profile in Italki.
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Titre Figure 16 – Details of language lessons, students, sessions by student and quality of lessons rated by learners.
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Titre Figure 17 – Points by native speakers in Lang-8.
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Titre Figure 18 – Leaderboard in Livemocha.
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Titre Table 1 – Overview of gaming mechanisms.
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Titre Figure 19 – Registration screen at RWorld.
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Titre Figure 20 – Lang-8's Premium Users system.
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Référence

Katerina Zourou et Marie-Noëlle Lamy, « Social networked game dynamics in web 2.0 language learning communities », Alsic [En ligne], Vol. 16 | 2013, mis en ligne le 28 février 2013, Consulté le 20 avril 2014. URL : http://alsic.revues.org/2642 ; DOI : 10.4000/alsic.2642

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Auteurs

Katerina Zourou

Katerina Zourou is a Senior Researcher at the Sør-Trøndelag University College, Tronheim, Norway on network-based peer learning systems. She is the initiator and project leader of three European projects, the LangOER network "Enhance teaching and learning of less used languages through OER" (2014-2016), the "Language learning and social media" network (2010-2012) and the "Improving Internet strategies and maximizing social media presence of LLP projects" project (2012-2013).
Affiliation: Sør-Trøndelag University College, Tronheim, NO.
Email: katerinazourou@gmail.com
Web: http://www.histproject.no/node/837
Address: Faculty of Technology, Sør-Trøndelag University College, E. C. Dahls gt. 2, 7004 Trondheim, NO.

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Marie-Noëlle Lamy

Marie-Noëlle Lamy is Professor of Distance Language Learning at the Open University. She established a distance pedagogy for FFL via group tutorials on audiographic platforms in the 2000s, later rolled on to six other languages. Her research focuses on the semiotic and sociocultural dimensions of technological mediation.
Affiliation: Open University, GB.
Email: Marie-Noelle.Lamy@open.ac.uk
Web: http://lamymn.wordpress.com/
Address: Faculty of Education and Language Studies, The Open University, Milton Keynes, MK76AA, GB.

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