AbstractLANGA is a game-based platform for second language learning. LANGA is first of its kind in that it blends a research-driven approach to teaching with advanced animation to make learning fun and engaging. Here, we present a detailed description of the architecure of LANGA and empirical evidence for its effectiveness.IntroductionThe technological revolution is paving new ways to deliver education, holding the promise to make it more accessible and to be centered around the strengths and weaknesses of each individual user. In the field of second language (L2)  learning, computer and smartphone-based applications are now gaining momentum as a form of support — or as an alternative — to more conventional ways of L2 teaching such as periods of immersion or classroom-based learning. These technologies — which we refer to as L2 apps — are extremely popular; for example as of March, 2016 the L2 app Duolingo reported having 100 million users, and being the most-downloaded educational app for the iOS platform ({}) . Many L2 apps feature recent technological advances such as animation, and use game mechanics to make interaction fun and engaging. However, these products differ widely in terms of teaching philosophy, as reflected in how the material is delivered and how the learner interacts with the software. Despite the growing popularity of these applications there are still many unresolved issues concerning their theoretical foundations and, most importantly, their efficacy — issues shared with related domains, such as educational apps, cognitive training, and rehabilitation (e.g., Hirsh-Pasek 2015, Simons 2016; also {} and {}). First of all, products in the education market are not always designed and validated using scientific evidence; further, there are no accepted standards for empirical evidence of efficacy as there are for drugs or medical devices (Better reading throug..., Goswami 2006). In fact, in most cases there are no empirical studies available to back up the claims made by the developers of educational apps Hirsh-Pasek 2015. Thus even when research (either independent or conducted in-house by developers) is available, the extent of this may not be sufficient to fully address questions of efficacy. We are not aware of any effectiveness study published in a peer-reviewed journal.This is not to say that available L2 apps are not supported by empirical evidence, as many report in-house research and some even make research reports available on their Web sites (e.g., {}). Moreover, use of many L2 apps suggests that they are at least partially based on principles shown by previous empirical research to be effective in L2 teaching (e.g., spaced repetition; Pimsleur 1967). For example, Vesselinov R.Vesselinov 2009, R.Vesselinov 2012, tested the effectiveness of two popular L2 apps (RosettaStone and Duolingo) in teaching basic knowledge of Spanish. In one study R.Vesselinov 2009, participants used the software for a total of 55 hours, a period that is equivalent to the amount of time a typical semester-long beginner’s class would last. Although the study reported statistically significant improvements in proficiency at the group level, by-subject analysis of  performance showed no change for 36% of users, suggesting high individual variability in learning outcomes using the app. In a second study, data were collected from online users whose total usage Notably, the report did not provide any indication about the training intensity, i.e. how many days/week and hours/day subjects were expected to use the software. This makes interpretation of results difficult, since intensity is a strong predictor for rate of learning and retention of knowledge. In this study, the primary outcome measure was the change in spoken proficiency from pre- to post-training. Although the study reported statistically significant improvements in proficiency at the group level, by-subject analysis of  performance showed no change for 36% of users.A likely explanation for this result is that individuals’ ability to learn a new language is influenced by a multitude of factors (citation needed). For example, people receiving the same training and amount of exposure to a given language might differ significantly in how fast they learn, how long they retain the material, how well they respond to a specific type of training etc. Cognitive neuroscience research has provided great contributions in support of the view that individual differences (IDs) in socioeconomic status, level of education, age, lifestyle, motivation, engagement with the task (citations needed), and cognitive skills (i.e. working memory (WM) span, executive attention, verbal memory etc.) (citations needed) predict the extent to which people improve as a result of training. Notably, some of the above-mentioned factors such as motivation were not controlled for in the study conducted by Vesselinov R.Vesselinov 2009. While any single study cannot account for all possible variables, interpretation of the scant data available  is difficult since it is not clear to what extent the changes in performance observed in a subgroup of subjects are attributable to the use of the software, or simply to participants’ motivation to learn a second language. The latter hypothesis is supported by results of the second study R.Vesselinov 2012  conducted by the same researcher, testing the effectiveness of Duolingo. In this study data were collected through the online accounts of a pool of Duolingo clients who gave consent to participate. Although in this case the time spent using the software between pre- and post-training was not the same among participants, it was estimated that using Duolingo for a period of time between 26 and 49 hours would be sufficient to observe statistically significant gains, and that motivation and baseline level of Spanish proficiency predicted between-subjects differences in change in performance. Collectively, the studies R.Vesselinov 2012, R.Vesselinov 2009 are of particular relevance considering that they currently represent the only instances of empirical investigation of computer-based second-language training platforms. Nonetheless, their limitations point to the importance of a careful study design in terms of choosing appropriate control groups and measures of a comprehensive set of factors to adequately parse out the effect of training from other confounding variables. Furthermore, one of the ultimate goals of research in second language acquisition is to develop accurate models of how IDs in social and cognitive factors predict optimal ways to learn a second language for each individual in terms of training strategy, intensity, type of content etc.. This would allow us to move from the traditional “one-size fits all” approach to a personalized one that optimally adapts to the unique strengths and weaknesses of each individual. Such an approach requires large-scale studies (hundreds of subjects) involving people with the most diverse background and cognitive skills, two conditions that are hardly met by traditional lab-based research for obvious reasons. Instead, web-based applications can be a game-changer and represent an ideal solution to this problem since they can reach out virtually everyone with an internet connection with no cost, removing physical barriers due to transportation and speeding-up the research process.These considerations have largely inspired our approach to the design of LANGA (LANguage GAming), a language learning platform founded on five principles:Use of compelling video-games that make training fun and engagingUse of an advanced SRE system to provide real-time feedback about pronunciationServe as a powerful research tool that, operating through the web, can reach a vast and heterogenous population that cannot be accessed with traditional lab-based experimentsCollect behavioral and neurophysiological data, at every stage of learning, in order to systematically test the effectiveness of current versions and inform the design of the next generation of training platformsAllow researchers to easily manipulate the main parameters of the training tasks in order to quickly test specific hypothesis about efficacy.LANGA is the result of a collaboration between our laboratory and a private company, Copernicus Studios Inc.. We have thus combined the strengths of our lab in empirical behavioural and cognitive neuroscientific work on language processing with the company’s strengths in animation and game design. Our goal is to produce a language learning software platform that is effective and enjoyable for learners, but that also houses a flexible “back end” that will allow teachers and researchers to customize the learning process for groups of people, and empirically test hypotheses concerning language learning to generate better understanding of the mechanisms leading to successful L2 acquisition. In the remainder of this paper we discuss the design principles and architecture of LANGA, and provide details of one example study used to assess the efficacy of some games implemented on the platform.The architectureIn this section we describe the high-level organization of LANGA. Details about the specific components (i.e. the grammars, games, lessons etc.) and how they relate to each other are presented in the  following subsections. The tasks executed by the user are made of three main building blocks: content, games and curricula. Content refers to the material being taught in the new language – including vocabulary, sounds, and grammatical structures as well as the associated media (artwork and sound files). The games define, intuitively, the set of rules  through which the user interacts with the content in any given trial. The curricula are the higher-order mesh that connect games and content – including the selection of what content to teach in what order, the combination and sequencing of vocabulary, pronunciation, and grammar training, and the duration and frequency of training sessions (dose). As mentioned before, one of the aims of LANGA is to allow researchers with little experience with software engineering to easily  test novel hypotheses. To do that, researchers must be able to quickly build and deploy new content and curricula and to be able to visualize data analytics performed on the behavioral and neurophysiological data to validate their hypothesis. The Learning Management System (LMS) accomplishes both these goals through its main components: the Authoring Tool and the User Database. The Authoring Tool allows researchers to build the content (i.e. vocabulary, grammars etc.), implement different training strategies, assemble these components into lessons and, finally, implement cognitive tests needed to analyze subjects’ background profile. On the other end, the User Database stores all the information required to research purposes such as the scores on the cognitive tests and online information gathered from subjects’ performance (i.e. accuracy in pronouncing specific words/sentences, session by session improvement, number of attempts to pronounce a given item etc.).  GrammarsThe grammar is the functional unit of training. Briefly,  grammars define the set of words or sentences that are taught in any given instance of a game. Grammars are always made of four words/sentences. Within the grammar there is a picture for each word, the correspondent written translation and sound file.  Every time the user is prompted to name a word from a given grammar, the SRE matches the sound file recorded through a microphone and compares it with the sound files of the words belonging to that grammar. A matching score is produced for each comparison and, in case the highest one overcome a pre-established threshold, the attempt to name the word is given positive feedback. The threshold can be set for each individual word based on how difficult they are to pronounce, allowing minimization of both false positives and negatives.Training strategiesCurrently, we have implemented and tested the effectiveness of two different strategies for teaching vocabulary. One strategy, referred as “rote” training, is a stimulus-paired associative task typically used both in classroom settings or by other computer-based programs. Simply, in this task each word to be learned (nouns and verbs) is represented by a  picture and paired with its spoken form (see Comparison of performance in the match-mismatch task across different stages of training. Barplots represent mean and 95% CI of the accuracy scores ., left panel). The other strategy, the “inferential” training consists of pairing a picture depicting an actor performing an action on an object  with the corresponding Spanish written and spoken three-word sentence with a subject-verb-object (SVO) structure (e.g.,  La bruja abate el basurero —- “The witch knocks over the garbage can”). The learner is required to guess the meaning of the individual words in the sentence based on the picture, prior knowledge, and the SVO syntax; in a series of training items each picture/sentence varies from the last only in one word (i.e., one of the nous or the verb). This aids the learner in making inferences as to word meanings, and “bootstrapping” later learning based on newly-acquired knowledge about the language. Some prior research has shown that such “implicit” training for L2 grammar can lead to better long-term retention and patterns of brain activity that more closely resemble those of native speakers Morgan-Short 2012, Morgan-Short 2012.

PARAMETERS Counts During the time course of invasion by _Salmonella_, a bacterium can be in 4 different generalized stages: swimming (looking to attach to a host) B, attached to a host cell Ba, invaded and vacuolar Bv, or invaded and cytosolic Bc. The counts of bacteria in these stages are constantly changing in time, but ignoring xenophagy and replication, the total number will remain constant: B_{\rm tot} = B(t) + B_a(t) + B_v(t) + B_c(t) We classify the relationship between host cells and bacteria in one of three ways: host cells with no bacteria H, with attached bacteria Ha, with invaded vacuolar bacteria Hv, and with invaded cytosolic bacteria Hc. A host cell may have a combination of attached, vacuolar and cytosolic bacteria at one time, so the total number of host cells is the the length of the union of these sets: H_{\rm tot} = \left \cup \left\{H_a (t) \right\} \cup \left\{H_v (t) \right\} \cup \left\{H_c (t) \right\} }\right\vert We also define infected host cells Hx as any with internalized bacteria (vacuolar or cytosolic): H_{x} = \left \cup \left\{H_c (t) \right\} }\right\vert A well-studied feature of invasion by _Salmonella_ is the formation of epithelial cell membrane ruffles, triggered by effectors secreted via the bacterium’s type III secretion system 1 (TTSS-1). We call the total number of ruffles R(t), and the number of host cells with at least one ruffle Hr(t). An important and controllable quantity when performing invasion assays is the ratio of bacteria to host cells at inoculation. This is called the multiplicity of infection (MOI) and in terms of previously defined parameters, $m = B_{\rm tot} / H_{\rm tot}$. Another is the confluency, which is an estimate of the proportion of area covered by a monolayer of cells in a culture dish. In terms of average host cell area A and side length of the square well L, we calculate confluency as $c = H_{\rm tot} A / L^2$. A similar, although time-dependent quantity, is the bacterial density per unit area ρB(t)=B(t)/L². More specifically, this is the density of bacteria have yet to attach to a host. Fractions Since this is a mean field model, it is more useful to speak in terms of fractions or percentages, rather than discrete counts. h(t)≡ fraction of host cells without attached and/or invaded bacteria $ = {H_{\rm tot}}$ ha(t)≡ fraction of host cells with attached bacteria $ = {H_{\rm tot}}$ hx(t)≡ fraction of host cells with invaded bacteria $ = {H_{\rm tot}}$ hv(t)≡ fraction of host cells with invaded vacuolar bacteria $ = {H_{\rm tot}}$ hc(t)≡ fraction of host cells with invaded cytosolic bacteria $ = {H_{\rm tot}}$ hr(t)≡ fraction of host cells with ruffles $ = {H_{\rm tot}}$ b(t)≡ fraction of bacteria that are available for attachment $ = {B_{\rm tot}} $ ba(t)≡ fraction of bacteria that have attached to a host cell $ = {B_{\rm tot}}$ bx(t)≡ fraction of bacteria that have invaded a host cell =bc + bv  bv(t)≡ fraction of vacuolar bacteria $ = {B_{\rm tot}} $  bc(t)≡ fraction of cytosolic bacteria $ = {B_{\rm tot}} $ $ (t) \equiv $ ruffles per host cell $ = {H_r} = {h_r H_{\rm tot}}$ $_a \equiv $ bacteria attached per host cell $ = {H_a} = m {h_a}$ $_x \equiv $ bacteria invaded per host cell $ = {H_v} = m {h_v}$ Rates To understand how these quantities evolve in time, we must define the rates which describe the various stochastic processes of bacterial invasion. A swimming bacterium can either attach to a host cell normally, or be recruited to a membrane ruffle. Γa≡ primary attachment rate per bacterial density, per host cell Γb≡ ruffle recruitment rate per bacterial density, per ruffle Once attached, a bacterium can cause ruffling, invade the host cell and stay vacuolar, or invade the host cell and escape early into the cytosol. Γr≡ ruffle formation rate per attached bacteria, per host cell Γv≡ vacuolar invasion rate per attached bacteria, per host cell Γc≡ cytosolic invasion rate per attached bacteria, per host cell  Γx≡ combined invasion rate per attached bacteria, per host cell =Γv + Γc There is evidence that suggests a physical limit to the number of ruffles per cell $_{\rm max}$, and the number of invaded bacteria per cell $_{x, \rm max}$, so we define the following effective rates. Γr*(t)≡ effective (limited) ruffle formation rate per attached bacteria $ = \Gamma_r (1 - (t)}{_{}})$ Γx*(t)≡ effective (limited) invasion rate per attached bacteria $ = \Gamma_x (1 - (t)}{_{x, }})$

PARAMETERS Counts N≡ number of host cells NI≡ number of infected cells NB≡ number of bacteria NR≡ number of ruffles Nr≡ number or ruffling cells (≥1 ruffles) tmax≡ total incubation time Fractions m≡ multiplicity of infection (MOI) $ = {N}$ c≡ confluency $ = {L^2}$  a≡ mean cellular area  L≡ side length of square well x≡ fraction of host cells infected $ = {N}$ b≡ fraction of bacteria remaining (i.e. not landed on a host) $ = {N_B (0)}$ f≡ fraction of attached bacteria that form ruffles r≡ fraction of host cells with ruffling (≥ 1 ruffle) $ \equiv $ ruffles per cell $ = {N}$ $_R \equiv $ bacteria per ruffle $\quad _R(t=0) = 1$ Rates Γ₀≡ primary attachment rate per bacterial density Γ₁≡ ruffle recruitment rate per bacterial density