Gamified Language Learning and Neoliberal Ideology: A Multimodal Critical Discourse Analysis of a Popular English Vocabulary Application

Summary

This study examines the impact of gamification on the motivation of Chinese university students in mobile-assisted English vocabulary learning. Critical Discourse Analysis reveals that, although gamified elements temporarily increase engagement, they also promote competitive and self-monitoring norms that may erode intrinsic motivation and impact students' psychological and sociotechnical experiences.

Abstract

The integration of gamification in mobile-assisted language learning (MALL) has reshaped how learners can engage with vocabulary acquisition and self-directed communication. Few studies have examined the impact of gamified applications on learner behavior and psychological reactions, particularly in non-native English learning contexts, despite their frequent commendation for enhancing user motivation and engagement. This study examines the motivational impact and ideological underpinnings of popular gamified English vocabulary apps, using Fairclough's three-dimensional Critical Discourse Analysis (CDA) framework to analyze both user interactions and platform design. Drawing on interface data, user sentiment analysis, and interview transcripts from the Chinese university students studying English as a foreign language (EFL), this study reveals that, consistent with Self-Determination Theory (SDT), gamification plays a significant role in encouraging learner motivation and engagement but simultaneously embeds neoliberal ideals of competition, self-optimization, and behavioral monitoring. These features, while intended to boost performance, may undermine intrinsic motivation over time. The findings highlight the psychological and sociotechnical dynamics of gamified learning environments, offering new insight into how digital educational platforms can reinforce self-regulatory expectations and performance-oriented discourse.

Introduction

The process of acquiring a language is commonly regarded as socially mediated and interactive, incorporating communicative interaction, output, and input in particular sociocultural situations. Second language acquisition (SLA) foundational research highlights that opportunities for engagement, feedback, and self-regulated practice are just as important in helping learners become proficient as exposure to linguistic input. This framework allows learners to participate in scaffolder feedback loops and authentic communication through the use of digital and mobile-assisted learning environments¹. The incorporation of gamification into these settings has further altered how students maintain motivation, track their development, and develop self-directed language practices.

Language learners can acquire a targeted foreign language more effectively if they have a comprehensive vocabulary, as it helps them understand what they are hearing or reading in the language. Grammar can be used to communicate data, but without language, conversation cannot be finished. However, many EFL students still struggle with vocabulary acquisition, which they perceive as a significant barrier to improving their overall English proficiency. Because acquiring complex vocabulary -- including grammar, spelling, meaning, connotation, collocation, and word structure -- requires consistent input, the cognitive load often motivates students to seek more engaging learning methods².

The effectiveness of vocabulary learning is constrained by the conventional paper-based learning methodology. As technological education advances, an increasing number of gamified English vocabulary learning applications have surfaced with the goal of increasing student motivation and effectiveness. According to Li et al.², gamification is the process of modifying people's usage patterns or behaviors by incorporating game mechanics and elements into non-gaming settings. Game-inspired applications are becoming increasingly common among EFL learners and attracting the interest of academic researchers due to their ability to combine the features of instructional gamification and mobile-assisted language learning (MALL) in an environment of vocabulary development.

Over the past 20 years, educational technology (EdTech) solutions have become more widely available and used in the classroom worldwide³. A new reliance on technology to close the learning gap has emerged in an era where research and consecutive annual reports⁶have been revealing a decline in learning levels among Indian children⁷. The variety and complexities of modern society have made higher education essential, and as a result, the body of scientific research devoted to forecasting educational achievement or the likelihood of student dropouts has grown rapidly^{8,9,10,11,12,13}. The traditional function of higher education institutions in disseminating knowledge has evolved; creativity in new knowledge, particularly with the rise of artificial intelligence¹⁴, and the training of skilled professionals have led to many of them interacting with various facets of society. Actually, the main goals of its academic and cultural activities are to raise the standard of knowledge within the community through instruction, investigation, and the capacity for sharing and transferring such information.

TAM has become a prominent paradigm in recent years for understanding the adoption of technology in a variety of domains, including social media¹⁵, online banking¹⁶, healthcare technologies¹⁷, and education. Numerous scholars have examined how instructors and students utilize e-learning resources or platforms under the theoretical framework of TAM. For instance, in light of the COVID-19 pandemic, Mustafa et al.¹⁸ examined the variables influencing e-learning outcomes. Students' acceptance of YouTube-based learning resources¹⁹ was also investigated. Rafique et al.²⁰ examined users' behavioral intentions for utilizing the e-book format and provided an explanation for why instructors and students enjoy or dislike online instruction¹⁷. Additionally, research has been done on users' adoption of mobile apps that use TAM.

The primary goal of this learning is to critically examine how gamified language learning applications used by college-level EFL students characterize and disseminate neoliberal discourses. Both the communicative and design outlines of these applications integrate these discourses. Although user motivation and acceptance have been systematically considered in the past, these training programs commonly rely on technical and psychological frameworks, such as the Self-Determination Theory (SDT) and the Technology Acceptance Model (TAM). However, from a Critical Discourse Analysis (CDA) perspective, this study aims to fill a significant research gap. It examines how language-learning applications encourage competitive behavior, presentation following, and self-optimization. These features, which are dominant to neoliberal ideology, are regularly made possible by gamification fundamentals like leaderboards, badges, scores, and development monitoring. The work will evaluate how textual prompts, interface imageries, and learners' thoughtful responses affect users' motivation, learning behaviors, and self-perception. The primary objective remains the influence of these methods on college students' learning of English.

This proposed work utilizes Fairclough's three-dimensional CDA method to evaluate gamified language learning apps from a new critical discourse viewpoint. Compared to existing works that mainly focused on technical or motivational adoption features using SDT or TAM, this work dismantles the neoliberal ideals ingrained in EdTech user interface expansion and communication. It particularly validates how gamification structures, such as leaderboards, lines, and evaluation indicators, serve as tools for self-optimization and tracking, particularly in the context of vocabulary learning for college EFL students. To validate the results beyond digital and human-centered information, the study employs a hybrid methodological approach that combines sentiment analysis, interface logging, and discourse mapping.

Global higher education has undergone significant changes as a result of neoliberalism, which prioritizes market-driven rules, rivalries, and privatization over more general values of education²¹. In an effort to improve employability and global competitiveness, universities have increasingly adopted EMI policies, recognizing the value of English as a key advantage. Still, EMI regularly exacerbates educational differences by excluding non-English speakers and favoring those who have access to English-language materials²². As a result, a hierarchical construction is familiar, with language proficiency acting as a potential barrier. Neoliberal goals are evident in the use of EMI in nations like South Korea, which aims to improve global standings and attract investments, but it may also undermine regional languages and lead to language supremacy²³.

Investigators employed SDT and TAM in various scenarios, including ongoing intentions to use online social networking sites²⁴ and technology-enhanced learning during COVID-19^25,26to further investigate the rationale behind the use of technology. Additionally, using SDT and TAM, Isabel et al.²⁷ examined how users responded to a gamified hiring tool and discovered that candidates' opinions of the tool's usability and convenience of use are connected with its capacity to meet users' demands for autonomy and competence as well as to encourage self-motivation. Wu and Chen²⁸aimed to integrate these two theories and demonstrate clear relationships between users' perceptional elements that impact the acceptance of gamified English vocabulary learning apps and their autonomous and monitored inspiration, accordingly.

In line with the assumptions of Self-Determination Theory (SDT), recent research has situated gamification within motivational frameworks that describe its potential to support learner engagement. In their thorough review of gamification research, Seaborn and Fels (2015)²⁹ emphasize how game features, such as leaderboards, badges, and points, are intended to boost intrinsic motivation and promote learning objectives. Although results vary depending on implementation, gamification can have a favorable impact on cognitive, motivational, and behavioral outcomes, according to a meta-analysis by Sailer and Homner (2020)³⁰. Although Landers, Bauer, and Callan (2017)^31,32showed that the motivating effect of leaderboards depends on their integration with goal-setting processes, Hanus and Fox (2015)^31,32 discovered that gamification in classrooms may reduce intrinsic motivation and academic performance if it is not aligned with learners' needs. These experimental and longitudinal studies also warn against overly simplistic applications. The significance of theory-driven gamification design was further highlighted by Mekler et al. (2017)³³, who demonstrated that while specific game mechanics may improve performance, they may not always promote intrinsic motivation. When taken as a whole, these findings offer a sophisticated basis for placing the current research within the body of knowledge on gamification's conflicting but promising role in promoting learning and motivation.

The majority of the previously discussed research^1,2,3 works are still focused on motivational and technological acceptance models, even though college students are progressively using gamified mobile-assisted language learning (MALL) skills. These consist of frameworks such as the Technology Acceptance Model (TAM) and the Self-Determination Theory (SDT)¹. These methods regularly address more nuanced ideological implications, while also supporting an understanding of user behavior and interaction patterns. In particular, the way the strategy of instructional technologies incorporates specific discourses or worldviews is not given sufficient reflection. There is a shortage of critical viewpoints that examine how gamified characteristics, such as attractiveness, tracking evaluation, and self-control, characterize and support neoliberal ideals. The communication and visual fundamentals of language learning submissions have also not been widely considered from the perspective of Critical Discourse Analysis (CDA)². Understanding how these design results affect college students' learning experiences and mould their opinions of learning attainment is particularly crucial. It examines the properties of neoliberal discourses on English language students in higher education contexts by learning them through EdTech stages.

Critical educational technology research has questioned the wider ramifications of educational technologies, going beyond their motivational benefits. Selwyn (2016)³⁴cautions against unquestioning optimism, arguing that technology is not a neutral instrument but rather should be viewed in the context of social, cultural, and political settings. By analyzing how data-driven technologies alter education governance under neoliberal logics, which often prioritizes efficiency and accountability over fairness and pedagogy, Williamson (2017)³⁵ expands on this criticism. Likewise, Roberts-Mahoney, Means, and Garrison (2016)⁴ emphasize how market-oriented and corporate ideals are reflected in personalized learning platforms, redefining students as consumers of data-driven education. By taking into account various viewpoints, the current study is positioned within continuing discussions on the political economy of EdTech as well as motivational and adoption frameworks like SDT and TAM. By acknowledging both the instructional potential of gamification and the systemic forces influencing its implementation, this dual framing enhances the research's crucial foundation. University students' incentive to use Gamified language-learning apps is mediated by competitive ranking and ease of use, according to recent comparative data from post-pandemic South Korea and Hong Kong. This suggests that gamified aspects affect both engagement and usability judgments⁵. This enhances the current study's emphasis on the discursive construction of such qualities within a critical context.

In order to highlight efficacy and potential across disciplines, the scoping review³⁶ examined the state of Gamified applications within TEFL and discussed comparative insights from medical education. With a focus on their publication year, geographic distribution, study design, delivery options, technology utilization, gamification aspects, and measurement tools, the review comprised a total of 33 studies. According to the assessment, the majority of publications were published in 2018, with a substantial portion coming from Asia. The intervention studies comprised 2,531 pupils in all. The most popular approaches were mixed-method approaches and quasi-experimental pretest/posttest designs. Mobile learning technologies are widely used, and online delivery has become the most common way of instruction.

Recent comparative data from post-pandemic learning environments highlight the increasing importance of examining gamified vocabulary learning apps. According to a study conducted in Hong Kong and South Korea, gamified features such as competitive ranking and streaks are not the only factors that motivate university students to use mobile language-learning apps; perceived ease of use and cultural learning preferences also play a significant role^4,5. These results demonstrate how the post-pandemic trend toward mobile-based self-regulated learning (SRL) has increased the educational importance of app usability and design. By situating the current study within this framework, the CDA technique is expanded beyond ideology critique to encompass the discursive development of motivational and adoption processes.

Protocol

This study was approved by the Ethics Committee of Fujian Police College, Fuzhou, China. All methods were carried out in accordance with the relevant guidelines and regulations. Informed consent was obtained from all participants involved in the study. Participants were clearly informed about the research purpose, data usage, voluntary nature of participation, and their right to withdraw at any time. Written informed consent was also obtained from all the participants for the publication of anonymized quotes and insights derived from the interview transcripts and app usage patterns. No personally identifiable information or images are included in this manuscript.

The suggested methodology applies a Critical Discourse Analysis (CDA) framework to analyze the neoliberal discourses in gamified language learning interfaces, built on the prior research with the Baicizhan application. The analysis checks Baicizhan's textual mechanisms, visual designs, and functional devices through Fairclough's three-dimensional framework. Furthermore, user sentiment analysis data, interface movement records, and qualitative interview logs of college EFL students are gathered. This blended approach helps evaluate how gamification promotes competition, behavioral monitoring, and self-optimization-key elements of neoliberal ideology. Important understandings are exposed by the procedure's difficult mapping of student discussion and app design. By highlighting the tensions between engagement-focused gamification and learners' intrinsic motivation, this study clarifies how technological features influence language learning in higher education. Figure 1 shows the architectural structure of the proposed method.

Figure 1 illustrates the three-stage Critical Discourse Analysis (CDA) approach that was used to analyze the app's embedded discourses. This paradigm purposefully reframes the process as an interpretive approach rather than a technical data processing, moving from the app's specific textual elements to the abstraction of societal ideological issues. By recording the specific locations of ideological encoding -- the textual, visual, and functional aspects of the app (such as streaks, ranking tables, and motivational remarks) -- Stage 1: App Artefacts creates the empirical foundation. Following that, these artifacts undergo Stage 2: CDA Interpretive Layers, which uses a sequence of overlapping conceptual layers to visually highlight the dialectical relationship between micro-level evidence and macro-level context. The textual elements are interpreted through Discursive Practice, which deals with the reproduction of meanings, and are linked to Social Practice, which examines the context of power and ideology. The Neoliberal Ideological Themes that organize the app experience, such as individualized accountability and Competitive Motivation, are ultimately produced by Stage 3: Thematic Synthesis, which utilizes interpretive coding and Thematic Abstraction to translate the patterns found in the discourse into abstract, qualitative findings. The four major steps in the proposed protocol are as follows:

Interface design and selection of the app
A popular gamified language study app (like Baicizhan) is chosen for the study. A three-dimensional CDA model developed by Fairclough is utilized here to perform a textual analysis examining vocabulary prompts, user feedback, and interface-related signals, as well as discursive practice analysis with an emphasis on in-app communication structures and user interaction patterns. Using social practice analysis, one can uncover neoliberal values such as competitiveness, efficiency, and self-tracking. These features reflect neoliberal values such as efficiency, competition, and self-tracking.

Data collection
To gain more about college-level EFL students' knowledge, purposes, and sentiments of gamified learning surroundings, semi-structured interviews are conducted with them.

Sentiment analysis and discourse mapping
A sentiment analysis is used to regulate the emotional reply and level of participation in user reactions, chat logs, or communication information. The areas of discourse are measured for compatibility with neoliberal ideas such as performance benchmarking and self-governance.

Data integration and validation
The relationships between gamification devices and indicators of learner anxiety, reduced motivation, or performance pressure are examined. The main conversation strategies that influence user interaction and behavior are controlled.

Interface design and app selection
Baicizhan app¹ is used as a primary example of gamified Mobile-Assisted Language Learning (MALL) in the first part of the study, with a focus on vocabulary acquisition. Since it has a plethora of gamification elements, such as ranking order, monitoring of progress, visual badges, and feedback triggers, this application is universal across Chinese EFL university students.

Given China's unique combination of state-driven digital education policy, exam-oriented learning culture, and growing EdTech marketization, the focus on Chinese EFL learners is especially important. The use of digital tools in schools has increased due to national efforts like Education Informatization 2.0, which support personalized and data-driven learning strategies. At the same time, China's exam-focused educational system consistently emphasizes quantifiable results and ongoing performance assessment, which makes room for gamified features that value goal achievement, rankings, and progress tracking. These competitive procedures have been further integrated into mobile learning platforms, such as Baicizhan, which assist millions of users in preparing for important English tests, by the rapidly expanding commercial EdTech industry. The Chinese environment is an ideal setting to examine how gamified learning design naturalizes neoliberal discourses of self-optimization, competition, and performativity, due to the intersection of policy, culture, and technology.

The essential elements of the application are reasonably removed and divided into three sections as part of the interface analysis: Visual elements (e.g., icons and progress bars), Textual cues (e.g., instructions and incentives), and functional constructs (e.g., goal formation, reminders, and social competition).

Fairclough's three-dimensional Critical Discourse Analysis (CDA) approach is applied to learning these attributes. This model provides room for a comprehensive scrutiny of the behaviors, where the communicative and interactive approach of the app contains neoliberal ideology. The initial step in the CDA process is a text analysis, which examines words applied in feedback responses and app requests. Later, a study of discursive performances focuses on how users work together with the app and how pieces of success or failure are constructed within it. Lastly, the social practice study describes how the app's design reflects broader neoliberal ideologies, such as self-regulation, competition, and performance valuation. Figure 2 shows the working process of App selection and interface design.

Interface data collection
This work gathers important and complete information using www.wjx.cn, a Chinese online survey area that offers paid sample services. Initially, the inclusion criteria were that participants had to be undergraduate students enrolled in Chinese universities and have used at least one gamified English vocabulary learning app before. The study's academic goal, the voluntary nature of participation, and the estimated time required were all clearly communicated in the recruitment messaging. Participants must read an informed consent statement and provide their voluntary consent before beginning the survey. Not only was IP address screening used to reduce duplicate or low-effort responses, but attention-check items and monitoring of abnormally short completion times were also used to verify data quality. The requirement that all participants be undergraduate Chinese students served as the sample condition. The average completion time for the 279 responses from non-duplicate IP addresses collected over a week was 243 s. Seven answers that implied the respondent had never used any gamified English vocabulary apps were removed. With a genuine response rate of 97.49%, a total of 272 responses were eventually kept.

Second, semi-structured interviews were conducted with Chinese EFL students to obtain deeper insight into their experiences with gamified vocabulary apps. Each interview lasted 30-45 min and followed a structured protocol covering learning habits, emotional responses to gamification, motivation patterns, and perceived pressures. Interviews were conducted online, audio-recorded with permission, transcribed verbatim, and independently coded by two researchers using a thematic analysis approach.

Third , interface interaction data, including screenshots, logs of feedback messages, streak counters, badges, and user prompts, were systematically extracted from the Baicizhan application. These artefacts provided the textual, visual, and functional material required for Fairclough's three-dimensional CDA. In parallel, sentiment analysis data were collected by translating user-generated Chinese text into English using DeepL³⁸, verifying translation accuracy through bilingual coders, and computing polarity scores using VADER³⁹, TextBlob⁴⁰, and SnowNLP⁴¹ for cross-linguistic validation.

Chinese university students learning English as a foreign language (EFL) were interviewed to gain a deeper understanding of their interactions, motivations, and opinions regarding gamified English vocabulary apps. Questions about (1) frequency and context of app use, (2) emotional reactions to tasks or rewards, (3) engagement and motivation from app features, (4) pressure or stress related to app use, (5) influence on study habits or vocabulary learning strategies, (6) social interactions regarding app progress, and (7) recommendations for enhancing the learning experience were all part of the semi-structured protocol. Each session, conducted via Zoom, in person, or by phone, lasted approximately 30-45 min. With participants' consent, all interviews were audio-recorded and transcribed verbatim in English. Thematic analysis was employed to analyze the data. Two researchers independently coded the transcripts, which were then iteratively categorized into themes. Disagreements were resolved through discussion. A thorough analysis of the behavioral and psychological reactions to gamification was made possible by the combination of these qualitative insights with interface logs that recorded usage frequency, feedback responses, and goal pursuit.

Sentiment data preprocessing:
To ensure interoperability with English-based tools like TextBlob and VADER, all textual data was translated into English before sentiment computation because the original user responses were in Chinese. To maintain contextual and affective correctness, the Deep LAPI was used for translation, and two bilingual researchers then manually validated the results. These verified English texts were then used to calculate the sentiment polarity and subjectivity scores. The Chinese-language sentiment model SnowNLP was also used to examine a random subset of 200 replies to evaluate cross-linguistic dependability; the results showed a strong positive association with VADER polarity scores (r = 0.84).

Table 1 summarizes the responses from the dataset of 272 Chinese university EFL students who reported prior use of a gamified English vocabulary learning application. According to the demographic data, the research population of this study is suitable for investigating the target user group, as it fully represents Chinese college students in terms of gender, age, major, and years of teaching experience. In terms of practice designs, 79.04% of those measured used the apps for more than an hour each week, and 94.11% of them used them multiple times a week. The information on practice behavior was observed in conjunction with supplementary replies from the members.

The dataset includes demographic variables (age, gender, academic major, year of study) and usage-related variables (frequency of app use and weekly duration of use). The 272 entities in the dataset are typically in the 18-22 age range (91.91%), with 60.66% of them being men and 39.34% being women. These age distribution assurances indicate that the number of participants corresponds to that of college students using mobile-assisted language learning and resembles the traditional undergraduate student population.

Although respondents came from a diverse range of academic experiences, the major groupings are as follows: Technologies (38.97%), Engineering (37.87%), Science (12.87%), and Mathematics (10.29%). The cross-disciplinary distribution enables the comparison of how students in other fields view and utilize gamified systems. Regarding academic achievement, juniors (36.03%) and sophomores (26.47%) comprised the largest percentage of participants, followed by freshmen (18.01%) and seniors (19.49%). According to this distribution, the majority of respondents are mid- to upper-level students, which may influence their degree of participation and critical analysis of the learning resources. In terms of app usage, 28.31% of contributors stated that they used Baicizhan every day, while 33.46% described using it four to five times per week, suggesting a high degree of frequent interaction with the social network. Just 2.21% of users used the app fewer than once every week. Participants' weekly app usage changed, with the majority using it for 1-3 h (50.37%) and fewer students using it for more than 5 h (11.4%).

Participants' login frequency and interaction logs were interpreted as SRL-adjacent behavioral indicators, reflecting users' autonomy in managing learning activities, monitoring routines, and persistence in engagement, in accordance with post-pandemic research on technology adoption and self-regulated learning (SRL). Qualitative interview data also revealed several themes consistent with TAM characteristics, including perceived usefulness ("the leaderboard keeps me motivated to study daily") and perceived ease of use ("the app is simple and intuitive to navigate"). These dimensions conceptually link the current CDA framework with adoption-based viewpoints seen in recent comparative research, despite the fact that they were never officially coded nor modeled⁵. To quantitatively triangulate CDA and sentiment findings and enhance the integration of behavioral, perceptual, and discourse-level studies, future versions of this study will incorporate short, validated measures of TAM and SRL.

Fairclough's CDA model
A real construction³⁷for investigating how discourse in gamified language learning applications, such as Baicizhan, generates and perpetuates neoliberal ideals is accessible through Fairclough's three-dimensional Critical Discourse Analysis (CDA) model. Three interconnected procedures are combined into the model: social practice (macro-level), discursive practice (meso-level), and textual analysis (micro-level).

Textual analysis (T) -Micro level
Language aspects, including terms, organization, and app design reminders (such "Daily Goal Attained" and "Keep Your Streak Alive") are the primary objective of this level. Assume that all of the user's textual units are as follows:

    (1)

here te_i stands for a special text message or prompt.

To evaluate neoliberal arrangement and occurrence:

    (2)

 (3)

Consequently, the textual layer's neoliberal density is:

    (4)

Discursive practice (D) - Meso level
This is information gathered from interviews and interface logs about how users interact with the app (frequency of use, feedback response, and goal pursuit).

Let us_i represent a user and ai_j represent an act of interaction (such as finishing a task or looking at a leaderboard). Describe the matrix of interactions:

    (5)

The user self-optimization score is now defined:

    (6)

The neoliberal weight (importance) of act ai_j is denoted by w_j. For example, seeing the Leaderboard has a higher w_j than simply accessing the app.

Social practice (S) - Macro level
This entails analyzing the ways in which app usage and discourse mirror more general social ideas (such as productivity and competition).

Let P(us_i,λ_k) be a binary or fuzzy value that indicates if user discourse promotes characteristic λ_k, and let λ_k be an ideology trait (such as fairness or self-optimization).

Next, users' ideological appearance:

    (7)

This measures the degree to which each neoliberal ideology λ_k has propagated over the user base. A user interface's overall CDA framework score can be written as follows:

 (8)

When each dimension is given a weight (α,β,γ), which can be empirically adjusted or based on qualitative considerations.

The three-dimensional Critical Discourse Analysis (CDA) model established by Fairclough is illustrated in Figure 3, which is used to evaluate gamified knowledge for educational boundaries, primarily in language learning stages such as Baicizhan. The top row of the figure represents the three analytical dimensions of CDA, while the bottom row integrates the outcomes of those dimensions to generate a single, cohesive consequence.

This study created a brief codebook for the Critical Discourse Analysis (CDA) that divided discursive aspects into three categories: textual (word choice, metaphors), visual (icons, colors, badges), and functional (points, levels, notifications). Motivational statements like "Keep going!" for text, star badges for visuals, and progress-tracking notifications for functional features are illustrative examples. After resolving ambiguities and iteratively improving codes, two skilled coders independently applied the codebook to the data. Cohen's Kappa was used to calculate inter-coder agreement, and the results showed satisfactory reliability (κ = 0.78), ensuring consistency and rigor in the study.

Sentiment analysis and discourse mapping
Sentiment analysis is applicable to user-generated content, as well as explanations, app reviews, communication logs, and conference transcripts, following the conclusion of Fairclough's three-tiered Critical Discourse Analysis, which includes textual, discursive, and social repetition³⁷. This step categorizes emotional tone (positive, negative, and neutral) and intensity using Natural Language Processing (NLP) procedures. To establish the user's emotional connection with gamified learning dynamics, for example, normal expressions of stress or satisfaction in response to line loss or leaderboard presentation are measured. This study helps define whether gamified devices, such as following and competition, growth pressure, or participation, support or contradict the neoliberal values exposed in CDA.

Algorithm 1, provided in Supplementary File 1, shows the working process of Sentiment analysis and Discourse Mapping. After textual information has been collected and pre-processed, user observations are prepared using methods such as lowercasing, stopword elimination, lemmatization, and punctuation filtering. Next, Sentiment analysis was performed on English and Chinese EFL textual data, following pre-processing steps such as lowercasing, stopword removal, lemmatization, and punctuation filtering. These texts are subjected to sentiment analysis using programs such as VADER, TextBlob, or BERT-based models, which classify each text as neutral, positive, or negative based on its emotional tone or polarity scores. This helps define the emotional responses of users to gamified fundamentals, such as assessments of presentation, lines, and awards. In parallel, expressions and ideas related to specific neoliberal concepts -- such as "self-optimization," "concurrence," "effectiveness," or "tracking" -- are identified by removing discourse aspects through the use of keyword-based matching or thematic coding, correspondingly.

Conceptual labels that were raised throughout the CDA stage can now be understood in each user's text. A discourse map, which authenticates how detailed discourses and attitudes co-occur within the dataset, is then designed based on the outcomes. This might incorporate producing charts or heat maps that demonstrate the expressive tone and regularity of each ideological theme. To produce a more comprehensive picture of the material, these sentiments and discourse understandings are then shared with the CDA results. This investigation helps verify whether learners are adopting or rejecting the neoliberal discourses presented in the boundary scheme. Additionally, it highlights emotional encounters in gamified learning, identifying instances where moving administrations may inadvertently compromise fundamental incentives or learner selection.

Results

This work examined how gamification in Mobile-Assisted Language Learning (MALL) applications, such as Baicizhan, influences the motivation and self-regulation behavior of college EFL learners through a critical discourse systematic method. A dataset from three unified foundations was used for the estimation: (1) records of 15 semi-structured conferences with EFL college students; (2) sentiment analysis data from more than 250 user engagement logs; and (3) interface data logs that included gamification structures like log...

Discussion

The proposed Critical Discourse Analysis (CDA) adopts a critical-ideological perspective that extends previous work by examining the deeper ideological functions embedded in gamified language-learning apps... Earlier research, particularly that of Chen and Zhao¹, has examined the motivational factors influencing the acceptance of gamified vocabulary apps among Chinese EFL learners on a large scale. Building on Fairclough's three-dimensional CDA framework, the present study argues that apps suc...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Adenoviral Vector (shNEAT1)	10692-V5	Vigene Biosciences	Adenovirus used for knocking down NEAT1 expression in vivo and in vitro.
BCA Protein Assay Kit	23227	Thermo Fisher	Kit used to quantify protein concentration in cell lysates.
Dual-Luciferase Reporter Assay Kit	E1910	Promega	Kit used for dual-luciferase reporter assays to measure miR-181a-5p interaction.
ELISA Kits (Cytokines)	N/A	R&D Systems	Kits used for measuring cytokine levels in serum and tissue samples.
Fetal Bovine Serum (FBS)	16000-044	Gibco	Supplement used in culture media for cell growth.
GAPDH Antibody	2118S	Cell Signaling Technology	Primary antibody against GAPDH used as loading control in western blot.
Hematoxylin and Eosin Staining Kit	N/A	Thermo Fisher	Kit used for histological staining of tissue sections for pathological analysis.
Immunofluorescence Antibodies (CD206)	14693-1-AP	Proteintech	Antibody used for detecting M2 macrophage marker CD206 in tissue sections.
Lipopolysaccharide (LPS)	L2630	Sigma-Aldrich	LPS used to induce inflammatory macrophage polarization in RAW264.7 cells.
miR-181a-5p Mimic	N/A	N/A	Synthetic miR-181a-5p mimic used to modulate miR-181a-5p levels.
NEAT1 Expression Plasmid	N/A	N/A	Plasmid used for overexpression of NEAT1 in RAW264.7 macrophages.
RAW264.7 Macrophage Cells	TIB-71	ATCC	Murine macrophage cell line used for in vitro experiments.
RIPA Lysis Buffer	89901	Thermo Fisher	Buffer used for lysing cells to extract total protein.
RPMI-1640 Medium	11875-093	Gibco	Culture medium for RAW264.7 macrophages.
SYBR Green Master Mix	4385612	Thermo Fisher	Reagent used for quantitative real-time PCR analysis.
Transwell Migration Assay Chambers	3422	Corning	Chambers used for transwell migration assays to assess macrophage migration.
TUNEL Apoptosis Assay Kit	ab66110	Abcam	Kit used for detecting apoptosis in RAW264.7 cells by TUNEL staining.
Western Blotting Antibodies (HMGB1)	ab18256	Abcam	Primary antibody against HMGB1 used in western blotting.