Search Results (1,204)

The Trait Emotional Intelligence Questionnaire (TEIQue) is a tool that has been examined in a number of cultural and language variations in an effort to validate it across a range of demographics. The aim of this study is to test the robustness of the TEIQue-Child Short Form’s reliability and validity using a Greek-speaking sample. As a result, seven factors emerge from the analysis, explaining 52.4% of the variance in total. The first factor is named “emotional regulation”, the second factor is named “sociability”, and the third factor is named “positive mood”. The fifth factor is named “low impulsivity”, while the fourth, sixth, and seventh factors are named “lack of persistence”, “emotion perception”, and “adaptability”, respectively. The reliability indices of the factors “emotional regulation”, “sociability”, “positive mood”, “low impulsivity”, and “emotion perception” are all above 70, indicating acceptable reliability. The reliability indices of the factors “lack of persistence” and “adaptability” are almost at acceptable levels (α = 0.69). In conclusion, it seems that the Trait Emotional Intelligence Questionnaire (TEIQue) has undergone extensive validation across diverse linguistic and cultural populations, consistently demonstrating strong psychometric properties, and the TEIQue-CSF is a valid and reliable tool. Full article

Background/Objectives: In Spain, a high proportion of children do not meet the recommended daily levels of physical activity (PA), which highlights the urgent need to understand the motivational factors that could influence PA behavior. Self-Determination Theory is a widely used approach for assessing motivation toward exercise, employing instruments such as the Behavioral Regulation in Exercise Questionnaire (BREQ-3). However, despite the cognitive and linguistic differences that limit its direct application, this tool has not yet been adapted for children aged 6–12 years. This study aimed to adapt the BREQ-3 for use with Spanish schoolchildren and to evaluate its validity and reliability in this age group. Methods: The BREQ-3 for children (BREQ-3C) was linguistically and culturally adapted. Comprehension was tested through cognitive interviews, and reliability was assessed via a test–retest with 125 Spanish schoolchildren. Statistical analyses: Confirmatory factor analysis (CFA), Cronbach’s alpha, and the intraclass correlation coefficient (ICC) were used to evaluate validity and reliability. Results: CFA supported the factorial structure of the adapted BREQ-3 for primary schoolchildren, showing acceptable model fit indices (chi-square minimum discrepancy/degrees of freedom (CMIN/df) = 1.552, root mean square error of approximation (RMSEA) = 0.053, comparative fit index (CFI) = 0.891, Tucker-Lewis index (TLI) = 0.870). Internal consistency ranged from poor to excellent for all items and the total score of the questionnaire (Cronbach’s alpha (α): 0.535 to 0.911), except for items 3, 13, 20, and 21, where the internal consistency was unacceptable. Test–retest reliability was generally satisfactory, with ICC values indicating fair to excellent temporal stability (ICC: 0.248 to 0.911). The measurement error indicators (standard error of measurement percentage (SEM%) and minimal detectable change percentage (MDC%)) varied widely, particularly for the less reliable items. Most item scores were not significantly different between the test and retest groups, although items 2, 3, 5, 9, 17, 19, and 20 were significantly different. Conclusions: The BREQ-3C has promising psychometric properties for assessing exercise motivation in children aged 6–12 years. This tool shows potential for use in research, education, and health interventions to understand and promote physical activity motivation in primary schools. Full article

In real-world decision-making situations, experts often rely on subjective and imprecise judgments, frequently expressed using linguistic terms. While fuzzy logic offers a valuable tool to capture and process such uncertainty, traditional methods often convert fuzzy inputs into crisp values too early in the process. This premature defuzzification can result in significant loss of information and reduced interpretability. To address this issue, the present study introduces an enhanced fuzzy TOPSIS model that utilizes expected interval representations instead of early crisp transformation. This approach allows the original fuzzy data to be preserved throughout the analysis, leading to more transparent, realistic, and informative decision outcomes. The practical application of the proposed method is demonstrated through a supplier selection case study, which illustrates the model’s capability to handle real-world, complex, and qualitative decision environments. By explicitly linking the method to this domain, the study provides a concrete anchor for practitioners and decision-makers seeking transparent and robust evaluation tools. Full article

As technology continues to shape how students read and write, digital literacy practices have become increasingly multimodal and complex—posing new challenges for researchers seeking to understand these processes in authentic educational settings. This paper presents three qualitative studies that use multimodal analyses and visual modeling to examine digital reading and writing across age groups, learning contexts, and literacy activities. The first study introduces collaborative composing snapshots, a method that visually maps third graders’ digital collaborative writing processes and highlights how young learners blend spoken, written, and visual modes in real-time online collaboration. The second study uses digital reading timescapes to track the multimodal reading behaviors of fifth graders—such as highlighting, re-reading, and gaze patterns—offering insights into how these actions unfold over time to support comprehension. The third study explores multimodal composing timescapes and transmediation visualizations to analyze how bilingual high school students compose across languages and modes, including text, image, and sounds. Together, these innovative methods illustrate the power of multimodal analysis and visual modeling for capturing the complexity of digital literacy development. They offer valuable tools for designing more inclusive, equitable, and developmentally responsive digital learning environments—particularly for culturally and linguistically diverse learners. Full article

This study critically examines the state of Indigenous education in Taiwan through an interdisciplinary approach that integrates policy analysis, statistical evaluation, and localized case studies. Despite the implementation of progressive legislation, Indigenous students continue to encounter persistent disparities in both secondary and tertiary education. By drawing on national datasets and school-level examples, this paper uncovers systemic mismatches between mainstream educational practices and the linguistic, cultural, and communal realities of Indigenous populations. To contextualize Taiwan’s challenges, this study includes a comparative analysis with Indigenous education in Canada, highlighting both shared obstacles and divergent strategies. The findings indicate that, despite policy reforms and targeted programs in both nations, entrenched inequalities endure, rooted in colonial legacies, insufficient cultural integration, and a lack of community-driven educational initiatives. The article argues for a transformative shift in Taiwan’s education system: one that emphasizes the indigenization of curricula, the inclusion of Indigenous voices in educational policymaking, and greater investment in culturally responsive support mechanisms, particularly at the high school and university levels. In summary, meaningful improvement in Indigenous education requires moving from an assimilationist paradigm to one rooted in cultural respect and self-determination. Full article

The proliferation of fake news, particularly in sensitive domains like religious texts, necessitates robust authenticity verification methods. This study addresses the growing challenge of authenticating Hadith, where traditional methods relying on the analysis of the chain of narrators (Isnad) and the content (Matn) are increasingly strained by the sheer volume in circulation. To combat this issue, machine learning (ML) and natural language processing (NLP) techniques, specifically through transfer learning, are explored to automate Hadith classification into Genuine and Fake categories. This study utilizes an imbalanced dataset of 8544 Hadiths, with 7008 authentic and 1536 fake Hadiths, to systematically investigate the collective impact of both linguistic and contextual features, particularly the chain of narrators (Isnad), on Hadith authentication. For the first time in this specialized domain, state-of-the-art pre-trained language models (PLMs) such as Multilingual BERT (mBERT), CamelBERT, and AraBERT are evaluated alongside classical algorithms like logistic regression (LR) and support vector machine (SVM) for Hadith authentication. Our best-performing model, AraBERT, achieved a 99.94% F1score when including the chain of narrators, demonstrating the profound effectiveness of contextual elements (Isnad) in significantly improving accuracy, providing novel insights into the indispensable role of computational methods in Hadith authentication and reinforcing traditional scholarly emphasis. This research represents a significant advancement in combating misinformation in this important field. Full article

Recent developments in natural language processing, particularly large language models (LLMs), create new opportunities for literary analysis in underexplored languages like Romanian. This study investigates stylistic heterogeneity and genre blending in 175 late 19th- and early 20th-century Romanian novels, each classified by literary historians into one of 17 genres. Our findings reveal that most novels do not adhere to a single genre label but instead combine elements of multiple (micro)genres, challenging traditional single-label classification approaches. We employed a dual computational methodology combining an analysis with Romanian-tailored linguistic features with general-purpose LLMs. ReaderBench, a Romanian-specific framework, was utilized to extract surface, syntactic, semantic, and discourse features, capturing fine-grained linguistic patterns. Alternatively, we prompted two LLMs (Llama3.3 70B and DeepSeek-R1 70B) to predict genres at the paragraph level, leveraging their ability to detect contextual and thematic coherence across multiple narrative scales. Statistical analyses using Kruskal–Wallis and Mann–Whitney tests identified genre-defining features at both novel and chapter levels. The integration of these complementary approaches enhances microgenre detection beyond traditional classification capabilities. ReaderBench provides quantifiable linguistic evidence, while LLMs capture broader contextual patterns; together, they provide a multi-layered perspective on literary genre that reflects the complex and heterogeneous character of fictional texts. Our results argue that both language-specific and general-purpose computational tools can effectively detect stylistic diversity in Romanian fiction, opening new avenues for computational literary analysis in limited-resourced languages. Full article

This study examines L2 Korean learners’ self-reports of their socialization into discourses around the use of two categories of non-honorific (banmal) and honorific (jondaenmal) language. L2 Korean learners (n = 49) of varying proficiency levels completed a questionnaire aimed at capturing their beliefs, attitudes, and practices regarding learning and using banmal. A subset of questionnaire participants (n = 11) were interviewed, and transcripts were analyzed using discourse analysis to understand how banmal is positioned discursively in participants’ self-reported accounts of learning and using L2 Korean. Findings revealed three dominant discourses in learners’ self-reported accounts of their socialization into learning and using banmal: (1) jondaenmal is more important to them than banmal, (2) banmal does not belong in formal learning contexts such as classrooms, and (3) banmal instruction should be delayed until the intermediate or advanced level. Additionally, these discourses were connected to two overarching, at times contradictory, affective responses from participants. While they reported heightened anxiety over when to use banmal, they also described how using it instilled confidence in their sociopragmatic abilities. These findings highlight the connection between the affective experiences of learners and prevailing discourses on particular linguistic forms. Finally, we suggest the need for more integrated approaches to teaching speech styles in L2 Korean classrooms. Full article

This paper revisits the cross-reference marking system of Mbyá Guaraní, focusing on two phenomena: object agreement using the prefix i- and its allomorphs, and absolutive cross-reference marking in converbs. The analysis demonstrates that cross-reference marking in Mbyá is sensitive to abstract Case. Building on a view of agreement as an obligatory operation whose failure does not result in ungrammaticality, this paper argues that the segment i- is an object agreement prefix, rather than part of an allomorph of an active subject agreement prefix. This marker is underspecified for person, allowing it to cross-reference 1st, 2nd or 3rd objects. The paper further argues that converbs in Mbyá Guaraní follow an absolutive cross-reference marking pattern, where only intransitive subjects or objects are cross-referenced. This pattern is shown to be consistent with cross-linguistic and historical data from the Tupí–Guaraní family. This paper’s contributions include a proposal for case-sensitive agreement in Mbyá, with active agreement prefixes realizing agreement with nominative DPs only. The analysis also emphasizes the different roles of Infl and little v as probes for person features, with little v being underspecified and not triggering cyclic expansion. The proposed framework accounts for both hierarchical cross-reference marking in independent clauses and absolutive marking in converbs, unifying these two patterns under the assumption of Case dependence of agreement. Full article

This paper investigates the morphosyntactic and semanto-pragmatic behavior of the German neo-pronoun xier, a gender-neutral form used to refer to nonbinary individuals. Framed within the Minimalist Program, the analysis explores how xier carries a gender feature that encodes nonbinary identity—not through binary morphological marking, but via presupposition. The use of xier triggers a presupposition about the referent’s identity: that they are nonbinary. This gender feature is not absent, void or underspecified, but interpretively rich and categorically distinct. The analysis thus rejects any account treating xier as lacking gender. Instead, it argues that xier exemplifies a grammatical strategy of encoding gender beyond the binary, through formal structures that engage the interpretive system directly. The paper further argues that xier’s morphosyntactic profile—including its compatibility with standard agreement morphology—shows that nonbinary gender can be syntactically represented and participate fully in φ-feature interactions. Drawing on cross-linguistic comparisons (e.g., English they and the Italian adaptation ze), the study shows how presuppositional gender encoding supports stable φ-Agree, interface-compatible labeling without requiring binary valuation. The proposal refines the architecture of φ-features by allowing for interpretively active gender categories that are formally encoded even when they do not match traditional binary specifications. This account offers a model for how minimalist syntax can accommodate socially driven innovations without abandoning core theoretical principles. Xier, in this light, demonstrates that grammatical systems can expand to encode emerging reference categories—not by omitting gender, but by formally encoding nonbinary gender via presupposition. This study is the first to offer a formal syntactic account of a German neo-pronoun, linking socially driven innovation to core φ-feature operations like Agree and valuation. Full article

Providing students with effective learning resources is essential for improving educational outcomes—especially in complex and conceptually diverse fields such as Mathematics and Computer Science. To better understand how these subjects are communicated, this study investigates the linguistic structures embedded in academic texts from selected subfields within both disciplines. In particular, we focus on meta-languages—the linguistic tools used to express definitions, axioms, intuitions, and heuristics within a discipline. The primary objective of this research is to identify which subfields of Mathematics and Computer Science share similar meta-languages. Identifying such correspondences may enable the rephrasing of content from less familiar subfields using styles that students already recognize from more familiar areas, thereby enhancing accessibility and comprehension. To pursue this aim, we compiled text corpora from multiple subfields across both disciplines. We compared their meta-languages using a combination of supervised (Neural Network) and unsupervised (clustering) learning methods. Specifically, we applied several clustering algorithms—K-means, Partitioning around Medoids (PAM), Density-Based Clustering, and Gaussian Mixture Models—to analyze inter-discipline similarities. To validate the resulting classifications, we used XLNet, a deep learning model known for its sensitivity to linguistic patterns. The model achieved an accuracy of 78% and an F1-score of 0.944. Our findings show that subfields can be meaningfully grouped based on meta-language similarity, offering valuable insights for tailoring educational content more effectively. To further verify these groupings and explore their pedagogical relevance, we conducted both quantitative and qualitative research involving student participation. This paper presents findings from the qualitative component—namely, a content analysis of semi-structured interviews with software engineering students and lecturers. Full article

Chart reasoning, a critical task for automating data interpretation in domains such as aiding scientific data analysis and medical diagnostics, leverages large-scale vision language models (VLMs) to interpret chart images and answer natural language questions, enabling semantic understanding that enhances knowledge accessibility and supports data-driven decision making across diverse domains. In this work, we formalize chart reasoning as a sequential decision-making problem governed by a Markov Decision Process (MDP), thereby providing a mathematically grounded framework for analyzing visual question answering tasks. While recent advances such as multi-step reasoning with Monte Carlo tree search (MCTS) offer interpretable and stochastic planning capabilities, these methods often suffer from redundant path exploration and inefficient reward propagation. To address these challenges, we propose a novel algorithmic framework that integrates a pheromone-guided search strategy inspired by Ant Colony Optimization (ACO). In our approach, chart reasoning is cast as a combinatorial optimization problem over a dynamically evolving search tree, where path desirability is governed by pheromone concentration functions that capture global phenomena across search episodes and are reinforced through trajectory-level rewards. Transition probabilities are further modulated by local signals, which are evaluations derived from the immediate linguistic feedback of large language models. This enables fine grained decision making at each step while preserving long-term planning efficacy. Extensive experiments across four benchmark datasets, ChartQA, MathVista, GRAB, and ChartX, demonstrate the effectiveness of our approach, with multi-agent reasoning and pheromone guidance yielding success rate improvements of +18.4% and +7.6%, respectively. Full article

This study examines the transformation of literary translation practices in Georgia from the Soviet era to the post-Soviet and neoliberal periods, using postcolonial translation theory as the main analytical lens. Translation is treated not merely as a linguistic transfer but as a process shaped by ideological control, cultural representation, and global power hierarchies. In the Soviet era, censorship policies rooted in socialist realism imposed direct ideological interventions; children’s literature such as Maya the Bee and Bambi exemplified how religious or individualist themes were replaced with collectivist narratives. In the post-Soviet period, overt censorship has largely disappeared; however, structural factors—including the absence of a coherent national translation policy, economic precarity, and dependence on Western funding—have become decisive in shaping translation choices. The shift from Russian to English as the dominant source language has introduced new symbolic hierarchies, privileging Anglophone literature while marginalizing regional and non-Western voices. Drawing on the Georgian Book Market Research 2013–2015 alongside archival materials, paratextual analysis, and contemporary case studies, including the Georgian translation of André Aciman’s Call Me By Your Name, the study shows how translators negotiate between market expectations, cultural taboos, and ethical responsibility. It argues that translation in Georgia remains a contested site of cultural negotiation and epistemic justice. Full article

Background/Objectives: Children with Autism Spectrum Disorder (ASD) often display heightened sensitivity to simple auditory stimuli, but have difficulty discriminating and integrating multiple phonological features (segmental: consonants and vowels; suprasegmental: lexical tones) at the syllable level, which negatively impacts their communication. This study aims to investigate the neural basis of segmental, suprasegmental and combinatorial speech processing challenges in Mandarin-speaking children with ASD compared with typically developing (TD) peers. Methods: Thirty children with ASD and thirty TD peers will complete a multi-feature oddball paradigm to elicit auditory ERP during passive listening. Stimuli include syllables with single (e.g., vowel only), dual (e.g., vowel + tone), and triple (consonant + vowel + tone) phonological deviations. Neural responses will be analyzed using temporal principal component analysis (t-PCA) to isolate overlapping ERP components (early/late MMN), and representational similarity analysis (RSA) to assess group differences in neural representational structure across feature conditions. Expected Outcomes: We adopt a dual-framework approach to hypothesis generation. First, from a theory-driven perspective, we integrate three complementary models, Enhanced Perceptual Functioning (EPF), Weak Central Coherence (WCC), and the Neural Complexity Hypothesis (NCH), to account for auditory processing in ASD. Specifically, we hypothesize that ASD children will show enhanced or intact neural discriminatory responses to isolated segmental deviations (e.g., vowel), but attenuated or delayed responses to suprasegmental (e.g., tone) and multi-feature deviants, with the most severe disruptions occurring in complex, multi-feature conditions. Second, from an empirically grounded, data-driven perspective, we derive our central hypothesis directly from the mismatch negativity (MMN) literature, which suggests reduced MMN amplitudes (with the exception of vowel deviants) and prolonged latencies accompanied by a diminished left-hemisphere advantage across all speech feature types in ASD, with the most pronounced effects in complex, multi-feature conditions. Significance: By testing alternative hypotheses and predictions, this exploratory study will clarify the extent to which speech processing differences in ASD reflect cognitive biases (local vs. global, per EPF/WCC/NCH) versus speech-specific neurophysiological disruptions. Findings will advance our understanding of the sensory and integrative mechanisms underlying communication difficulties in ASD, particularly in tonal language contexts, and may inform the development of linguistically tailored interventions. Full article

Ireland, historically a country of emigration, has transformed into a hub of immigration. Today, over 200 languages are spoken among its 5.25 million residents, with approximately 750,000 individuals speaking a language other than English or Irish at home. This growing linguistic diversity is increasingly reflected in Irish primary classrooms, where teachers are called upon to support students from a wide range of linguistic and cultural backgrounds). In response, Teaching English as an Additional Language (EAL) modules have expanded across initial teacher education (ITE) programs in Ireland. This study examines over two decades of teacher development initiatives, tracing a shift from an earlier bilingual model—where multilingualism was viewed primarily as second language acquisition—to a more expansive, European-informed vision of plurilingualism. Drawing on recommendations for reflexive, linguistically and culturally responsive education, this research adopts an insider/outsider discursive case study approach to explore student teachers’ preparedness to support multilingual learners in Irish primary schools. Conducted through a collaboration between an Irish teacher educator/module coordinator and an intercultural education specialist, this study employs reflexive thematic analysis) of student teachers’ self-reports from a twelve-week elective module on linguistic and cultural diversity within a Primary Bachelor of Education program. Data were drawn from surveys (n = 35) across three module iterations in 2019, 2021, and 2023. Findings indicate student teachers’ growing awareness of language teaching strategies and resources, developing positive orientations toward inclusive and plurilingual pedagogy, and emerging skills in professional collaboration. However, areas for further development include strengthening agency in navigating real-world multilingual teaching scenarios and embedding deeper reflexivity around linguistic identities, integrating students’ home language and intercultural learning. The paper concludes with recommendations to expand access to language teaching resources for diverse student profiles and support collaborative, shared EAL leadership through professional learning communities as part of teacher education reform. Full article