Hasil untuk "English language"

E. Hoff

Judy Pearsall, Patrick Hanks, Catherine Soanes et al.

A. Pennycook

T. Winograd

T. Parsons

N. Chomsky

A. Graesser, D. McNamara, M. Louwerse et al.

D. Biber, Susan Conrad, R. Reppen

T. Yashima

M. Celce-Murcia

M. Nystrand, Adam Gamoran, Robert Kachur et al.

J. Jenkins

Abstract In the relatively few years since empirical research into English as a Lingua Franca began being conducted more widely, the field has developed and expanded remarkably, and in myriad ways. In particular, researchers have explored ELF from the perspective of a range of linguistic levels and in an ever-increasing number of sociolinguistic contexts, as well as its synergies with the field of Intercultural Communication and its meaning for the fields of Second Language Acquisition and English as a Foreign Language. The original orientation to ELF communication focused heavily, if not exclusively, on form. In light of increasing empirical evidence, this gave way some years later to an understanding that it is the processes underlying these forms that are paramount, and hence to a focus on ELF users and ELF as social practice. It is argued in this article, however, that ELF is in need of further retheorisation in respect of its essentially multilingual nature: a nature that has always been present in ELF theory and empirical work, but which, I believe, has not so far been sufficiently foregrounded. This article therefore attempts to redress the balance by taking ELF theorisation a small step further in its evolution.

U. Ammon

Suresh Canagarajah

D. Crystal

Stephan Schicker

In diesem Beitrag wird die forschungsgeleitete Entwicklung, iterative Evaluierung und Optimierung des Kurses Das Fake News-Radar aktivieren (Sekundarstufe II) durch Design-Based Research (DBR) (vgl. Euler / Sloane 2014) vorgestellt. Dieser vier Unterrichtseinheiten umfassende Kurs zielt darauf ab, Lernende für das Thema Falschinformationen bzw. für textinterne Auffälligkeiten von Fake News zu sensibilisieren. Die Konzeption des Kurses ist an der Schnittstelle von Sprachdidaktik und Mediendidaktik angesiedelt und greift auf verschiedene interdisziplinäre Bezugskonzepte wie das epistemisch-wachsame Lesen (vgl. Sperber et al. 2010) oder die Gamification (vgl. Sailer et al. 2017) zurück. Die empirische Evaluierung des Kurses im Zuge der 1. Iteration zeigte u.a., dass die Lernenden die Textlastigkeit der Unterrichtsmaterialien als herausfordernd und wenig motivierend empfanden. Aufbauend auf diesen Evaluationsergebnissen wird der Kurs in der nächsten Iteration konzeptionell dahingehend optimiert, dass eine verstärkte multimodale Aufbereitung der Inhalte im Sinne des Multimedia-Prinzips (vgl. Mayer / Fiorella, 2021) sowie der gezielte Einsatz von Gamification-Elementen vorgesehen sind, um die im Motivationsmodell von Ryan und Deci (2020) zentrale wahrgenommene Selbstbestimmung der Lernenden gezielt zu fördern.   Abstract (English): This article focuses on the four-lesson course Activating the Fake News Radar for upper secondary school learners, which aims to sensitize learners to the topic of disinformation and text-internal features of fake news. The research-led development, iterative evaluation and subsequent improvement of the course using Design-Based Research (DBR) (cf. Euler / Sloane 2014) is presented. The design of the course is positioned at the interface of language didactics and media didactics and draws on various interdisciplinary reference concepts such as epistemic vigilance (cf. Sperber et al. 2010) or gamification (cf. Sailer et al. 2017). The empirical evaluation of the course during the first iteration revealed, among other things, that learners perceived the text-heavy nature of the instructional materials as challenging and demotivating. Based on these evaluation results, the course will be conceptually optimized in the next iteration by incorporating a more strongly multimodal presentation of content in line with the multimedia principle (cf. Mayer / Fiorella, 2021), as well as the targeted use of gamification elements to specifically support learners’ perceived autonomy – a central factor in the motivation model proposed by Ryan and Deci (2020)

Zicong Cheng, Ruixuan Jia, Jia Li et al.

Diffusion Large Language Models (DLLMs) are inherently ill-suited for variable-length generation, as their inference is defined on a fixed-length canvas and implicitly assumes a known target length. When the length is unknown, as in realistic completion and infilling, naively comparing confidence across mask lengths becomes systematically biased, leading to under-generation or redundant continuations. In this paper, we show that this failure arises from an intrinsic lengthinduced bias in generation confidence estimates, leaving existing DLLMs without a robust way to determine generation length and making variablelength inference unreliable. To address this issue, we propose LR-DLLM, a length-regularized inference framework for DLLMs that treats generation length as an explicit variable and achieves reliable length determination at inference time. It decouples semantic compatibility from lengthinduced uncertainty through an explicit length regularization that corrects biased confidence estimates. Based on this, LR-DLLM enables dynamic expansion or contraction of the generation span without modifying the underlying DLLM or its training procedure. Experiments show that LRDLLM achieves 51.3% Pass@1 on HumanEvalInfilling under fully unknown lengths (+13.4% vs. DreamOn) and 51.5% average Pass@1 on four-language McEval (+14.3% vs. DreamOn).

Işıl Günseli Kaçar, Elif Tokdemir Demirel

The ultimate goal of English Language teaching is to help learners attain effective interaction skills. It is necessary to observe and explore classroom interaction systematically and closely in order to reach this ultimate goal. Hence, this qualitative case study focuses on the differences between pre-service teachers (PSTs ) and in-service teachers (INTs), particularly regarding how they managed and shaped interaction in the classroom via the Self Evaluation of Teacher Talk (SETT) framework (Walsh, 2006). The data for the study was collected from the transcriptions of the video recordings of English as a Foreign Language (EFL) 9th graders in a private high school setting in Türkiye. A total of 200 minutes of lesson time was transcribed for the INTs and 240 minutes of lesson time was transcribed for the PSTs. The Transana 2.10 version was utilized for the transcriptions (Woods, 2006). A micro-analytic perspective was adopted for the transcriptions of recordings and two coders coded the transcripts using the SETT framework. Frequencies and percentages of the categories in the framework were compared and it was observed that while INTs used all classroom modes with varying frequencies, PSTs tended to use specific modes more frequently than others. The differences were observed to affect the resulting interactional patterns in the classroom. The study elaborates on these differences and their impact on the training of pre-service teachers.

Miao Jiang, Honghui Chen

Chinese named entity recognition (NER) is a fundamental natural language processing (NLP) task that involves identifying and categorizing entities in text. It plays a crucial role in applications such as information extraction, machine translation, and question-answering systems, enhancing the efficiency and accuracy of text processing and language understanding. However, existing methods for Chinese NER face challenges due to the disruption of character-level semantics in traditional data augmentation, leading to misaligned entity labels and reduced prediction accuracy. Moreover, the reliance on English-centric fine-grained annotated datasets and the simplistic concatenation of label semantic embeddings with original samples limits their effectiveness, particularly in addressing class imbalances in low-resource scenarios. To address these issues, we propose a novel Chinese NER model, LGDA, which leverages Label-Guided Data Augmentation to mitigate entity label misalignment and sample distribution imbalances. The LGDA model consists of three key components: a data augmentation module, a label semantic fusion module, and an optimized loss function. It operates in two stages: (1) the enhancement of data with a masked entity generation model and (2) the integration of label annotations to refine entity recognition. By employing twin encoders and a cross-attention mechanism, the model fuses sample and label semantics, while the optimized loss function adapts to class imbalances. Extensive experiments on two public datasets, OntoNotes 4.0 (Chinese) and MSRA, demonstrate the effectiveness of LGDA, achieving significant performance improvements over baseline models. Notably, the data augmentation module proves particularly effective in few-shot settings.

A. Salahi-Niri, M. Zali, A. Yadegar

Objective: Helicobacter pylori remains one of the most prevalent bacterial infections globally. While H. pylori eradication reduces gastric cancer risk, increasing antimicrobial resistance (AMR) impairs the effective treatment with major geographic variability. This systematic review summarizes the global epidemiology, mechanisms of resistance, clinical consequences, and emerging strategies to address H. pylori AMR. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for articles published between 2000 and June 2025. Keywords included “Helicobacter pylori”, “antimicrobial resistance”, “clarithromycin”, “metronidazole”, “levofloxacin”, “children”, and “epidemiology”. Inclusion criteria comprised English-language studies in human subjects reporting prevalence, mechanisms, or treatment outcomes related to resistance. Excluded were case reports, animal studies, and small series (<20 patients). Emphasis was placed on multicenter trials, surveillance reports, and systematic reviews. Results: Analysis of included studies demonstrated that clarithromycin resistance exceeds 20-30% in many regions, particularly East Asia and Southern Europe, undermining traditional triple therapy. Metronidazole resistance is widespread, ranging from 30-70% globally, while levofloxacin resistance shows alarming upward trends. Amoxicillin and tetracycline resistance remain rare, while rifabutin retains activity against multidrug-resistant strains. Pediatric populations exhibit especially high clarithromycin and metronidazole resistance, with pooled prevalence exceeding 35% in some regions, limiting therapeutic options. Molecular mechanisms primarily involve point mutations in 23S rRNA, rdxA/frxA, and gyrA genes, while next-generation sequencing has identified additional candidate loci. Novel strategies, including bismuth-based quadruple therapy, vonoprazan-based dual regimens, and molecular-guided therapy improve outcomes. Adjunctive measures such as probiotics, antimicrobial peptides, and stewardship interventions offer further promise. Conclusions: H. pylori AMR represents a critical barrier to eradication worldwide. Expanded global surveillance, rapid molecular diagnostics, and personalized therapy are urgently needed. Future research should prioritize pediatric-focused strategies, non-antibiotic alternatives, and equitable access to optimized regimens. Coordinated international action is essential to contain resistance and preserve the benefits of eradication.