Hasil untuk "Romanic languages"

G. Berry, G. Boudol

L. Kalé, Sanjeevi Krishnan

Roman Knöll, M. Mezini

A. M. Romanic, R. White, A. Arleth et al.

Nikhil Swamy, Cătălin Hrițcu, C. Keller et al.

Jiwon Jesse Han, Anirudh Chiti, Kai-Feng Chen et al.

A deep, space-based, all-sky near-infrared survey carried out with the Nancy Grace Roman Space Telescope would constitute a foundational astronomical infrastructure for decades to come. In this white paper, we present a concrete and feasible path to imaging the entire sky at $\sim0.1''$ resolution, beginning with high-impact fields in Cycle 1 and scaling to ultra-wide coverage within the nominal mission. This first-epoch survey will reach $\mathrm{H}\sim25.5$ AB mag (5$σ$) and maximize synergies with contemporaneous observatories, while preserving substantial time for other ambitious Roman programs. We outline representative scheduling scenarios and an example Cycle 1 program that triples early Roman-LSST overlap and delivers high-value community data products such as LSST forced photometry, joint \textit{Gaia}-Roman astrometry, and catalogs of Galactic substructure, stong lenses, and other rare systems. The Cycle 1 program will lay the foundation for an eventual all-sky survey, while also delivering high-impact early science. We invite broad community participation in shaping and carrying out both the initial program and the long-term vision of an all-sky Roman survey.

Tristan Kneisel, Marko Schmellenkamp, Fabian Vehlken et al.

This paper explores how natural-language descriptions of formal languages can be compared to their formal representations and how semantic differences can be explained. This is motivated from educational scenarios where learners describe a formal language (presented, e.g., by a finite state automaton, regular expression, pushdown automaton, context-free grammar or in set notation) in natural language, and an educational support system has to (1) judge whether the natural-language description accurately describes the formal language, and to (2) provide explanations why descriptions are not accurate. To address this question, we introduce a representation language for formal languages, Nile, which is designed so that Nile expressions can mirror the syntactic structure of natural-language descriptions of formal languages. Nile is sufficiently expressive to cover a broad variety of formal languages, including all regular languages and fragments of context-free languages typically used in educational contexts. Generating Nile expressions that are syntactically close to natural-language descriptions then allows to provide explanations for inaccuracies in the descriptions algorithmically. In experiments on an educational data set, we show that LLMs can translate natural-language descriptions into equivalent, syntactically close Nile expressions with high accuracy - allowing to algorithmically provide explanations for incorrect natural-language descriptions. Our experiments also show that while natural-language descriptions can also be translated into regular expressions (but not context-free grammars), the expressions are often not syntactically close and thus not suitable for providing explanations.

Luis Arturo Hernández Basave

Este trabajo expone la necesidad, desde una perspectiva sociolingüística, de saber utilizar las formas en las que aparece la segunda persona del plural en español. Nuestra hipótesis de investigación estriba en que el alumnado en Francia aprende nociones propias de la variante centro-norte peninsular (Moreno-Fernández, 2020) respecto al uso del vosotros y ustedes, pues es el geolecto por el que optan las instituciones educativas. Así, la dificultad de comprender una perspectiva lingüística que no supone que tales pronombres denotan un tratamiento de familiaridad o formalidad respectivamente tiende a causar actitudes de desaprobación y redunda en el desprestigio de las variantes americanas. Para ello, presentaremos los resultados provenientes de una encuesta en donde docentes y estudiantes se decantan por uno de los pronombres en cuestión y exponen su parecer respecto a las variantes de prestigio. Mediante un análisis cualitativo y cuantitativo, pretendemos demostrar la importancia de crear una consciencia sociolingüística en discentes y docentes de ELE ante la variación dialectal del español.

Silvia Vaccino-Salvadore

The emergence of ChatGPT in the field of education has opened up new opportunities for language learning, but it has also brought about significant ethical considerations that must be carefully considered and addressed to ensure that this technology is used responsibly. With the field of artificial intelligence (AI) advancing at an unprecedented rate, it is imperative for educators and administrators to remain vigilant in monitoring the ethical implications of integrating ChatGPT into language education and beyond. This paper will explore several ethical dimensions concerning the use of ChatGPT, a sophisticated language model developed by OpenAI, in language education. It will discuss privacy, bias, reliability, accessibility, authenticity, and academic integrity as significant ethical implications to consider while integrating ChatGPT into the language classroom. By gaining an initial understanding of the ethical implications involved in utilizing ChatGPT in language education, students, teachers, and administrators will be able to make informed decisions about the appropriate use of the technology, ensuring that it is employed in an ethical and responsible manner.

Xuechun Huang, Hamish Chalmers

In response to calls for more research to assess the effects of translanguaging on substantive learning outcomes, this systematic review begins that process by synthesising existing research on pedagogical translanguaging approaches that have been formally assessed for effectiveness in English as a Foreign Language (EFL) classrooms. A systematic process of searching and selecting the literature found 10 eligible studies. Data were extracted for narrative synthesis and quality appraisal. Results showed that pedagogical translanguaging was most frequently used to teach reading and writing rather than other domains. Five studies favoured translanguaging over English-only approaches, four of which were rated as having a high risk of bias. The remaining studies either detected no statistically significant differences between these approaches or favoured translanguaging in a small number of highly specific measures. We conclude that pedagogical translanguaging may be helpful in teaching in some circumstances, but that the low methodological quality of the literature on average makes it difficult to draw firm causal inferences. This systematic review provides the relevant background on what is already known about the effects of pedagogical translanguaging in EFL contexts to inform the work of researchers wishing to act on recommendations for more intervention research to establish the effects of the approach on English language outcomes.

Belle Li, Xiaojing Kou, Curtis J. Bonk

Since late 2022, dozens of YouTube channels focusing on a diverse array of topics related to language learning with generative AI tools such as ChatGPT have rapidly emerged. This study explores the implementations and perspectives of YouTube content creators who now constitute an increasingly important segment of the ecosystem of language teaching and learning. A mixed methods netnographic approach was employed, combining qualitative and quantitative techniques. A total of 140 videos were identified and analyzed, and an in-depth content analysis was conducted to uncover underlying themes. Four main categories of creators were identified: educators, learners, technology professionals, and e-learning providers. Educators, especially English and Japanese teachers, were the majority, followed by learners and technology field professionals. This study highlights the benefits, drawbacks, and concerns associated with the integration of AI tools in language learning. By examining this rapidly evolving phenomenon, the study contributes towards an understanding of the role and impact of generative AI tools in language education.

Luke Plonsky

As in many other social sciences, second/additional language (Lx) researchers are often interested in generalizing their findings beyond the samples they collect data from. However, very little is known about the range of learner backgrounds and settings found in Lx research. Moreover, the few papers that have addressed the range of settings and demographics sampled in Lx research paint a disappointing picture). The current study examines the extent to which concerns expressed over this issue are merited and worthy of further attention. Toward that end, sample-related features such as L1, Lx/target language, age, proficiency, and educational setting (or lack thereof) were extracted from a sample of 308 systematic reviews of Lx research. The data from this “meta-synthetic” sample are then used to estimate the extent to which Lx research has sampled—and might or might not be able to generalize to—different populations and contexts including those pertinent to migrant populations, the focus of this special issue. The results reveal an incredibly disproportionate interest in participants with English as a first or target language and as well as university students in a narrow range of countries. The findings are used to call out the applied linguistics community on this gross oversight while also seeking to inform future research and contribute to the ongoing methodological reform movement in applied linguistics.

Xiaying Li, Belle Li, Su-Je Cho

ChatGPT is a state-of-the-art generative artificial intelligence (AI) chatbot released by OpenAI in 2022. It simulates human conversation and has the capability to generate different texts at various levels of sophistication in near real time depending upon the user’s skill in creating prompts. While concerns have been raised about academic dishonesty and cheating among students, ChatGPT has significant academic potential for education, particularly in the field of language learning. This research explores the potential of ChatGPT in supporting and empowering Chinese language learners (CLLs) whose first language is English to enhance their writing skills, mainly focusing on the research question: Is there a functional relation between Chinese language learners from low‐income families using ChatGPT after school twice a week and improvements in their Chinese writing? Four participants with varying language proficiency levels were recruited, and their data were analyzed using an ABA design. Over three weeks, they utilized ChatGPT twice a week for approximately 20 min each after school. The students’ writing scores, writing samples, and learning reflections were used to triangulate the data and enhance the data’s trustworthiness. The findings indicate that (1) each participant made a noticeable improvement in their Chinese writing scores during the intervention and reversal phases; (2) ChatGPT played a crucial role in correcting errors and facilitating the development of complete sentence structures; and (3) the students expressed a sense of empowerment through their interactions with ChatGPT. These findings highlight that ChatGPT shows promise as a supportive tool for CLLs from low-income families, reducing educational inequality and promoting equitable access to language learning opportunities.

Zilu Jiang, Zexing Xu, Zilong Pan et al.

This study examined the robustness and efficiency of four large language models (LLMs), GPT-4, GPT-3.5, iFLYTEK and Baidu Cloud, in assessing the writing accuracy of the Chinese language. Writing samples were collected from students in an online high school Chinese language learning program in the US. The official APIs of the LLMs were utilized to conduct analyses at both the T-unit and sentence levels. Performance metrics were employed to evaluate the LLMs’ performance. The LLM results were compared to human rating results. Content analysis was conducted to categorize error types and highlight the discrepancies between human and LLM ratings. Additionally, the efficiency of each model was evaluated. The results indicate that GPT models and iFLYTEK achieved similar accuracy scores, with GPT-4 excelling in precision. These findings provide insights into the potential of LLMs in supporting the assessment of writing accuracy for language learners.

Azam Sadat Emadi, Iman Masoumi, Seyed Reza Musawi

Let $G=(V,E)$ be a simple graph of order $n$. A Majority Roman Dominating Function (MRDF) on a graph G is a function $f: V\rightarrow\{-1, +1, 2\}$ if the sum of its function values over at least half the closed neighborhoods is at least one , this is , for at least half of the vertices $v\in V$, $f(N[v])\geq 1$. Moreover, every vertex u with $f(u)=-1$ is adjacent to at least one vertex $w$ with $f(w)=2$. The Majority Roman Domination number of a graph $G$, denoted by $γ_{MR}(G)$ , is the minimum value of $\sum_{v\in{V(G)}}f(v)$ over all Majority Roman Dominating Function $f$ of $G$. In this paper we study properties of the Majority Roman Domination in graphs and obtain lower and upper bounds the Majority Roman Domination number of some graphs.

Elena Callegari

This paper introduces Exclamative Se Constructions (ESCs), analyzing their use in two Romance languages: Trevigiano, a northern Italian dialect, and Standard Italian. ESCs are used to express shock or surprise at someone’s statement and to challenge its accuracy. Although they resemble adverbial clauses, ESCs function autonomously as main clauses. I identify four defining properties of ESCs: adverbial clause form, form-function mismatch, main-clausality, and the anchoring of surprise to a preceding assertion rather than to the ESC’s own propositional content. I argue that ESCs function as counterarguments, specifically rebutting assumed premises using contextually relevant scales. These rebuttals consistently involve asserting a value that contrasts sharply with some initial statement, often reaching an extreme or unexpected point on the contextually relevant scale.

M. Farrús

The language learning field is not exempt from benefiting from the most recent techniques that have revolutionised the field of speech technologies. L2 learning, especially when it comes to learning some of the most spoken languages in the world, is increasingly including more and more automated methods to assess linguistics aspects and provide feedback to learners, especially on pronunciation issues. On the one hand, only a few of these systems integrate automatic speech recognition as a helping tool for pronunciation assessment. On the other hand, most of the computer-assisted language pronunciation tools focus on the segmental level of the language, providing feedback on specific phonetic pronunciation, and disregarding the suprasegmental features based on intonation, among others. The current review, based on the PRISMA methodology for systematic reviews, overviews the existing tools for L2 learning, classifying them in terms of the assessment level, (grammatical, lexical, phonetic, and prosodic), and trying the explain why so few tools are nowadays dedicated to evaluate the intonational aspect. Moreover, the review also addresses the existing commercial systems, as well as the existing gap between those tools and the research developed in this area. Finally, the manuscript finishes with a discussion of the main findings and foresees future lines of research.

Jessica A. Scott, Scott Cohen

Translanguaging is an approach to the education of multilingual students that has been gaining in popularity in recent years. Translanguaging is defined as the use of multiple languages and modalities during classroom instruction with the express purpose of encouraging and teaching students to use all their linguistic resources to make sense of content and demonstrate their understanding. Though there has been a great deal of research on translanguaging in general, this article focuses on two less-studied areas for translanguaging: the intersection of translanguaging in deaf education and content area education. Here, the authors explore the literature available in the domains of translanguaging and science, translanguaging and deaf students, and translanguaging in science classrooms with deaf students. Using the findings from these articles, the authors present four recommendations for teachers who want to engage in translanguaging in the content areas with deaf students: First, teachers can use both signed language and English (or other written languages) during instruction to expose students to essential concepts in multiple languages. Next, teachers can expose students to discipline and subject-specific written and signed words, as well as colloquial meanings of similar written and signed words (e.g., evidence versus fact versus proof in American Sign Language). Additionally, teachers should attempt to be visually oriented while translanguaging. Finally, we recommend that teachers encourage students to respond to questions or prompts using multiple languages and/or modalities. Implications of each recommendation are explored.

Sanath Kumar Vengaldas, Adarsh Reddy Muthyala, Bharath Chaitanya Konkati et al.

Roman domination is a well researched topic in graph theory. Recently two new variants of Roman domination, namely triple Roman domination and quadruple Roman domination problems have been introduced, to provide better defense strategies. However, triple Roman domination and quadruple Roman domination problems are NP-hard. In this paper, we have provided genetic algorithm for solving triple and quadruple Roman domination problems. Programming (ILP) formulations for triple Roman domination and quadruple Roman domination problems have been proposed. The proposed models are implemented using IBM CPLEX 22.1 optimization solvers and obtained results for random graphs generated using NetworkX Erdos-Renyi model.

Mohammad Hadavi, Lutong Sun, Djordje Romanic

Severe windstorms pose threats to people, human-made structures, and the environment. An investigation of insured losses caused by windstorms is a multipurpose study that serves to advance the resilience and sustainability of modern communities. The present study proposes a systematic analysis of insured losses imposed by different types of windstorms in two Canadian provinces, Ontario (ON) and Quebec (QC), during the period 2008-2021. Actual wind damage data from the Canadian insurance market were considered in this study. Our calculations show that ON and QC received half of all wind catastrophes across Canada, and nearly three-quarters of all types of catastrophes in ON and QC were wind-related ones. The total windstorm loss of over CA$5.2 billion was not evenly distributed between QC and ON, but rather had a QC:ON ratio of 1:3.1. We attributed this discrepancy in the inflicted damage between two provinces to the predominantly eastward and northeastward storm trajectories and the higher density of wealth and population in ON. Convective storms were the most devastating wind type comprising nearly 65% and 67% of the total number of events and associated damage, respectively. Finally, tornadoes had the highest average loss per event in two provinces combined. Future prospects and the implication of this research are also discussed.