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Comparing language tests and test preparation materials holds important implications for the latter's validity and reliability. However, not enough studies compare such materials across a wide range of indices. Therefore, this study investigated the text complexity of IELTS academic reading tests (IRT) and IELTS reading practice tests (IRPrT). Fine-grained quantitative analyses were undertaken to delineate measures of lexical, syntactic, and discourse complexity across a corpus of 108 IRT and 108 IRPrT published by Pearson, Macmillan, and Cengage Learning. The results suggest little difference between IRT and IRPrT at the lexical level; however, there were significant differences in some measures of syntactic and discourse level complexity. The findings bear implications for stakeholders including learners as test takers, instructors, material developers, and language testing researchers. We interpret this to mean that while IRPrT materials are lexically conducive to the practice for IRT and can provide a similar experience, the IRPrT do show some differences in the amount of subordination and idea repetition at the discourse level. Therefore, instructors and learners may seek to supplement practice with these structures when preparing for IRT, and the designers of such practice materials should consider aligning these factors in the future.

In the current era, the use of corpora in language teaching is mainly explored in English classes as it has become a trend in education. Hence, this research aimed to identify the corpus metadata, frequently used words, and unique words related to the Islamic boarding school context to be used in the English instructional process. This research employed a mixed method combining quantitative and qualitative data analysis methods. Two English Islamic boarding school books, several articles covering the scope of Islamic boarding school, and students' speech texts were selected as the data. Then, they were analyzed using the Voyant tool. The finding showed total words of 49,970: 5,417 specific words, 0.108 vocabulary density, and a 12,980-readability index. The finding will be incorporated into instructional resources for developing Islamic boarding school students' general and/or specialized vocabulary. The words, in particular, will provide a foundation for students in constructing Islamic speech texts, delivering speeches, and using English in an Islamic boarding school environment.

Idioms are highly conventionalized expressions that allow users to express beyond literal meanings. Despite the language difference, counterparts of idioms may overlap cross-culturally due to similar origin, social habits, and experiences. It is therefore interesting to probe whether L2 learners may benefit from deliberate instructions built on shared counterparts existing in both L1 and L2, known as cross-language overlaps. Although the phenomenon of cross-language overlap has been reported by lexical and collocation processing research (e.g., Carrol & Conklin, 2017; Hubers et al., 2020), it is yet uncertain whether its impact is influential for L2 idiom learning with the absence of semantic or syntactical similarity. To address this gap, the current study attempted to investigate whether raising English as a foreign language learners' awareness of cross-language overlap may facilitate L2 idiom teaching and how such overlap-enhancement instruction may change over different congruence conditions (congruence-available and incongruence conditions) and time. The study collected L2 idiom learning data from forty-five Chinese English learners, following a pretest-posttest design. While the experimental group (n=26) learned twenty-eight target L2 idioms with an enhancement instruction emphasizing the overlaps, the control group (n=28) learned these idioms with a conventional instruction method. Findings of this study revealed that participants were able to benefit from cross-language overlaps from both conditions, and the enhancement instruction on the semantic overlaps would result in significant learning improvement. It is therefore pedagogically advisable for language teachers to marry up L1-L2 idiom phrases containing similar counterparts and deliberately focus on the semantic congruency.

AI-generated paraphrasing tools, especially QuillBot and Paraphrasing Tool, play a crucial role in preventing plagiarism in academic writing. However, their effectiveness and proficiency have been questioned, particularly regarding the adequacy of their strategies. This qualitative study analyzed the paraphrasing strategies and proficiency levels of QuillBot and Paraphrase Tool. Using a purposive sampling technique, all 30 abstracts from one issue of the "Journal of Second Language Writing" were paraphrased using the two paraphrasing tools in their standard modes, and the results were analyzed using the frameworks of Keck (2014) and Nabhan et al. (2021). The results of the study indicated that both tools primarily used synonym substitution, with QuillBot favoring word-level changes and Paraphrase Tool emphasizing sentence restructuring. QuillBot tended to show minimal revision, followed by moderate revision, while Paraphrase Tool exhibited more moderate revision, followed by minimal and substantial revision. Paraphrase Tool exhibited broader paraphrasing capability than QuillBot, but both tools show some paraphrasing limitations. Overall, while these tools may enhance some writing, writers should thoroughly review the core concepts of the original texts and grammatical structures in specific contexts. For novice writers, paraphrasing practice in classrooms should be conducted under teachers' guidance. AIgenerated tools should be secondary.

Mastery of the art of crafting online reviews and responses is essential as a valuable additional tool for enhancing learners' English proficiency in specialised contexts. This study aims to: 1) Examine the formality levels in crafting reviews and responses; 2) Explore face-threatening acts utilised in reviews and politeness strategies employed in responses; and 3) Uncover moves and steps in composing reviews and responses. Data was collected from the Top 25 Hotels in the World in 2022 according to Tripadvisor.com. Six distinct corpora were formed, comprising positive, negative, and mixed reviews, along with corresponding responses, resulting in 87,973 tokens. Findings show reviewers leaned towards casual language (85.87%), while respondents used a consultative style (82.67%) in their responses. The shift from casual to consultative occurred most frequently (70.67%). For politeness, admiration (73.07%) and expressions of complaints/reprimands (64.53%) were common in reviews. In responses, strategies like offering, promising, or giving gifts (72.00%) and apologising and begging for forgiveness (50.40%) were prevalent. Reviews used three moves and ten steps, while responses employed six moves and 16 steps. These insights can be integrated into ESP classrooms to enhance review and response writing skills effectively.

The Khotons, in the west of Mongolia, were originally Turkic people who spoke the Khoton language, until the 19th century, which is currently considered extinct. This study aimed to prove that the Khoton language belonged to the Turkic languages; and to standardize the Swadesh inventory in relation to the Khoton words. The Swadesh inventory of words was the primary source of this research, which was sampled to examine the basic characteristics like semantic (meaning) acoustic (sound-based), pronunciation and spellings. This study adopted a comparative-historical research design with a qualitative approach, which involved an in-depth content analysis of the data. A data classification process was used to analyze the data by dividing them into categories (3-tiers) to enable retrieving, sorting and storing information. The Swadesh list of words also acted as the instrument of the study as this list was used to target the Khoton vocabulary and determine their equivalence. Such a data classification also helped to manipulate, track and analyze individual specimens in data. The findings of the study reveal that a majority of the linguistic combinations (lexemes) sampled for this study were found in the Khoton language, fully corresponding to the meaning of the English words in the Swadesh list. It was also evident that the basic vocabulary of the Khoton language had the elements of a Turkic language; and that there was an opportunity to reproduce the Khoton language. It is recommended that future studies should examine other versions of Swadesh inventory and compare them with the words in Turkic languages.

The purpose of this research is to explore three English synonyms, namely critical, serious, and crucial, with a particular focus on meanings, degrees of formality, collocations, and semantic preferences. Two dictionaries, namely the Oxford Advanced Learner's Dictionary and the Cambridge Advanced Learner's Dictionary online, as well as the Corpus of Contemporary American English (COCA), were utilized as sources of information for the purpose of this research. It was shown that the two synonyms that are critical and crucial share the same core meaning and semantic preferences. However, the word serious is still distinct from the other two in terms of its particular meanings, degrees of formality, collocations, and semantic preferences. Because of this, the three words cannot be used interchangeably in any situation without causing confusion.

Across many language backgrounds, a consistent hurdle to accessing United States higher education is understanding the basic information necessary to apply for admission and financial aid and complete the many enrollment management processes necessary to begin one's college career (apply for housing, receive and submit vaccinations, register for classes, etc.). However, to date, no studies have explored how this type of higher education information can be simplified and translated into Arabic, one of the most widely spoken languages in the world and a linguistic background shared by tens of thousands of prospective international students (and their families) seeking higher education in the United States. This case study reports on research-to-practice work conducted with the University of Iowa, specifically how the university simplified their enrollment management information and how that information was translated into Arabic for native Arabic speakers seeking access to the University of Iowa. Findings reveal that the institution simplified text to speak more directly to prospective student audiences by using second person pronouns and simpler sentence structure and diction to engage this audience. Moreover, analyses of machine and human translations of English to Arabic suggest that human translation should be the preferred mechanism of translating higher education information, as Google Translate and ChatGPT provided adequate but not perfect translations of Iowa's information. Implications for practice and college access are addressed.

In recent years, the shift in the teaching approaches employed in Thai English as a Foreign Language (EFL) classroom provides new opportunities and challenges to the teachers and students form Generation Z (1995-2009) and Generation Alpha (2010-2024). This study examines verb errors among Thai EFL learners from Generation Z and Generation Alpha to compare error types and sources. 300 written assignments from both generations were analyzed by Grammarly based on a mixed-methods approach adapted from Corder's (1974). The findings showed that Generation Z produced 466 errors, considerably more than Generation Alpha's 355 errors, with both cohorts producing all types of errors, namely misformation, omission, addition, and misordering, respectively. Despite Generation Z producing higher overall error detection, Generation Alpha displayed higher rates of the certain error forms, such as misuse of singular and plural verb usage and expletive construction. Interlingual interference was the most frequently produced error source for both groups. These findings highlight the balance between L1 and L2 in EFL classrooms and the significance of teacher supervision in promoting autonomous learning. The study provides valuable insights for EFL teachers, suggesting appropriate teaching strategies in writing classes tailored to different generations.

This study seeks to clarify whether teachers are able to distinguish between essays written by English L2 students or generated by ChatGPT. 47 instructors who hold experience teaching English to native speakers of Japanese in universities or other higher education institutions were tested on whether they could identify between human written essays and ChatGPT generated essays. The ICNALE written corpus (Ishikawa, 2013) was used to find and randomly select the essays of four Japanese university students' written work who studied English at roughly CEFR A2 level. The AI chatbot, ChatGPT, was used to generate four essays utilizing prompts which directed the chatbot to mimic grammar mistakes common to nonnative speakers of English. Teachers were requested to identify which of the eight essays they believed to be human written or ChatGPT generated. On average, the teachers were able to identify 54.25% of items accurately. This result is slightly better than random chance, and implies that most teachers cannot make an accurate assessment on a ChatGPT generated essay when ChatGPT is prompted to make grammar mistakes.

Previous research has shown that L1-L2 congruency is a facilitative factor in collocation processing. The present study explores the congruency effect between learners' L2 and L3. Thirty-three proficient Norwegian learners with Lithuanian as their L1 and English as their L2 completed a phase acceptability task consisting of three groups of Norwegian collocations: congruent with L2, congruent with both L1 and L2, and incongruent with both. The results revealed that collocation L2-L3 congruency facilitates collocation processing.

This brief report presents the results of a re-analysis of data by Yi, Man, and Maie (2023), who investigated L1 and L2 intuitive knowledge of phrasal frequency and collocation strength in multiword sequences. We utilized an individual-differences approach and examined which participant variables (age of onset, length of residence, language use, and L2 proficiency) predicted the participants' accuracy in judging the phrasal frequency and association strength of multiword sequences in English. We found that the demographic variables were only related to the accuracy in judging association strength, but those variables differentially predicted the accuracy depending on whether the collocations were of high or medium association strength.

This research explores the collaboration between Indonesian English as a Foreign Language (EFL) educators and Artificial Intelligence (AI) in content development. Employing a qualitative approach, semi-structured interviews were conducted to delve into the perspectives, experiences, and interactions of educators in the realm of AI-enhanced content creation. The study adopts a phenomenological approach, seeking to uncover the underlying essence of the evolving educator-AI relationship in content development. The findings reveal a spectrum of AI integration among educators, from heavy reliance to a balanced approach. Educators demonstrate adaptability and resourcefulness, combining school textbooks with AI-powered tools, such as ChatGPT, magicschool.ai, and you.com, creating dynamic and engaging instructional strategies. Their approaches to lesson planning, content development, presentation, teaching methods, and assessment display a diverse range of AI incorporation, emphasizing flexibility and student-centred learning. Educators are blending textbook analysis, AI tools, and multimedia resources to enhance learning environments. Interactive and student-centred teaching methods, such as pair work, role-play scenarios, and dialogue practice, foster language acquisition, communication skills, and student confidence. In assessment and evaluation, the integration of AI tools, such as ChatGPT, Twee, and Questionwell is emerging, enriching evaluation methods and promising improved student learning experiences. This research sheds light on the dynamic nature of AI-augmented content development in Indonesian EFL education, highlighting educators' adaptability and resourcefulness in embracing technology to enhance pedagogical practices and foster enriched language acquisition experiences for their students.

In the realm of language acquisition, the integration of Artificial Intelligence (AI) presents a promising frontier. However, gaps exist in understanding the practical application of AI-driven tools, particularly in second language learning contexts. This study delves into the usability of ChatGPT, an advanced AI language model, within the domain of second language acquisition. This paper synthesizes existing literature on ChatGPT's multifaceted capabilities, its effectiveness, its associated challenges, and the potential solutions to these challenges in language learning environments. This review demonstrates ChatGPT's substantial potential in enhancing language learning outcomes, including fostering learner autonomy, improving motivation, and developing language proficiency. Yet, nuanced challenges such as preserving academic integrity and difficulties in crafting effective prompts emerge as crucial considerations. To address these issues, possible solutions including enhancing AI literacy among learners and educators are discussed. This paper sheds light on the complex dynamics of AI-assisted language education, urging ongoing research and refinement to fully utilize these technologies in enhancing second language learning.

A tension exists between rationalists and empiricists regarding the nature of knowledge: innate then activated/discovered (rationalists) or constructed then invented (empiricists). The assumption is that, to a certain extent, basic knowledge seems to be innate in our mind and develops through experience by thinking processes to construct meanings. This meaning construction (thinking), mainly using language, is to make sense of and interpret the world. Meanings should be the primary motivation underlying linguistic constructions at all levels. This paper investigates meaning constructions at the lexical level of affixation by focusing on semantic behaviours of verbs in a complex word formation as an attempt: 1) to find out the semantic patterns of verb-based complex word formation, 2) to reveal if there is an iconic relationship of a sign (signifier) and its meaning (signified), and 3) to suggest for its teaching. Using semantic classes of verbs, based on situation types in deriving complex words, this study attempts to find out semantic patterns of such complex word formation and the possibility of revealing its iconicity. Using Antconc software, this study scrutinized linguistic expressions involving related verbs of state, event, and action in a corpus to reveal meaning construction at that level. The findings show several semantic patterns of verb-based complex word formation and an indication of the iconicity of signs. The paper also offers suggestions for teaching affixation.

The main objective of the current study is to carry out a critical stylistic analysis of Al Jazeera's online news reports of the 2017 Gulf crisis. The study specifically examines the linguistic strategies employed by Al Jazeera newsmakers in order to effectively communicate their ideological perspectives. The research employs Jeffries's critical stylistic framework (2010) and corpus methodologies to examine a corpus obtained from Al Jazeera English, which covers the first month of the crisis. A combination of qualitative and quantitative methodologies has been employed to analyze the ideological implications embedded within the narrative of the platform, focusing specifically on the strategies of naming, describing, equating, and contrasting. The study discloses that Al Jazeera has used specific nouns and phrases to portray the measures against Qatar as deliberate, violent, unjustifiable, and retaliatory, blaming the Saudi leadership. Complex noun phrases and evaluative adjectives have been utilized to intensify this description, while nominalization conceals agency and creates skepticism. The narrative has subtly portrayed Qatar as a passive victim of negative actions, using equating strategies to criticize the Saudi leadership's policies and the blockade. Contrasting strategies have presented contradictory actions, questioned their credibility and legitimacy, and encouraged cohesion among Gulf nations.

The proliferation of artificial intelligence (AI) has impacted scholarly publishing. This study explores the use of AI tools by Arab researchers who publish in English. A survey was conducted to gather information on AI tool adoption in research conception, writing, editing, and publishing. It collected responses from 84 participants, who provided insights into their usage patterns, challenges encountered, and ethical considerations when using AI. The findings reveal diverse adoption rates of AI tools, with high usage for language correctness and content enhancement such as grammar and spelling checking. The ethical implications of AI tool usage, including the need to ensure data quality, prevent plagiarism, and maintain ethical standards, were emphasized by respondents. Tools such as Grammarly, Endnote, and QuillBot were recognized for their utility in addressing common challenges. However, certain research-related tasks, notably research design, exhibited lower AI tool integration. The study underscores the gap between awareness and adoption of AI tools, indicating potential barriers to their utilization. Challenges such as cost, integration issues, and concerns about the accuracy and appropriateness of AI-generated content were identified. These findings offer insights for researchers, institutions, and publishers to enhance editorial processes, promote ethical AI tool usage, and bridge the gap between awareness and adoption.

This study applies the Apriori algorithm to analyse patterns, syntactic structures, and thematic clusters in Chinese studies data from various genres. This study aims to identify recurring linguistic elements in order to shed light on the dynamic nature of the Chinese language across different contexts and time periods. The Apriori algorithm is used to identify frequent item sets and establish associations between linguistic constructs in large datasets (over 20 years). This study examines the complexity of the Chinese language by analysing co-occurrence patterns, syntactic tendencies, and thematic categorizations. This study examines the evolution of language, regional word choices, and cultural nuances. Thematic clusters and sensory associations establish the relationship between language and culture. The study of Chinese language patterns and cultural implications utilises data to advance computational linguistics and theory. Computational models prioritize cultural and historical context analysis for more comprehensive language processing. Theoretical implications help researchers understand language evolution and culture, while practical implications improve language technology tools. The conclusion provides support for research in computational linguistics, cultural studies, and linguistic theory-based holistic language analysis and application.

Lexical bundles (LBs) are frequent groups of words that appear repeatedly in different academic texts. A plethora of research has explored their distribution and usage in general, particularly in academic texts. However, to our knowledge, the extent of research investigating LBs in the discussion sections of Medical Research Articles (MRAs) is scant. The present study examined the diversity and density of four-word LBs in the discussions of 1400 MRAs. Four-word bundles totaling 413, including general and subject-bound LBs, were extracted using the freeware "concordance software program" AntConc and categorized based on their syntactic structures and discourse functions. The findings revealed that discussions structurally rely heavily on phrasal LBs (i.e., prepositional phrases and noun phrases) in general and subject-bound LBs compared to clausal bundles, which include VP-based and Clause-based LBs. Regarding functional categories, the general referential bundles with their subcategories were found to have the most considerable proportion in the medical RA genre. Given the importance of LBs in disciplinary writing and academic discourse, the findings could be instrumental in crafting suitable pedagogical materials and activities on general and subject-specific LBs for academic writing in English for Medical Purposes.

This contrastive corpus-based study aims to analyze the similarities and differences of two synonyms "important" and "significant" concerning on the degree of formality in their distribution across genres as well as their collocations and semantic preference. The corpus data derived from the Corpus of Contemporary American English (COCA) and the British National Corpus (BNC) which represent varieties of English usage across different cultures (American and British). This study analyzed the top 20 noun collocates with Mutual Information (MI) scores value of at least three. The corpus data from COCA and BNC explored some shared characteristics and contrastive features. The findings discovered that the two adjectives have the same core meaning and degree of formality since their highest frequency in academic texts whereas frequencies are the lowest in informal genres i.e. TV and movie subtitles and fiction. In certain conditions, these synonyms are different in terms of noun collocation due to the fact that these synonyms co-occur with particular noun collocates. Interestingly, the findings also confirm two adjectives are near synonyms as they share only two similar theme of semantic preference. In consequence, the two synonyms cannot be used interchangeably in all contexts. Moreover, it was found that the corpora provide some useful details that are deficient in the dictionary.

This study investigated the use and functions of metadiscourse markers in English as a foreign language (EFL) virtual classroom during the Covid-19 pandemic. The study examined which metadiscourse markers--interactive or interactional--were used more frequently and how they were employed in an EFL context. It explored two interactive metadiscourse resources (code glosses and evidentials) and two interactional metadiscourse resources (attitude and engagement markers). The study utilized a mixed-method approach, using Hyland's (2004) two-componential taxonomy, to analyze a corpus of 303,148 words from 35 online lectures (90 minutes each) delivered by three university instructors in the UAE. The Mann-Whitney U test was employed to determine any significant differences in the use of these resources and their subcategories. The results revealed that the three instructors used more interactional than interactive resources. The qualitative analysis showed that code glosses and evidentials were primarily used to manage the flow of information, provide elaboration on propositional content, and provide evidence to support arguments. They were also employed to achieve cohesion and logical coherence in online classrooms. In contrast, attitude and engagement markers were used to engage students and signal the instructors' attitudes toward their material and audience. The study concludes with pedagogical implications for EFL instructors, students, and syllabus designers to foster social justice and fairness in the online learning environment, ensuring all students feel valued and empowered in their educational journey.

Reflective practice, in the shape of post-teaching self-evaluations, is a core element of many pre-service English as a Second Language (ESL) teacher training programmes such as CELTA. Most research on reflective practice has been carried out with pre-service teachers, but more evidence is needed to understand the reflective practice of in-service ESL teachers. This study employed a Corpus-Linguistics tool called LancsBox to analyse the nature of reflective discourse found in 44 post-teaching self-evaluations, written by in-service L1-English ESL teachers, in a language school in Colombia. Corpus Linguistics techniques included frequency lists, keywords, ngrams, and concordances. Results suggest that in-service teachers tend to reflect upon the area of Subject Knowledge the most. Other frequent areas of reflection include Lesson Planning and Classroom Management. Areas such as Understanding Learners and Use of Learning Technologies seem far less important. Generally, the most salient reflective discourse type they produce is Factual, followed by Prudential and Evaluative discourse. The pedagogical implications of this study are threefold. First, both preservice and in-service ESL teachers need to be taught how to reflect and this needs to be supported by teacher trainers. Second, to guide overall reflective practices, tools employed by pre- and in-service ESL teachers to reflect on their lessons could be adapted, so they mirror specific areas of reflection such as the teaching skills and reflective discourse types being evaluated. Third, the current study suggests a self-reflection tool pre- and in-service ESL teachers can use to assess and reflect on their own teaching practices.

The researcher in this study applied the notions of quantitative method, theoretical linguistics and applied linguistics to examine the correlation between the linguistic theory of end-weight principle and English adjective clauses in Q1-SCOPUS applied linguistics articles and Thai EFL learners' descriptive essays. The significance of this study is that structure and use in writing should be learned simultaneously. This current study also innovatively supports Thai EFL learners to examine their linguistic competence in structuring information in their English writing. The data collection in this study was divided into two datasets. The first dataset was 20 Q1-SCOPUS applied linguistics articles published between 2022 and 2024. Approximately 200,000 words provided 117 sentences containing adjective clauses in the final position or VPpostmodifiers. On the other hand, the second dataset was 67 descriptive essays written by Thai EFL highschool learners from both public schools and private schools under the supervision of the Ministry of Education, Thailand. The original theory of the end-weight principle proposed by Quirk et al. (1972) is followed in this study. The syntactic structure of adjective clauses follows Radford (2009) who analyzed adjective clauses as complementizer phrases (CP). The inferential statistical analysis of correlation follows SPSS 29. The results showed that there was a statistically significant relationship between the datasets of applied linguistics articles and EFL learners' descriptive essays where the p-values were reported at 0.01 and 0.02, respectively. Not only do these statistical relationships show that English is an end-weight language, but also Thai EFL learners can follow the norm accurately. This is due to the positive effect of the mother tongue interference since the Thai language is an end-weight language. The pedagogical implication of the study to EFL learners is that information structures should be contributed to grammar classes. For future studies, it is recommended to test the hypothesis of the end-weight principle with reduced adjective clauses in the medial position of sentences.

This paper focuses on integrating multimodal communication into the English-as-a-foreign-language classroom to enhance the development of students' multimodal communicative competence, multiliteracies, and 21st-century skills. To do so, I compiled a corpus of authentic materials from Lady Whistledown's Society Papers in Julia Quinn's novel "The Viscount Who Loved Me" (2000), her appearances as narrator in the Netflix series "Bridgerton" (2022), and some tweets posted by @Bridgerton. This corpus was used to plan and design a game-based teaching proposal. Finally, the paper offers a critical analysis and suggests how this proposal can feasibly contribute to fostering students' multimodal communicative competence.

Corpus analysis offers a more accurate understanding of a language than intuition but is not widely used in foreign language instruction. This study uses a hermeneutic-phenomenology approach and content analysis to examine the impact of a corpus literacy course on the perceptions and performance indicators of 29 English language teachers. The results suggest that corpus literacy instruction is crucial for language teachers to incorporate corpus into their classroom practices effectively. Even a brief, intensive corpus literacy course can positively alter teachers' perceptions and abilities, increase their awareness of corpus usage in language teaching, and equip them with the skills needed, highlighting the importance of incorporating corpus literacy into the design of English language teacher education programs.

This article highlights the need for a new type of corpus that will combine both a learner and a pedagogic corpus. The need for such a type of corpus is underlined by the interaction between input and output and the expected impact of the former on the latter within all instructional settings. Such a corpus will allow us to collect data, including teacher language, learner language, coursebook content, etc. and relate it to students' output at specific points in time. Our study aims to present and describe the "Young Learner Corpus of English" (henceforth, "YoLeCorE"), which is an example of a combined pedagogic and learner corpus. "YoLeCorE" consists of 1,5 million tokens and includes both the input and the output of 17 Greek young learners (aged 8-9) of English as a foreign language. "YoLeCorE"'s unique characteristic is that it includes not mere samples of learner language, but all the language that each and every one of the students listened to, read, or produced -- orally and in writing -- during a whole school year. It includes four distinct sub-corpora (one for each language skill) and allows researchers to conduct a variety of analyses and thus track the process of instructed second language acquisition.

Through consultations with the online version of the Longman Dictionary of Contemporary English (LDOCE) and the Corpus of Contemporary American English (COCA), this study focuses on distinguishing between the two synonymous verbs "restrict" and "constrain," both part of Coxhead's (2000) Academic Word List, in terms of formality and collocations. The COCA-informed findings revealed that both near-synonyms are most prevalent in academic genres, sharing an equally high level of formality, and exhibit six overlapping groups of noun collocates. Interestingly, the target synonyms, along with their left- and right-sided noun collocates, form context-specific clusters. The verb "restrict" is associated with terms related to governance and rights (i.e., "laws and regulations restrict liberties") while "constrain" with governance and commerce (i.e., "rules constrain spending"). The two are also characterized by unique patterns of collocating with nouns that have opposite morphological meanings: "restrict" pairs with noun collocates containing morphemes representing inbound activities (e.g., "acc"- in "access," as in "restrict access"), while "constrain" with those indicating outbound directions (e.g., "ex"- in "expansion," as in "constrain expansion").

In the field of applied linguistics, Diane Larsen-Freeman is widely recognized, among her numerous contributions, as the originator of Complex Dynamic Systems Theory (CDST). Over the years since her seminal publication (Larsen-Freeman, 1997), CDST has evolved into a meta-theory that guides the field's thinking on a variety of developmental phenomena, not least the study of second language acquisition. As its theoretical potency rises, so does the need to achieve a tangible understanding of CDST's core constructs. In this paper, we empirically examine one such construct, co-adaptation, in the context of asynchronous dyadic interaction. A data corpus of 39 emails sent between two college students in China and the United States over the course of seven weeks is analyzed using NVivo 12. Co-adaptation is observed at the pragmatic, discourse, and linguistic levels. True to the spirit of CDST, we describe, and discuss the nuances of, our findings. Considering the novelty of our approach to data analysis, we end by reflecting on the limitations specific to our study, as well as worthwhile directions for future pursuits.

Complex Dynamic Systems Theory (CDST) has significantly reshaped our understanding of second language learning and teaching across the micro, meso, and macro levels for several decades since its systematic introduction by Larsen-Freeman and Cameron (2008). This study conducts a comprehensive review of empirical research on second language learning and teaching within the framework of CDST from 2008 to 2022. Textometric analysis, a computer-supported qualitative method for textual analysis, was employed to conduct the review by using the IRAMUTEQ software. A total of 198 publications were identified, leading to the emergence of three prominent research themes: research on the language system, research on the second language learner and learning, and research on the second language teacher and teaching. The chronological trends and distinguishing features of the three research themes were discussed against a mainstream model of language learning and teaching at the micro, meso, and macro levels (The Douglas Fir Group, 2016). The findings of this review hold the potential for enlightening future research endeavors in this field.

Even though corpora have transformed language education, the majority of corpus-related research focuses on the teaching of writing. Via a systematic review of previous studies and a survey of language corpora available, this study aims to ascertain whether and to what extent the teaching of speaking, especially of conversation skills, to EFL learners, has been informed by existing language corpora, to identify spoken English corpora available and discover whether they have been informed by insights from such an approach to studying conversation as Conversation Analysis (CA). Finally, the study suggests possibilities for incorporating CA insights such that CA-informed, corpus-driven language pedagogy can be materialized. Previous studies on the use of corpora for teaching speaking were examined and spoken language corpora available were identified along with how they have been recommended and applied to the teaching of speaking, as well as possibilities for developing CA-informed corpora of spoken English for conversation teaching. The study revealed that conversation teaching in the EFL context remains to be informed by corpus linguistics. Accessible spoken English corpora are not yet geared towards language teachers and learners, and there remain issues to be resolved before employing the available corpus data and confirming its efficacy in teaching EFL conversation or speaking in general.

Critical thinking and anxiety influenced the translation competence of translators. This study sought to examine the interactions between critical thinking, attitude, and anxiety influenced the translation competence of translators. This study adopted an empirical approach to collect data from 145 student translators from many colleges in Saudi Arabia. The questionnaire was used as a data collection tool. Data were analyzed by using structural equation modelling to find out the relationship between the study factors. The results indicated that there was a negative relationship between AI anxiety with critical thinking and attitude. However, there was a strong positive relationship between attitude with critical thinking, and Machine Translation anxiety. Also, there was a positive relationship between Machine Translation anxiety with AI anxiety and critical thinking.

Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify participants' motivation for enrolling in two successive statistics education professional development online courses. Computational text mining, such as topic modeling, is a learning analytics field that has proven effective in analyzing large volumes of text to automatically identify topics or themes. This contrasts with traditional qualitative approaches, in which researchers manually apply labels (or codes) to parts of text to identify common themes. Combining topic modeling and qualitative research may prove useful to education researchers and practitioners in better understanding and improving online learning contexts that feature asynchronous discussion. Three topic modeling approaches were used in this study, including both unsupervised and semi-supervised modeling techniques. The three topic modeling approaches were validated and compared to determine which participants were assigned motivation themes that most closely aligned to their posts made in an introductory discussion forum. A discussion of how each technique can be useful for identifying topical themes within discussion forum data is included. Though the three techniques have varying success rates in identifying motivation for enrolling in the MOOCs, they do all identify similar themes for motivation that are specific to statistics education.

Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model.

Hands-off data-driven learning is a data-based, student-oriented learning model characterized by inquiry and discovery. English context vocabulary teaching is the key to English teaching in colleges and an important indicator to evaluate the quality and level of college English teaching, which is a language teaching paradigm focusing on the language environment. Combining the two approaches can give students a more realistic, practical, and meaningful language learning experience. This paper analyzes the vocabulary learning level of two non-English major undergraduate classes at Southwest University of Political Science and Law before and after the application of the context experiment. The positive effect of context vocabulary teaching in the control groups is verified by comparing and analyzing the influence of context teaching based on hands-off data-driven learning on their scores and learning results between the experimental and control groups. It shows that the combination of context in English vocabulary teaching with hands-off data-driven learning can help to improve students' ability to understand, absorb, and apply English vocabulary.

In this article, I present a justification for implementing AI literacy courses in higher education. I explore the ethical concerns and biases surrounding AI technologies, highlighting the importance of critical analysis and responsible use of AI. I then propose a conceptual framework, focusing on awareness, skill development, and the practical application of AI. This framework aims to foster comprehensive understanding and empower students to leverage AI's potential while mitigating risks. I also provide sample course titles and learning objectives. The suggested course format covers AI concepts, ethical considerations, bias awareness, and practical prompt engineering skills. There is a need to integrate AI literacy courses into higher education curricula, and I offer a roadmap for implementation. By equipping students with AI literacy, colleges can prepare students to navigate an AI-driven world responsibly while nurturing innovation and critical-thinking skills needed for future success.

This study analyzes four English synonyms -- clear, obvious, apparent, and evident -- concentrating on meanings, distribution across genre, collocations, and semantic preference and prosody. The data were drawn from learner's dictionaries and the Corpus of the Contemporary American English (COCA). It was discovered that the four synonyms share the same core meaning but differ in terms of detailed meanings, collocations, semantic preference, and semantic prosody. "Apparent" and "evident" were found to be the most formal synonyms in this study. "Obvious" is mostly associated with negative meanings. "Clear" has a wide range of detailed meanings and is usually involved with nature and the physical world. The results of this study also shed light on ELT as they can guide teachers and students in practicing skills of corpora and applying them to classroom, autonomously.

The present study attempted to examine the effect of using the connectivist approach on developing secondary-stage students' cross-cultural awareness and translation performance. The study comprised thirty-two first-year secondary stage students enrolled in El-Jalawea Institute, Sohag Governorate. The study adopted the quasi-experimental design. Thirty-two participants were randomly assigned to one group, and they were taught through a suggested web-based program. Data were gathered and analysed using both quantitative and qualitative instruments. A pre-post online translation and cross-cultural awareness test, an online questionnaire, and a web-based training program/blog were used in the study. Findings indicated that participants showed significantly higher levels of translation performance samples in the areas of reading comprehension, writing fluency, Computer-assisted Translation (CAT) tool usage, text analysis, and terminology search. Findings also showed significantly higher levels of cross-cultural awareness in both cognitive and affective aspects compared to students' achievement before the treatment. Participants also showed evidence of transition from word-for-word to meaning-for-meaning translation. The development of the students' performance was demonstrated by multiple measures that imply that the suggested web-based program is effective for developing secondary-stage students' translation performance and cross-cultural awareness skills.

There is substantial evidence that children's apparent omission of grammatical morphemes in utterances such as "She play tennis" and "Mummy eating" is in fact errors of commission in which contextually licensed unmarked forms encountered in the input are reproduced in a context-blind fashion. So how do children stop making such errors? In this study, we test the assumption that children's ability to recover from error is related to their developing sensitivity to longer-range dependencies. We use a pre-registered corpus analysis to explore the predictive value of different cues with regards to children's verb-marking errors and observe a developmental pattern consistent with this account. We look at context-independent cues (the identity of the specific verb being used) and at the relative value of context-dependent cues (the identity of the specific subject+verb sequence being used). We find that the only consistent effect across a group of 2- to 3-year-olds and a group of 3- to 4-year-olds is the relative frequency of unmarked forms of "specific" subject+verb sequences being used. The relative frequency of unmarked forms of the verb alone is predictive only in the younger age group. This is consistent with an account in which children recover from making errors by becoming progressively more sensitive to context, at first the immediately preceding lexical contexts (e.g., the subject that precedes the verb) and eventually more distant grammatical markers (e.g., the fronted auxiliary that precedes the subject in questions).

In this study, we integrated corpus technology in pre-service TESOL (Teaching English to Speakers of Other Languages) teachers' technological pedagogical content knowledge (TPACK) development in corpus technology, termed corpus-based language pedagogy (CBLP), and highlighted the collaborative effort for knowledge building among participants for TPACK development. The study examined the role of online collaboration in facilitating how pre-service TESOL teachers developed their CBLP (TPACK in corpus technology) using a learning-by-design approach, enacted it in co-designing lessons, revised their lessons and reflected on their collective knowledge building processes. Using a case study approach involving 33 participants, the study focused on both intra- and inter-group interactions to understand the pre-service teachers' learning dynamics/changes and unpack the interaction mechanism involved in online collaborative learning. To these ends, we collected data including CBLP group lesson designs, intra-group data (interviews) and inter-group peer critical comments and analysed them based on the two stages (comprehension and transformation) of Shulman's pedagogical reasoning. The results suggest that engaging in online collaboration, facilitated by the learning-by-design approach, is instrumental in enabling pre-service teachers to learn corpus technology and expand their repertoire of teaching strategies. Our findings imply that both intra- and inter-group collaboration modes are important to help pre-service TESOL teachers holistically develop TPACK for language teaching. Similar implications may be applied to other subject-specific TPACK training.

Word frequency has a long history of being considered the most important predictor of word difficulty and has served as a guideline for several aspects of second language vocabulary teaching, learning, and assessment. However, recent empirical research has challenged the supremacy of frequency as a predictor of word difficulty. Accordingly, applied linguists have questioned the use of frequency as the principal criterion in the development of wordlists and vocabulary tests. Despite being informative, previous studies on the topic have been limited in the way the researchers measured word difficulty and the statistical techniques they employed for exploratory data analysis. In the current study, meaning recall was used as a measure of word difficulty, and random forest was employed to examine the importance of various lexical sophistication metrics in predicting word difficulty. The results showed that frequency was not the most important predictor of word difficulty. Due to the limited scope, research findings are only generalizable to Vietnamese learners of English.

Corpus-based studies have offered the domain definition inference for test developers. Yet, corpus-based studies on temporal fluency measures (e.g., speech rate) have been limited, especially in the context of academic lecture settings. This made it difficult for test developers to sample representative fluency features to create authentic listening passages. To address this issue, the Fluency Corpus of Academic English Lectures (FCAEL) was created to offer insight into the thresholds for temporal fluency features in academic lecture settings. The current study compared the corpus data to the academic lecture passages in the Test of English as a Foreign Language Internet-based test (TOEFL iBT) and International English Language Testing System (IELTS) to examine the domain definition inference of these tests. In total, 14 temporal fluency measures were examined. A bootstrapped one-way multivariate analysis of variance (MANOVA), followed by a series of bootstrapped analyses of variances (ANOVAs), independent t-test, and Tukey tests showed some support for the tests, although many limitations were also found. The study suggests the 25th-75th percentile of FCAEL as tentative thresholds for each temporal fluency feature. The proposal may be useful for test developers to create and revise test materials. Coding schemes, analysis codes, and raw corpus data are available on the project's Open Science Framework page, exemplifying how Open Science can provide benefits beyond the academic community.

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes.

Recent studies have accumulated to suggest that children, akin to adults, exhibit a processing advantage for formulaic language (e.g. "save energy") over novel language (e.g. "sell energy"), as well as sensitivity to phrase frequencies. The majority of these studies are based on formulaic sequences in their canonical form. In natural language use, however, many formulaic sequences, collocations in particular, can be modified with words intervening in between the individual constituents (e.g. "save energy [right arrow] save all forms of energy"). For some collocations, their nonadjacent forms are equally, and even more frequently used than their adjacent form. Despite this, it remains almost unknown whether the processing advantage and sensitivity to phrase frequencies persist in nonadjacent collocations in children. Based on reading times in a self-paced reading experiment, the current study found that two age groups -- third and sixth graders -- exhibited a processing advantage for Chinese collocations and sensitivity to phrase frequencies, with two and even four Chinese characters inserted in the middle. The persistence of processing advantage and sensitivity was affected by insertion length as well as age. These results are in line with a usage-based theoretical approach to language learning, processing and use.

The study examines a corpus of 306 request emails written by 32 English-speaking (ES) teachers and 121 L2 learners from distinctive L1 backgrounds (i.e., Chinese, French, Spanish) and with different levels of L2 proficiency. Pragmatic competence is analyzed through the coding of direct and indirect request strategies used in formal and informal email writing. Findings reveal the influences of communicative situation, L1 background, and L2 proficiency on pragmatic competence in email writing. First, L2 learners show a significantly lower degree of situational variability compared with ES teachers. Second, L1 backgrounds have a significant impact on L2 writing performance. Third, L2 learners with higher English proficiency tend to use more indirect request strategies, but they have not developed pragmatic competence to adjust their usage across written contexts. Findings are discussed in relation to pedagogical implications for developing writing competence of L2 learners, which should be attuned to diverse rhetorical expectations and individual needs.

The impact of text length on the estimation of lexical diversity has captured the attention of the scientific community for more than a century. Numerous indices have been proposed, and many studies have been conducted to evaluate them, but the problem remains. This methodological review provides a critical analysis not only of the most commonly used indices in language learning studies, but also of the length problem itself, as well as of the methodology for evaluating the proposed solutions. Analysis of three data sets of texts produced by English language learners revealed that indices that reduce all texts to the same length using a probabilistic or an algorithmic approach solve the length-dependency problem; however, all these indices failed to address the second problem, which is their sensitivity to the parameter that determines the length to which the texts are reduced. The paper concludes with recommendations for optimizing lexical diversity analysis.

Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy. However, the actual goals still need to be attained, like embedding essays into vectors with cohesion and coherence, and providing student feedback is still challenging. In this paper, we proposed coherence-based embedding of an essay into vectors using sentence-Bidirectional Encoder Representation for Transformers. We trained these vectors on Long short-term memory and bidirectional long short-term memory to capture sentence connectivity with other sentences' semantics. We used two datasets: standard ASAP Kaggle and a domain-specific dataset with almost 2500 responses from 650 students. Our model performed well on both datasets, with an average quadratic weighted kappa score of 0.76. Furthermore, we achieved good results compared to other prescribed models, and we also tested our model on adversarial responses of both datasets and observed decent outcomes.

For some time, arguments have been made that for the increased successful use of corpus linguistics (CL), in the form of Data-Driven Learning (DDL; Johns, 1991) in the second language classroom, teacher education programs must play a central role by providing the necessary instruction and triggers. Three means of integrating corpora into teacher education have been identified (Lenko-Szymanska, 2022): as a tool for language awareness, as pedagogically-focussed instruction for DDL, and as a fully-fledged CL course. It is difficult to gauge the effectiveness of these three approaches because we can only rely on the relatively few, but growing, number of research studies that are published. Naturally, the existing studies are based on relatively small numbers of participants, rely mainly on data collected through questionnaires and self-reports, and rarely trace the extended effects of the instruction after the student teachers leave the programs. It is also hard to generalize from these accounts as they are deeply imbedded in their institutional curricula and their local teacher education traditions. Nonetheless, they provide a foundation for the discussion in this paper which proposes a tripartite framework for the integration of instructional CL into language teacher education programs. This consists of corpus literacies, corpus-based language pedagogy, and corpus-based reflective practice. In addition, we present a summary of some useful published resources that can be used to support the integration of such a framework.

Data-driven learning (DDL) is an increasingly popular area of research for language teaching and is gradually being incorporated into teacher education programs globally. However, assessing teacher trainees' (self-reported) corpus literacy, and its impact on eventually (and successfully) incorporating corpora into language teaching, is an ongoing issue. This paper explores the experiences of in-service English language teacher trainees in developing both corpus literacy and corpus-based language pedagogy skills in Vietnam. The study finds a disconnect between participants' high self-reported corpus literacy skills, high intention to conduct corpus-based language teaching, and their ability to convert said intention and literacy into viable ideas for corpus-based language teaching within their lesson planning. Potential reasons include difficulties conceptualizing DDL's integration into lesson objectives, selection of inappropriate corpora for the target audience, issues planning DDL activities for primary-age learners, and Vietnamese-specific issues mainly around access to technology. Accordingly, we present several recommendations for improving the assessment of DDL trainees' actual integration of DDL into their teaching practice.