Computer-assisted Language Learning Research Articles

Regulatory network analysis based on integrated miRNA-TF reveals key genes in heart failure

The etiology and pathophysiology of heart failure are still unknown. Increasing evidence suggests that abnormal microRNAs (miRNAs) and transcription factors (TFs) expression may be associated with the development of heart failure. Therefore, this study aims to explore key miRNAs, TFs, and related genes in heart failure to gain a greater understanding of the pathogenesis of heart failure. To search and download the dataset of mRNA chips related to heart failure from the GEO database (GSE59867, GSE9128, and GSE134766), we analyzed differential genes and screened the common differentially expressed genes on two chips using R language software. The binary interactions and circuits among miRNAs, TFs, and corresponding genes were determined by Pearson correlation coefficient. A regulatory network of miRNAs, TFs, and target genes was constructed based on bioinformatics. By comparing the sequences of patients with and without heart failure, five downregulated genes with hypermethylated mRNA and three upregulated genes with hypomethylated mRNA were identified. The miRNA-TF gene regulatory network consisted of 26 miRNAs, 22 TFs and six genes. GO and KEGG analysis results revealed that BP terms like cellular response to organic substance, cellular response to cytokine stimulus, and KEGG pathways like osteoclast differentiation, MAPK signaling pathway, and legionellosis were enriched of the DEGs. TMEM87A, PPP2R2A, DUSP1, and miR-92a have great potential as biomarkers for heart failure. The integrated analysis of the mRNA expression spectrum and microRNA-transcription factor-gene revealed the regulatory network of heart failure, which may provide clues to its alternative treatment.

Smart Operation Control of Power and Heat Demands in Active Distribution Grids Leveraging Energy Flexibility

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

A Concrete Syntax Transformation Approach for Software Language Processing

In this paper we present an approach for software language processing tasks such as code generation, reverse-engineering and refactoring, based upon text-to-text (T2T) transformations expressed using the concrete syntax of the source and target software languages. The goal of the approach is to provide simpler and more usable techniques for specifying such tasks, compared to model-to-model (M2M) or model-to-text (M2T) transformation approaches. We evaluate the approach on language processing tasks of domain-specific language (DSL) tool support, software abstraction, model transformation, reverse engineering and program translation, and show that it can be effective for these tasks.

Multimedia glosses and second language vocabulary learning: A second-round meta-analysis

The use of glosses to aid vocabulary learning in second languages has been one of the most actively studied areas in computer-assisted language learning (CALL) literature. To compile research articles that examine the effect of utilizing glosses on second language (L2) vocabulary learning, the present study employed a second-order meta-analysis technique. The second-order meta-analysis is a study that synthesizes and analyzes the findings of multiple meta-analyses rather than individual primary studies, providing a higher level of abstraction and overview of existing evidence. The study synthesizes the results from seven primary meta-analyses conducted between 2008 and 2023, which included 136 original studies. Results showed that the overall mean effect size for using glosses was medium (g = 0.63 for the fixed-effect size model and 0.76 for the random-effect size model). The results showed that moderators had a significantly mitigated the effects of multimedia glosses. In particular, beginner-level students benefited greatly from being exposed to multimedia glosses, resulting in a large effect size. Additionally, the recognition test tended to produce a higher effect size compared to other types of vocabulary tests. Furthermore, glossing was found to be more effective in improving vocabulary acquisition in expository texts rather than narrative texts. Moreover, single-mode glosses were reported to be more effective than multi-mode glosses. The findings indicated that in-text glosses, out-text glosses, and bottom glosses exhibited a small effect size, whereas pop-up and margin glosses demonstrated a medium effect size. Implications for language learning and suggestions for future meta-analytic research are provided.

Navigating linguacultures through fanfiction: unveiling opportunities and challenges in the Chilean EFL school context

Recently, fanfiction has gained attention in the field of computer-assisted language learning (CALL) as it provides space for language practice to facilitate genre-based writing and literacy development. However, there is limited research on using fanfiction as a pedagogical tool to encourage both language and culture learning in diverse foreign language (FL) school contexts. To address this gap, this article presents a pedagogical innovation that aligns fanfiction with a linguacultural (LC) framework to involve students in collaborative, technology-enhanced LC learning. The study involved 15 EFL school teachers in Chile who participated in a workshop on LC-based fanfiction project design. Data collected included teachers’ project plans and reflective journals. Analysis of teachers’ project plans identified creative opportunities for bi-linguacultural practice, online exchange, and identity reflection. Triangulation with teachers’ reflective journals revealed the benefits of fanfiction for enhancing student engagement, language creativity, cultural awareness, and use of information communication technologies (ICTs). However, challenges were identified, ranging from student-related issues to the complexities of remote teaching in rural areas. This study emphasizes the significance of exploring pedagogical innovations in diverse FL contexts, to identify and challenge the contextualized barriers hindering successful integration of language, culture, and technology in the classroom.

Exploring the role of personal quantification in alleviating generalized anxiety disorder among Chinese PhD students: A cross-sectional study.

Personal quantification plays a crucial role in preserving individual mental health. However, in previous research, its effectiveness in alleviating generalized anxiety disorder (GAD) has not been conclusively established. This study explores the impact of personal quantification on GAD among PhD students. The research data was obtained through questionnaires distributed to 308 PhD students across universities in China. Among these students, 118 anxiety-free participants were excluded, yielding valuable data from 190 students with GADs. We employed Python programming language and SPSS software for the empirical analysis. The results illustrated that personal quantification significantly and negatively impacted GAD (β = -0.148, P = .002), concurrently producing a significantly positive effect on self-efficacy (β = 0.359, P < .001). Further analysis showed that through 5000 sampling iterations and a 95% confidence level, self-efficacy significantly reduced certain symptoms of GAD (β = -0.1183; P = .026; 95% Cl: -0.2222 to -0.0144). Moreover, when the coefficient of self-efficacy was significantly negative, the impact of personal quantification on GAD remained statistically significant (β = -0.1056; P = .033; 95% Cl: -0.2025 to -0.0087). The findings indicated that personal quantification has a significant role in alleviating GAD among PhD students, which is partly mediated through self-efficacy. This study contributes valuable insights to the nonpharmacological alleviation of GAD in Chinese PhD students.

Student-teachers’ professional agency development through ADDIE model of CALL teacher preparation

Developing teachers’ professional agency through standard models of instructional designs is critical in helping them deal with the barriers in designing and implementing online learning environments. Therefore, the purpose of this study was to explore the role of an ADDIE (Analysis–Design–Development–Implementation–Evaluation) model of the computer-assisted language learning (CALL) teacher preparation program (TPP) in developing TEFL (teaching English as a foreign language) student-teachers’ professional agency (PA). We conducted an instrumental (exploratory) case study to explore the PA development of 18 TEFL student-teachers who took a CALL course that was designed based on ADDIE. The deductive thematic analysis of student-teachers’ autobiographical narratives, unstructured interviews, and course projects showed that each stage of ADDIE contributed to developing the dimensions of the PA. We concluded that the constructive role of the ADDIE model of CALL TPP in developing TEFL student-teachers’ PA might be due to ADDIE’s capability to construct a context for developing student-teachers’ dynamic capacity to tackle educational concerns and provide a context of playing different professional roles. The study bears implications for TEFL teacher educators to consider the action-based nature of PA when the ADDIE model is implemented.

Methodological characteristics in technology-mediated task-based language teaching research: Current practices and future directions

Abstract In the past two decades, the synergistic relationship among task-based language teaching (TBLT), instructed second language acquisition, and computer-assisted language learning has gained increasing interest.Technology-mediated TBLT combines these three research domains by integrating the use of technology with task-based approaches for second language (L2) learning purposes. Since the emergence of this framework, empirical studies have increasingly explored the incorporation of tasks with technology-mediated settings for L2 learning and teaching purposes. To understand the methodological characteristics of technology-mediated TBLT research to date, we conducted a systematic search and reviewed 254 technology-mediated TBLT studies published between 2000 and 2022 in peer-reviewed journals and book chapters. These studies were coded for methodological features, research foci, and types of technology. We further examined the role of technologies in task performance to identify their effectiveness in creating authentic tasks. The findings revealed that technology-mediated TBLT research investigated a rather limited scope of contexts, learner groups, and linguistic features, with little attention paid to evaluating the quality of task outcomes. The types of technology used were skewed toward computer-mediated communication. The results also showed that studies examined various interactional features, and the majority reported both quantitative and qualitative data. Furthermore, technologies were integrated into task design to create meaningful language use contexts. Based on these findings, we share suggestions for future technology-mediated TBLT research.

SMS based reservoir condition monitoring sensor system for pumped hydro storage in mini-grids

Solar mini-grids are a cost-effective and efficient method of improving energy access in sub-Saharan Africa, particularly in low-income communities. However, conventional battery storage systems have reduced the affordability of solar mini-grid-generated electricity, necessitating the development of alternative energy storage systems such as pumped hydro storage to replace lithium-ion batteries. This paper introduces a model for an SMS-based fluid parameter monitoring system to provide visibility of reservoir conditions during peak and off-peak periods. The model uses hardware such as voltage regulators, sensors, microcontrollers, and C- programming language software. Sensors monitor various parameters in the reservoir and penstock, transmitting signals to a microcontroller for analysis. The results are relayed to a display unit and personnel's mobile phones for alert notifications, ensuring precise and accurate reporting. Simulation results show the reliability of the design to monitor water levels, water temperature, flow rate, and transmission through SMS to mobile devices for timely reporting. The paper recommends the development and commercialization of this model for larger applications, such as monitoring dams for irrigation and power generation.

Optimization Through Artificial Neural Network and Compare with Response Surface Methodology for Multiples Yield

Cotton slub yarn is widely used in denomination and any other casual, physical and mechanical Conditions. The data for the underlying purpose was collected from the Department of Polymer Engineering, National Textile University, and Faisalabad. R-Programming language software is used for analysis. The output of cotton depends on several factors whose cumulative influence on Process efficiency has a direct influence. The purpose of the research was to optimize the 100% cotton slub yarn model (slub length, slub thickness, pause length and linear density) for multiples yield (elongation, imperfection, strength, coefficient of mass variation and hairiness) as Optimizing is a way of identifying and enhancing the performance of the constructed framework by assessing a set of quality parameters, such as process efficiency using two methods response- surface methodology (RSM) and artificial neural network (ANN) and the results are compared using mean square error (MSE). Furthermore, coefficients of determination () and the mean square error root (RMSE) are used for greater accuracy. However, the ANN has consistently performed better than the RSM in all the aspects. The final selected ANN model was able to simultaneously predict the five output parameters with an RMSE of 0.229.

Predicting the Potential Risk Area of the Invasive Plant Galinsoga parviflora in Tibet Using the MaxEnt Model

The Tibetan plateau, with complex and diverse ecosystems, is an important ecological security barrier to China. However, climate change and the spread of invasive plant species have imperiled the once pristine and diverse ecosystem of the region. To prevent the further spread and control of invasive plants, it is important to delineate the potential distribution patterns of alien invasive plants at the regional scale across Tibet and understand their responses to climate change. Galinsoga parviflora Cav., a member of the family Asteraceae, is an annual herbaceous plant distributed globally as an invasive weed and possesses characteristics that make it highly invasive, such as a strong ability to proliferate and disperse. The species is also known to have an allelopathic effect. There has been no report on the spatial distribution of G. parviflora in Tibet. Using field survey data, we investigated the risk of G. parviflora invasion and its impacts on the ecological safety of Tibet. We employed the MaxEnt model using the R language and SPSS software to optimize and select model parameters and data. We acquired various environmental variables along with current and future climate change scenarios (two carbon emission scenarios, SSP126 and SSP585, for the years 2050 and 2090) to predict the geographic distribution and potential risk areas in Tibet that G. parviflora can invade. The MaxEnt model accurately predicted the distribution of G. parviflora in Tibet with an average AUC of 0.985. The most suitable environmental conditions in which G. parviflora performed the best in Tibet included a mean annual temperature of 6.2–10.0 °C and an elevation range of 2672–3744 m above sea level. Our results indicate that low precipitation during the coldest quarter of the year (mean temperature −2–3 °C) was the most important variable predicting G. parviflora distribution. The results also showed that the species was hardly found when precipitation in the coldest quarter exceeded 155 mm. The current potential invasion risk areas for G. parviflora included the river valleys of central, southeastern, and eastern Tibet. With future climate change scenarios (i.e., SSP126, SSP585), the suitable habitats for G. parviflora distribution will likely shift to northwest regions from the southeast. Particularly under the highest carbon emission scenario (i.e., SSP585), the potential risk area expands more rapidly, and the center of distribution shifts to northwest regions. These findings provide useful information about the current and future changes in G. parviflora distribution in Tibet, which is crucial for the comprehensive and proactive management and control of G. parviflora under future climate change.

Effect of Mobile Assisted Language Learning (MALL) Attitude and Practices in University Students

This study explores how technology, particularly mobile devices, is changing the landscape of foreign language learning. Traditionally, foreign language education relied on various methods and techniques, but the rise of technology and changing teacher attitudes have paved the way for innovative approaches. Mobile Assisted Language Learning (MALL) represents the latest evolution, following the earlier Computer Assisted Language Learning (CALL) approach. This research investigates the impact of MALL on student attitudes and practices in foreign language learning. The mixed study involved 40 university students from various majors. Data was collected through a 5-point Likert scale survey and follow-up semi structured interviews with some volunteers. The findings revealed a strong belief among students that mobile devices are highly effective for language learning. Students highlighted the advantage of instant access to target language input through mobile tools. Overall, participants' high mobile phone use, media preferences, and frequency of mobile phone usage all pointed towards a positive attitude towards the role of mobile phones in their foreign language development. The findings of this study extend beyond the immediate research and can be applied by future researchers, teachers, parents, and others interested in language learning.

Teaching and Learning English in the Digital Era: Opportunities and Challenges

This paper examines the opportunities, challenges, and perspectives of teaching and learning English in the digital era. With the rapid advancement of technology and its integration into educational settings, English language instruction has experienced significant transformations. The paper explores the potential benefits of digital tools and platforms in language learning while also addressing the challenges in the digital language classroom. Based on a review of previous studies and a comprehensive analysis, it further discusses the evolving roles of teachers and learners in this digital landscape and presents strategies for effective implementation and utilization of technology in English language education. The findings offer to share expertise, disseminate the lessons learned, and recommend best practices collected from scholarship. Sustainable solutions to specific problems and perspectives to cope with future challenges have been proposed with the recommendation of effective systems such as Computer Assisted Language Learning (CALL), Mobile Phone Assisted Language Learning (MPALL) and other sophisticated tools that fit some specific situations.

Hey &lt;i&gt;Google&lt;/i&gt;, Let’s Write

Writing involves more than attention to form (e.g., orthography, grammar), since it requires attention to text type, content, and genre. However, most students of English as a second language (L2) tend to prioritize linguistic accuracy in their writing, to the detriment of the content of their texts. Automatic speech recognition (ASR) has the potential to mitigate this, as it reduces the cognitive burden of writing by facilitating the text input process (using a skill most humans possess—speaking), offering assistance in spelling, and allowing a focus on other aspects of the task (e.g., cohesion, content). Automatic speech recognition is not only accessible and free, but it also fulfills Chapelle’s (2001) criteria of an effective computer-assisted language learning tool (e.g., authenticity, learner fit). Despite these affordances, there is a dearth of studies examining the possible affordances of ASR for writing. This mixed methods, one-shot study examines L2 writers’ perceptions of using ASR to write using the technology acceptance model (TAM). Seventeen (N = 17) undergraduate students at a Canadian university were provided with training on Google Voice Typing (Google Docs) and carried out a series of ASR-based writing tasks over a two-hour period. In order to measure their perceptions of the target criteria, participants filled in a TAM-informed survey consisting of statements about their experience with ASR scored on a 7-point Likert scale. To further explore the participants’ perceptions, semi-structured interviews followed. Findings indicate positive perceptions of ASR’s usefulness in terms of language learning and its ease of use due to the user-friendly voice commands. This suggests that ASR has pedagogical potential, thus requiring further examination to determine its optimal use for L2 writing.

Adaptation of English Teachers to the Use of CALL in Technical Secondary Schools in Nyanza District, Rwanda, and Its Implications on Students’ Performance in English

In some Rwandan schools, the use of Computer-Assisted Language Learning (CALL) in English teaching has influenced students' English performance. In technical secondary schools, language teachers have begun utilizing new technologies as pedagogical tools to enhance students' English performance. The current study investigates how teachers adapt to the use of CALL and its implications for students' performance in English subjects in some Rwandan technical secondary schools. Sociocultural theory in language teaching guided this investigation. It used a descriptive research design with a sample of 24 English teachers from Nyanza Technical Secondary Schools. This study utilized a census of all 24 English teachers. Data were collected using questionnaires in the form of a Likert scale. Descriptive analyses indicate that 91% of respondents have a positive attitude towards CALL. The findings show that teachers of English use CALL in teaching English, which affects students’ performance in English. The inferential analysis results show a P value of 0.020, which is lower than the significant correction of 0.05. Therefore, the study findings confirm the teachers’ positive attitude towards CALL. According to the research, the Rwanda TVET Board should train new teachers and continue updating existing English teachers with new CALL technologies.

Risk factors and prognosis of postoperative acute myocardial infarction in elderly hip fracture patients combined with coronary heart disease

IntroductionThis article mainly studies the risk factors for postoperative acute myocardial infarction (AMI) in elderly hip fracture patients combined with coronary heart disease (CHD), constructs a prediction model, and evaluates the prognosis of all the patients.MethodsThis article retrospectively collected elderly patients with hip fracture and CHD who underwent hip fracture surgery at the Third Hospital of Hebei Medical University from January 2019 to December 2021. Demographic data, laboratory indicators, and imaging examination results were collected from the medical case system. The risk factors of postoperative AMI were determined by univariate and multivariate logistic regression, and a nomogram prediction model was established. The ROC curve, calibration curve and DCA decision curve were plotted by R language software. The patients in the training set were followed up for 2 years to evaluate their survival situation.Results1094 eligible patients were divided into a training set (n = 824 from January 1, 2019 to September 31, 2021) and a validation set (n = 270 from October 1, 2021 to December 31, 2022). In the training set, women accounted for 58.6%; The average age of the patients was 79.45 years old; The main type of fracture was intertrochanteric fracture. There were 64.7% patients taken B receptor blockers; A total of 166 (20.1%) patients underwent percutaneous coronary intervention (PCI); Hypertension accounted for 55.5%; 520 (63.1%) patients had a preoperative waiting time greater than 3 days; The average hemoglobin value upon admission was 101.36 g/L; The average intraoperative bleeding volume was 212.42 ml; The average surgical time was 2.5 ± 0.3 h; Reginal anesthesia accounted for 29.7%; 63 (68.5%) AMI patients had no obvious clinical symptoms; 68 (73.9%) AMI patients did not show ST-segment elevation in ECG; The risk factors of postoperative AMI were age, hemoglobin at admission, diabetes, chronic kidney disease, intraoperative bleeding, and reginal anesthesia. The AUC of the nomogram prediction model was 0.729. The AUC in the validation set was 0.783. Survival analysis showed a significant statistical difference in 2-year mortality between patients with AMI and without AMI, among all the patients with AMI, patients with ECG ST-segment elevation has higher mortality than patients without ECG ST-segment elevation.ConclusionOur research results found that the incidence of postoperative AMI in elderly patients with hip fractures and CHD was 11.1%. Age, diabetes, hemoglobin at admission, regional anesthesia, chronic kidney disease, and intraoperative bleeding are risk factors. The AUC of the nomogram in training set is 0.729. The 2-year mortality rate of the patients with AMI is higher than that of patients without AMI.

Network pharmacology and molecular docking technology for exploring the effect and mechanism of high-dose vitamin c on ferroptosis of tumor cells: A review.

To investigate the mechanism by which high-dose vitamin C (HVC) promotes ferroptosis in tumor cells via network pharmacology, vitamin C-related and ferroptosis-related targets were obtained from the PharmMapper and GeneCards databases, respectively, and their common targets were compared using the Venn diagram. Common targets were imported into the STRING database for protein-protein interaction analysis, and core targets were defined. Core targets were enriched for Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways using the R language packages. A map of the core target-based interaction network and a map of the mechanism by which HVC regulates ferroptosis were constructed. A total of 238 vitamin C-related and 721 ferroptosis-related targets were identified, of which 21 targets were common to both. Furthermore, ALDOA, AHCY, LDHB, HSPA8, LGALS3, and GSTP1 were identified as core targets. GO enrichment analysis suggested that the main biological processes included the extrinsic apoptotic signaling pathway and pyruvate metabolic process. KEGG enrichment analysis suggested that HVC regulates ferroptosis mainly through the amino acid and carbohydrate metabolic pathways. The targets were validated by molecular docking. In conclusion, HVC may promote ferroptosis in tumor cells by regulating metabolic pathways, and there is a synergistic effect between HVC and type I ferroptosis inducers. Glycolysis-dependent tumors may be beneficial for HVC therapy. Our study provides a reference for further clinical studies on HVC antitumor therapy.

Language Dissemination Paths and Modes Aided by Computer Technology

The expansion of technology and computer science, as well as advancements in language instruction and learning methodologies, has enabled computer-assisted language learning technologies to tackle this challenge. In the field of Chinese learning, a few language learning computerized systems in the country and abroad concentrate mainly on language, grammar acquisition only have one or two assessment indicators as basis of evaluation, that definite functional flaws provide a general assessment to learners' pronunciation. In this manuscript, Language Dissemination Paths and Modes Aided by Computer Technology (LDPM-QICCNN-KOA) are proposed. The input data are collected from Chinese Corpus dataset. Then the data is given into unscented trainable kalman filter for preprocessing the input data. Then the preprocessed data are provided to QICCNN for Language Dissemination. In general, the based Quantum-inspired Complex Convolutional Neural Network doesn’t express adapting optimization approaches to determine optimal parameters to ensure exact identification. Hence, KOA utilized to enhance Quantum-inspired Complex Convolutional Neural Network, which accurately done the Language Dissemination Paths and Modes. The proposed LDPM-QICCNN-KOA method is executed on python. Then performance of proposed technique is analyzed with other existing methods. The proposed technique attains 26.36%, 20.69% and 35.29% higher accuracy; 19.23%, 23.56%, and 33.96% higher F1-Score; 26.28%, 31.26%, and 19.66% higher precision when comparing with the existing methods such as research on network oral English teaching system depend on machine learning (LDPM-DBN), nonlinear network speech recognition structure in deep learning algorithm (LDPM-DNN), research on open oral English scoring system depend on neural network (LDPM-BPNN).

Promoting critical reading instruction in higher education: a three-step training scheme facilitated by using corpus technology

Abstract This study examines the development of students’ cognitive skills through hands-on concordancing, also known as the inductive Data-driven learning (DDL) method (Cobb, T. 1997. Is there any measurable learning from hands-on concordancing? System 25(3). 301–315). This method involves learners directly using concordance software to identify language rules by observing and working with concordance results. While previous research has explored DDL’s role in language acquisition, its impact on cognitive growth has received less attention. Building on a corpus-based language pedagogy (CBLP) (Ma, Q., J. Tang &amp; S. Lin. 2022a. The development of corpus-based language pedagogy for TESOL teachers: A two-step training approach facilitated by online collaboration. Computer Assisted Language Learning 35(9). 2731–2760, Ma, Q., R. Yuan, L. M. E. Cheung &amp; J. Yang. 2022b. Teacher paths for developing corpus-based language pedagogy: A case study. Computer Assisted Language Learning. 1–32), we designed a three-step training scheme that included an online task, a computer lab lesson, and a student independent corpus exploration task. The study involved 45 second-year English majors. The data were collected through surveys, interviews, and written reports, focusing on student learning of cognitive skills such as speculating, noticing, inferring, and verifying, as synthesized by Chun (2011. Learners as corpus researchers: Developing cognitive skills through hands-on concordancing. Foreign Languages Education 18(3). 73–103). The results indicate a positive response to the CBLP, with students’ independent exploration task contributing to improved critical reading and thinking skills. This highlights the effectiveness of our three-step CBLP scheme in developing cognitive skills and enhancing independent research. Overall, the study emphasizes the potential of hands-on concordancing and CBLP in fostering critical reading skills.

The agency development of teachers of an additional language via immersive VR telecollaboration

Teacher agency is essential for professional growth and teachers’ ability to support transformative student learning. This study looks into the potential of virtual reality (VR) as a joint practice teaching and reflective discussion environment for achieving language teacher agency. A group of teachers of an additional language (LX) participated in a telecollaborative project. They were provided with VR devices and language learning software, Immerse, for practice teaching and reflective discussions after each VR teaching. The data of this qualitative study were video recordings of the teaching and discussion sessions and the participants’ answers to an end-of-project questionnaire based on the Cognitive Affective Model of Immersive Learning (CAMIL) by Makransky and Petersen in 2021. The video recording analysis results showed that the participants used almost all the teaching facilitation functions (e.g., tools that allowed teachers to present content, conduct classroom management, and monitor students) provided by Immerse. The themes that emerged from the participants’ reflective discussions analysis also indicated mutual support for teacher agency development. Their responses to the questionnaire revealed that taking advantage of the teaching facilitation functions reflected the factors affecting learner agency as outlined in the CAMIL model. The findings of this study provided empirical evidence that VR environments can be a viable choice for teacher intercultural collaboration in which teachers of different backgrounds and experiences could together explore the potentiality of VR for teaching an additional language, sustain one another’s growth, and achieve teacher agency.