Computer-assisted Language Learning Research Articles

Assessing aortic morphology post aortic valve replacement using unsupervised hierarchical clustering and statistical shape modelling

Abstract Introduction There are different surgical procedures used to treat aortic valve disease such as aortic valve replacement (AVR), the Ozaki procedure, the Ross procedure, and the valve-sparing procedure. There may be postoperative side effects associated with aortic morphology. Computational analyses that assess morphology are thus necessary in such scenario. Statistical shape modelling is used to assess three-dimensional morphology, discover unique shape features, create mean shapes, and create shape modes that display morphological variability. Hierarchical cluster analysis is a machine learning method used to classify a population into subgroups. Both of these statistical methods were applied to aortic valve replacement patients to evaluate morphological variability after aortic valve replacement (AVR) surgery. Purpose The purpose of this study is to assess the aortic morphology of AVR patients, identify morphological differences between different surgical procedures, identify subgroups postoperatively using statistical shape modelling and hierarchical cluster analysis, and assessing possible correlations between the resultant clusters and function i.e. ejection fraction. Methods Computed tomography and cardiac magnetic resonance images were used to reconstruct aortas into 3D models using segmentation and 3D reconstruction software. For each patient, two models were created (i.e. ascending aorta only and aorta including the arch and the descending aorta). N=35 patients were included in the study. Statistical shape analysis was run to create templates and shape modes. Statistical environment and language software was used to run hierarchical cluster analysis. Results Overall, the hierarchical cluster analysis did not classify the aortas based on type of surgery, but most patients in the Ross group demonstrated morphological differences from the other surgical groups and many of the aortas belonging to the Ross group were clustered together in both clustering analyses. No significant differences between clusters were seen (p=0.47 for ascending, p=0.19 for whole aorta). No correlation was found between clusters and ejection fraction (p=0.75, r2<0.01), possibly indicating that post-AVR aortic morphology in this sample does not reflect overt functional changes. Conclusion Both statistical shape modelling and hierarchical cluster analyses were successful in assessing morphological variability and identifying subgroups in this population. This framework could therefore be extended to larger samples to inform on surgical decision-making allowing surgeons to choose the appropriate surgery for the patient or design prosthetic valves that are more customised to the patient’s anatomy.Cluster-Ejection Fraction Correlation

Teachers’ perceived perspective and selfefficacy in teaching EFL using Intelligent Computer-Assisted Language Learning (ICALL): A rigorous analysis

This study aims to describe Indonesian EFL teachers’ perceived perspective and self-efficacy in EFL teaching using ICALL, reveal the prominent factor influencing each variable, and examine the effect of the perceived perspective on their self-efficacy. This study used a cross-sectional survey design with a quantitative approach. There were 284 Indonesian EFL teachers taught in secondary schools chosen using a voluntary sampling technique as the respondents. Data were obtained by employing a Perceptify-ICALL questionnaire with forty items using a six-point Likert’s scaling method (α = .839). Data were analyzed using descriptive statistics, hierarchical multiple regression, and MANOVA. Results showed that teachers’ perceived perspective was in a very high category, but not for their self-efficacy. The prominent factor influencing their perspective was the support and facilities provided by the school and government in succeeding EFL teaching using ICALL. Whereas the most influencing factor of their self-efficacy was their skills in operating computers and AI. Another finding was the fact that the teachers’ perceived perspective significantly affected teachers’ mastery experience. 2Pratama, Lestari, Kirkgöz & Imaniar: Intelligent Computer-Assisted Language Learning The JALT CALL Journal vol. 20 no.3 This study suggests that Indonesian EFL teachers’ perceived perspective in EFL teaching using ICALL is well constructed, but their efficacy is on the contrary due to lower skills in operating computers and AI-powered devices or programs. Future professional development programs in improving the teachers’ computer and AI-operation skills are suggested to be undertaken as well as bringing the ICALL-based approach to EFL learning.

Quantifying the impact of ASR-based instruction: What does the iSpraak platform learner data show?

Computer-assisted Pronunciation Training (CAPT) tools have become increasingly dependent on Automatic Speech Recognition (ASR) technology to provide automated corrective pronunciation feedback to learners. The extent to which ASR-based tools measurably improve second language (L2) pronunciation is of great interest to language educators globally, and Computer-assisted Language Learning (CALL) researchers. Studies to date have largely been conducted by research practitioners with small-to-medium sized samples at single institutions. The findings and conclusions drawn from such small-scale data collection might be significantly bolstered by analysing the vast stores of learner data from large CAPT platforms. This study is informed by a sizable eight-year dataset from iSpraak, an open-source pronunciation tool designed to model and evaluate L2 speech. Quantitative analysis of anonymised learner interactions with this application reveals significant gains in intelligibility measures across multiple languages. Results also suggest that the extent of ASR’s ability to improve learner pronunciation may be L2 dependent.

Exploring collaborative writing among large groups in online distance learning through a public forum and private chat tool

Abstract This study explored how collaborative writing, an often-used instructional strategy in second language (L2) learning, intersects with large-group dynamics, and investigated their potential impact on the quality of writing outcomes in an online distance learning course. Using a mixed-methods approach, the research scrutinized intra-group interaction processes in two large groups undertaking a computer-assisted language learning writing assignment and evaluated the impact of these interaction processes on their writing products. Data from discussions in both a public online forum and a private social communication platform (WeChat) were collected, systematically coded, and analysed quantitatively and qualitatively based on language functions. Data collection also included an assessment of the written products and follow-up group interviews. The findings indicate distinct interaction patterns between high-performing and low-performing groups, characterised by an expert/participant pattern and a dominant/passive pattern, respectively. Additionally, insights from the interviews shed light on these interaction patterns and the potential impact on student learning outcomes. The study suggests practical implications, highlighting the importance of task design in promoting high levels of collaborative knowledge construction to enhance students’ writing skills and L2 language learning in large-group settings.

Spatially explicit predictions using spatial eigenvector maps

Abstract In this paper, we explain how to obtain sets of descriptors of the spatial variation, which we call “predictive Moran's eigenvector maps” (pMEM), that can be used to make spatially explicit predictions for any environmental variables, biotic or abiotic. It unites features of a method called “Moran's eigenvector maps” (MEM) and those of spatial interpolation, and produces sets of descriptors that can be used with any other modelling method, such as regressions, support vector machines, regression trees, artificial neural networks and so on. The pMEM are the predictive eigenvectors produced by using a distance‐weighting function (DWF) in the construction of MEM. Seven types of pMEM, each associated with one of seven different DWFs, were defined and studied. We performed a simulation study to determine the power of different types of pMEM eigenfunctions at making accurate predictions for spatially structured variables. We exemplified the application of the method to the prediction of the spatial distribution of 35 Oribatid mites living in a peat moss (Sphagnum) mat on the shore of a Laurentian lake. We also provide an R language package called pMEM to make calculations easily available to end users. The results indicate that anyone of the pMEMs obtained from the different DWFs could be the best suited one to predict spatial variability in a given data set. Their application to the prediction of mite distributions highlights the capability of pMEMs for predicting distributions, and for providing spatially explicit estimates of environmental variables that are useful for predicting distributions.

B-352 Effects of COVID-19 on Breast Cancer Positivity: A Retrospective and Predictive Study

Abstract Background The COVID-19 pandemic has had a significant impact on the healthcare system, resulting in overall reduction in access to healthcare services, including the suspension of screening programs and postponement of surgeries and medical treatments. The aim of this study was to evaluate the effects of the pandemic on breast cancer positivity from cases received at a laboratory in São Paulo, Brazil. Methods A retrospective analysis of a database from a pathology laboratory in São Paulo, Brazil, was performed during the 2017-2023 period and classified as following: pre-pandemic (January 2017-February 2020), pandemic (March 2020 - April-2022), and post-pandemic (May 2022-December 2023). Cases were classified as negative or positive for malignancies. Positive anatomopathological examinations (breast biopsies and surgical specimens) considered primary, metastatic mesenchymal or endothelial malignancies. The Mann-Kendall test was used to evaluate the trend, and the PACF (Partial Autocorrelation Function) correlogram was used to analyze historical memory for definition of the autoregressive model. The augmented Dickey-Fuller hypothesis test was applied to analyze whether the series is stationary or not, and the first differences test was applied. The ARIMA model was used for the estimates (1,1,1). Statistics were performed using the gretl software and pymannkendall library in the Python language. Results A total of 74,973 samples were evaluated during the 2017-2023 period. In the three-year pre-pandemic period, 32,644 tests were recorded, with an average of 29.34% positive cases. From the analysis of the pre-pandemic time series, there was a positive trend towards an increase in cases (p < 0.0001, z=6.1360, Tau=0.6955). In a non-pandemic scenario, from March 2020 to February 2021, the number of new tests expected would be 11,535; however, in the pandemic period, only 8,904 were observed, a 22.81% reduction on expected cases. According to the historical average positivity rate in the pre-pandemic period, the expected and observed total were, respectively, 3,384 and 2,566 positive cases for malignancy. Conclusions This study reveals a significant decrease of approximately 22.81% in new breast anatomopathological diagnoses during the COVID-19 pandemic in a laboratory in São Paulo, Brazil. For comparison purposes, we identified a 24.17% of missed opportunities on diagnoses of new malignancy-positive cases, compared to the pre-pandemic period. These findings highlight the need for strategies to mitigate the negative effects of the pandemic on the early detection and treatment of breast cancer.

The Role of ChatGPT in English Language Learning: A Hedonic Motivation Perspective on Student Adoption in Chinese Universities

Recent advancements in artificial intelligence, particularly OpenAI’s ChatGPT, have transformed English language learning through Computer-Assisted Language Learning (CALL) tools. This study examined the adoption of ChatGPT among university-level English learners employing the Hedonic Motivation System Adoption Model (HMSAM). An online survey was conducted with 266 valid responses, analyzed using Structural Equation Modelling (SEM) to assess the key factors influencing ChatGPT adoption. According to the findings, the main factors influencing students’ inclination to utilize ChatGPT are perceived usefulness, boredom, and a sense of control. These findings highlight important considerations for integrating AI in English education, suggesting that practical AI tools must address both functional and motivational aspects of learning. The study offers insights for educators and developers in designing AI-driven language learning tools that align with students’ needs and motivations, potentially shaping the future of English education.

A Gamified Learning Environment (Moodle) to Enhance English Language Learning at University Level

This paper explores the use of gamification in Second Language Acquisition (SLA) within Computer-Assisted Language Learning (CALL). The aim is to investigate the effects of a gamified learning environment, specifically Moodle, on psychobehavioural factors and English learning achievement, and to discuss the affordances of gamification based on empirical findings, contributed both by the present study and previous research. For this purpose, a case study was conducted at the University of Andorra, where a treatment group completed a gamified English course on Moodle, and a control group did the same tasks without gamification. A mixed methodology was used to measure both affective and cognitive variables as well as students’ perceptions on their gamified learning experience. The results show that the treatment group outperformed the control group in Foreign Language Anxiety (FLA) and in Academic Motivation (AM). As for the effects on speaking fluency, they are inconclusive, which is consistent with previous literature. Consequently, the authors advocate for further research to explore the effects of gamification on actual learning in different learning disciplines, beyond students’ mere perceptions.

Exploring opportunities for language immersion in the posthuman spectrum: lessons learned from digital agents

Purpose Technologically-enhanced language education has shifted from computer-assisted language learning (CALL) to mobile-assisted language learning (MALL), including the use of conversational digital agents, and more recently, towards the use of generative artificial intelligence (AI) large language model (LLM) programmes for language learning purposes. This paper aims to explore the interplay between such posthuman communication and posthumanist applied linguistics, and between digital agents and human agency in response to the increasing permeation of AI in life and learning. Design/methodology/approach A core team of four researchers investigated how digital agents could be leveraged to support immersive target language learning and practice, focusing specifically on the conversational AI that pervaded digitally-mediated communication prior to the release of generative AI. Each researcher engaged in a digital autoethnography using conversational agents found in the digital wilds to learn a target second language via digital immersion. Findings Through qualitative data analysis of autoethnographic narratives using NVIVO, four key thematic codes characterizing the learning journeys emerged: context, language learning, posthuman engagement and technological parameters. The posthuman learning experiences conflicted with the multisensory, embodied and embedded ethos of posthumanist applied linguistics, indicating that informed human pedagogical agency must crucially be exercised to benefit from the learning potential of posthuman agents. Interactions with conversational agents did provide small-scale, just-in-time learning opportunities, but these fell short of immersive learning. Originality/value The methodology and findings offer a unique and valuable lens on the language learning potential of emerging LLM-based generative agents that are rapidly infusing conversational practices.

Effectiveness of Computer Application in Improving Reading Skills in Chinese Language and Towards Post-Attitudes as Home-based Learning

Background: As technology continues to advance, language acquisition methodologies have increasingly been integrated into computer-assisted language learning (CALL) systems. In the context of Chinese language acquisition, characters are structured in three phases: the overall character, its components, and its strokes. Proficiency in identifying and distinguishing individual characters hinges upon understanding their structural composition. Objective: This study aims to explore the impact of a computer application on Chinese language reading skills and students' perceptions regarding the utilization of computer applications for home-based learning. Method: A total of 60 students engaged in home-based learning using a designated computer application were involved in the study. Employing a quantitative approach with a quasi-experimental design, the study assessed the effects of the intervention. Results and Conclusion: Following one month of home-based learning through computer applications, participants exhibited higher gain scores in reading skill tests, albeit the differences were not statistically significant. Students expressed a preference for and perceived greater ease in acquiring reading skills through computer applications compared to traditional methods. Furthermore, the integration of computer applications in teaching and learning processes demonstrated the potential to enhance students' motivation, interest, and confidence in language acquisition, albeit without a notable impact on attention levels. The study underscores the pedagogical promise of Computer-Assisted Language Learning (CALL) and advocates for further research endeavors in this domain.

The predictive effects of motivation, enjoyment, and self-efficacy on informal digital learning of LOTE: Evidence from French and German learners in China

This article highlights the prevalent bias on global English in the existing informal digital language learning (IDLL) research and draws attention to informal digital learning of language other than English (IDL-LOTE) as a highly under-researched area in computer-assisted language learning. Recognizing how motivation, enjoyment, and self-efficacy serve as three crucial individual differences factors in second language (L2) development, the study sets out to examine the predictive effects of the three variables on IDL-LOTE. Drawing upon questionnaire data collected from 862 Chinese university students learning French or German as their LOTE, a structural equation modeling analysis was conducted. Our findings highlight that 1) promotion-focused motivation, enjoyment, and self-efficacy can significantly and directly influence IDL-LOTE, but prevention-focused motivation fails to generate such a direct impact; 2) enjoyment and self-efficacy can respectively act as a full mediator in the indirect effect of prevention-focused motivation on IDL-LOTE, and they can also partially channel the indirect influence of promotion-focused motivation on IDL-LOTE; 3) the specific language being learned (i.e., French or German) does not moderate the predictive effects of motivation, enjoyment, and self-efficacy on IDL-LOTE. These findings enable us to propose pedagogical implications and point out future research directions to advance the agenda of IDLL.

Effects of highlights and annotations on EFL learners’ Reading comprehension: an application of computer-assisted interactive reading model

This mixed-methods study investigates the effects of highlights and annotations on reading comprehension among Chinese EFL students majoring in teaching English as a foreign language, within a computer-assisted interactive reading model. The study involved 183 participants, randomly assigned to three experimental groups and a control group, each utilizing distinct combinations of highlights and annotations to evaluate their impact on reading comprehension. The results indicate that the intervention groups exhibited significant improvements in reading comprehension compared to the control group. Qualitative feedback from participants revealed increased engagement and metacognitive awareness, suggesting that these interactive features function as effective cognitive scaffolds. By facilitating information processing and reducing cognitive load, highlights and annotations enhance the reading experience and comprehension. These findings support cognitive load theory and underscore the pedagogical potential of integrating interactive elements into digital reading materials. This integration can promote active reading strategies and learner-centered instruction, offering valuable insights for educators and curriculum designers. By demonstrating the efficacy of technology-enhanced reading interventions, this research contributes to the broader field of Computer-Assisted Language Learning (CALL), emphasizing the role of interactive tools in improving language proficiency and academic success among EFL learners.

The Proteasome-Family-Members-Based Prognostic Model Improves the Risk Classification for Adult Acute Myeloid Leukemia.

Background: The accumulation of diverse molecular and cytogenetic variations contributes to the heterogeneity of acute myeloid leukemia (AML), a cluster of hematologic malignancies that necessitates enhanced risk evaluation for prognostic prediction and therapeutic guidance. The ubiquitin-proteasome system plays a crucial role in AML; however, the specific contributions of 49 core proteasome family members (PSMs) in this context remain largely unexplored. Methods: The expression and survival significance of 49 PSMs in AML were evaluated using the data from BeatAML2.0, TCGA, and the GEO database, mainly through the K-M plots, differential genes enrichment analysis, and candidate compounds screening via R language and statistical software. Results: we employed LASSO and Cox regression analyses and developed a model comprising three PSMs (PSMB8, PSMG1, and PSMG4) aimed at predicting OS in adult AML patients, utilizing expression profiles from the BeatAML2.0 training datasets. Patients with higher risk scores were predominantly found in the AML-M2 subtype, exhibited poorer ELN stratification, showed no complete remission following induction therapies, and had a higher mortality status. Consistently, significantly worse OS was observed in high-risk patients across both the training and three validation datasets, underscoring the robust predictive capability of the three-PSMs model for AML outcomes. This model elucidated the distinct genetic abnormalities landscape between high- and low-risk groups and enhanced the ELN risk stratification system. Ultimately, the three-PSMs risk score captured AML-specific gene expression signatures, providing a molecular basis for selecting potential therapeutic agents. Conclusions: In summary, these findings manifested the significant potential of the PSM model for predicting AML survival and informed treatment strategies.

Identification of Salt-Stress-Responding Genes by Weighted Gene Correlation Network Analysis and Association Analysis in Wheat Leaves.

The leaf is not only the main site of photosynthesis, but also an important organ reflecting plant salt tolerance. Discovery of salt-stress-responding genes in the leaf is of great significance for the molecular improvement of salt tolerance in wheat varieties. In this study, transcriptome sequencing was conducted on the leaves of salt-tolerant wheat germplasm CH7034 seedlings at 0, 1, 6, 24, and 48 h after NaCl treatment. Based on weighted gene correlation network analysis of differentially expressed genes (DEGs) under salt stress, 12 co-expression modules were obtained, of which, 9 modules containing 4029 DEGs were related to the salt stress time-course. These DEGs were submitted to the Wheat Union database, and a total of 904,588 SNPs were retrieved from 114 wheat germplasms, distributed on 21 wheat chromosomes. Using the R language package and GAPIT program, association analysis was performed between 904,588 SNPs and leaf salt injury index of 114 wheat germplasms. The results showed that 30 single nucleotide polymorphisms (SNPs) from 15 DEGs were associated with salt tolerance. Then, nine candidate genes, including four genes (TaBAM, TaPGDH, TaGluTR, and TaAAP) encoding enzymes as well as five genes (TaB12D, TaS40, TaPPR, TaJAZ, and TaWRKY) encoding functional proteins, were identified by converting salt tolerance-related SNPs into Kompetitive Allele-Specifc PCR (KASP) markers for validation. Finally, interaction network prediction was performed on TaBAM and TaAAP, both belonging to the Turquoise module. Our results will contribute to a further understanding of the salt stress response mechanism in plant leaves and provide candidate genes and molecular markers for improving salt-tolerant wheat varieties.

Integration of Computer-Assisted Language Instruction in English Language Teaching of Oral Skills among Secondary School Learners

Integration of Computer-Assisted Language Instruction in English Language Teaching of Oral Skills among Secondary School Learners

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.

Online language teaching in times of change: a CALL to action for language teachers and educators

Abstract Change, whether slowly developing over decades or rapid change caused by worldwide crises, affects education and educators profoundly. This article argues for a change in language teacher education embracing the changing world of online communication, and emphasising the epistemological awareness language teachers need to bring to the online classroom. Based on Computer-Assisted Language Learning (CALL) research and best practice examples from across the world, the paper outlines past changes, contextualises online language teaching in the wider area of online communication and describes progress in practice, theory and epistemological understanding of online teaching. It culminates in a series of evidence-based suggestions for future training and education of language teachers that will integrate adequate pedagogy and an enhanced epistemological understanding.

An interactive R-based custom quantification program for semi-quantitative analysis of triacylglycerols in bovine milk.

The R programming language, RStudio, and open-source software solutions for analysis of liquid chromatography-mass spectrometry (LC-MS) data have been used with user-written R-based custom quantification programs (CQP) for semi-quantification of triacylglycerols (TAGs) in bovine milk lipid extracts. Using the peak-finding capabilities of the package "xcms" in RStudio, peaks were integrated, and retention times aligned, normalized, and then used for semi-quantitative analysis of a custom set of four extraction internal standards (EISs) and 29 TAG regioisomers using the choice of four analytical internal standards (AISs). Alternating stereospecific numbering (sn) 1,3 TAG regioisomers (standards 1, 3, and 5 of six calibration standards) and sn-1,2 TAG regioisomers (standards 2, 4, and 6 of six standards) were used to make a set of six calibration standards, which were used for quantification using a linear fit model, polynomial fit model, power fit model, level-bracketed linear fit, replicate-bracketed polynomial fit, replicate-bracketed power fit, and replicate- and level-bracketed linear fit and response factors. For example, the linear fit for EIS1 gave an unacceptable coefficient of determination (CoD), r2 = 0.9616, whereas the polynomial fit gave r2 = 0.9908 and the power fit gave r2 = 0.9928, while the double-bracketed linear fit gave CoDs of r2 = 0.9960, 0.9848, and 0.9781 for the three brackets, yet gave the least % difference to known calibration concentrations. For unparalleled transparency, the CQP produced webpages that allowed every step in the data processing and quantification sequence to be verified and reproduced, and contained interactive figures. The data are publicly available using a digital object identifier (DOI). The R code can be downloaded and used with the downloadable data to reproduce the results, to modify the code and further customize the results, or to copy and paste and adapt the code to other quantification applications.

Prognostic value of PEA3 subfamily gene expression in cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a lethal malignancy with limited treatment options and poor prognosis. The PEA3 subfamily of E26 transformation specific genes: ETV1, ETV4, and ETV5 are known to play significant roles in various cancers by influencing cell proliferation, invasion, and metastasis. To analyze PEA3 subfamily gene expression levels in CCA and their correlation with clinical parameters to determine their prognostic value for CCA. The expression levels of PEA3 subfamily genes in pan-cancer and CCA data in the cancer genome atlas and genotype-tissue expression project databases were analyzed with R language software. Survival curve and receiver operating characteristic analyses were performed using the SurvMiner, Survival, and Procr language packages. The gene expression profiling interactive analysis 2.0 database was used to analyze the expression levels of PEA3 subfamily genes in different subtypes and stages of CCA. Web Gestalt was used to perform the gene ontology/ Kyoto encyclopedia of genes and genomes (GO/KEGG) analysis, and STRING database analysis was used to determine the genes and proteins related to PEA3 subfamily genes. ETV1, ETV4, and ETV5 expression levels were significantly increased in CCA. There were significant differences in ETV1, ETV4, and ETV5 expression levels among the different subtypes of CCA, and predictive analysis revealed that only high ETV1 and ETV4 expression levels were significantly associated with shorter overall survival in patients with CCA. GO/KEGG analysis revealed that PEA3 subfamily genes were closely related to transcriptional misregulation in cancer. In vitro and in vivo experiments revealed that PEA3 silencing inhibited the invasion and metastasis of CCA cells. The expression level of ETV4 may be a predictive biomarker of survival in patients with CCA.

Factors impeding implementing CALL-based instruction: A mixed-methods study

As an innovative approach to language pedagogy, Computer-Assisted Language Learning (CALL) research has been gaining traction in recent decades. However, the novel incorporation of technology in the classroom context presents some unique challenges unprecedented in conventional language learning contexts. The present study is an attempt at identifying the factors prohibiting the successful implementation of CALL, with particular attention on the context of Iran. To this end, an exploratory mixed-methods sequential design was adopted. Fifteen Iranian EFL instructors were interviewed and the data were analyzed through thematic analysis. Built upon the findings of the interviews, a questionnaire was developed to assess the generalizability of the interview findings. The 33-item questionnaire was developed and distributed online among 200 EFL teachers. It was revealed that certain factors, including EFL teachers and learners' knowledge of computers, cultural and religious background, institutional support policy, and human factors such as learners' learning style and age could hinder the successful incorporation of CALL in language learning contexts. Principal Component Analysis (PCA) confirmed the generalizability of the identified prohibitive factors. The findings are discussed under the light of Rogers’ (2003) Diffusion of Innovation Theory (DIT) to further contextualize the implications of the study for macro- and micro-level policymakers, curriculum developers, and language teachers.