Computer-assisted Language Learning Research Articles

Investigating young consumers’ acceptance of hemp fiber textiles and apparel: an analysis of Partial Least Squares Structural Equation Modeling

Purpose Concerns over environmental degradation, alongside the global legalization of hemp cultivation, have prompted applications of hemp-derived textile products. However, research examining consumers’ acceptance of such products is lacking. It is critical to understand how young consumers adopt these products as they prioritize sustainability and lead the future purchasing power. Thus, this study aims to investigate young consumers’ perceptions, attitudes, and purchase intentions regarding hemp fiber textiles and clothing based on the Theory of Consumption Value and the Spiral of Silence Theory. Design/methodology/approach A US-based research company was hired to recruit survey participants, yielding 314 valid data. Data analysis, including Exploratory Factor Analysis and Partial Least Squares Structural Equation Modeling, was conducted using the R programming language and RStudio software. Findings Purchase intentions of hemp-based clothing and textiles among young consumers are influenced by attitude, perceived social value and future trend perception. Perceived social value also impacts attitude and future trend perception. High environmental involvement correlates with positive attitudes. Consumers’ subjective knowledge of hemp and objective knowledge regarding hemp fiber impact their perceptions of future trends in such products. Originality/value This research represents a seminal consumer study focusing on the resurgence of industrial hemp fiber textiles and clothing. This study empirically validates the significant role of social factors in young consumer acceptance of such products at both personal and societal levels. This study expands current knowledge and provides actionable insights for practitioners.

Technological Interface Components That Support Accelerated Learning in the Acquisition of Foreign Language Vocabulary

There is a need to find innovative learning methods that enable accelerated learning of a foreign language. This study examined the effect of computer-assisted language learning (CALL) in acquiring a foreign language, which combines cognitive and emotional stimuli in the background. The study explored two factors related to the acquisition of a foreign language: the duration and scope of the learning process and the depth of internalization of the newly acquired language. Another objective was to assess the learning method in two learning environments, 2D and VR, to determine if the learning environment affects the learning results and leads to better vocabulary retention. One hundred native French speakers, with an average age of 47.5, participated in the study and had no prior knowledge of the newly acquired language. We randomly divided the participants into two groups (2D and VR). They studied 550 words in a new language for five days: 30 min each evening and 15 min in the morning. The post-learning test pointed out that both groups improved their vocabulary scores significantly. Approximately one month after the learning experience, we administered a knowledge retention test to 32 participants and found that the level of knowledge had been retained. Finally, background variables (e.g., gender, age, and previous knowledge of the newly acquired language) did not affect the learning results. The findings indicate that CALL, which integrates background cognitive and emotional stimuli in both learning environments, significantly accelerates learning pace, broadens the scope of newly acquired words, and ensures retention. The level of improvement observed in our study is notably higher than that reported in the literature for studies that had previously evaluated CALL and in-class language acquisition.

Novel perspectives on early diagnosis of acute compartment syndrome: the role of admission blood tests.

The role of admission blood indicators in patients with acute compartment syndrome (ACS) remains debated. Our primary purpose was to observe variations of admission blood indicators in patients with ACS, while our secondary goal was to explore potential biomarkers related to ACS. We collected information on patients with tibial fracture between January 2013 and July 2023, and divided them into ACS and non-ACS groups. Propensity score matching (PSM) analysis was performed to lower the impact of potential confounding variables such as demographics and comorbidities. Admission blood indicators were analyzed using univariate, logistic regression, and receiver operating characteristic (ROC) curve analyses. Then, we established a nomogram prediction model by using R language software. After propensity PSM analysis, 127 patients were included in each group. Although numerous blood indicators were found to be relevant to ACS on univariate analysis, logistic regression analysis showed that monocytes (MON, p = 0.015), systemic immune-inflammation index(SII, p = 0.011), and creatine kinase myocardial band (CKMB, p < 0.0001) were risk factorsfor ACS. Furthermore, ROC curve analysis identified 0.79×109/L, 1082.55, and 20.99U/L as the cut-off values to differentiate ACS patients from patients with tibial fracture. We also found that this combination had the highest diagnostic accuracy. Then, we constructed a nomogram prediction model with AUC of 0.869 for the prediction model, with good consistency in the correction curve and good clinical practicality by decision curve analysis. We found that the levels of MON, SII, and CKMB were related to ACS and may be potential biomarkers. We also identified their cut-off values to separate patients with ACS from those with tibial fracture, helping orthopedists promptly evaluate and take early measures. We established a nomogram prediction model that can efficiently predict ACS in patients with tibial fracture.

Global burden of low back pain and its attributable risk factors from 1990 to 2021: a comprehensive analysis from the global burden of disease study 2021

BackgroundThis study aimed to examine the evolving trends in the global burden of low back pain (LBP) from 1990 to 2021 and predicted disease burden until 2035.Materials and methodsLBP-related data were obtained from the Global Health Data Exchange (GHDx) query tool. All estimates and their 95% uncertainty intervals (UIs) were generated using DisMod-MR 2.1, a Bayesian meta-regression tool in Global Burden of Disease, Injury, and Risk Factor Study (GBD) 2021. Data processing and visualization were conducted using The R Programming Language software version 4.3.2 and Joinpoint 4.7.ResultsIn 2021, approximately 628.8 million people worldwide were affected by LBP, with approximately 266.9 million new incident cases and age-standardized incidence rate (ASIR) of 3176.6 per 100,000. Compared with 1990, although the ASIR and age-standardized disability-adjusted life years rate (ASDALYsR) decreased, absolute numbers increased significantly. Projections for 2035 reveal a continued decline in ASIR and ASDALYsR for LBP. The LBP burden varied by the sociodemographic index quintile and GBD region, with the highest ASIR and ASDALYsR observed in Central Europe and the greatest decrease in East Asia. Globally, women bear a higher burden of LBP than men, with middle-aged populations experiencing the heaviest burden. Occupational ergonomic factors, high body mass index, and smoking remain the primary risk factors for LBP, with occupational ergonomic factors contributing the most to the overall burden.ConclusionDespite a projected decline in incidence, the global burden of LBP persists, exhibiting significant regional and gender disparities. To mitigate its future burden, precise and effective prevention and control strategies targeting high-risk factors are imperative.

Entrenching CALL for English Language Multicultural Pedagogy in Nigerian Secondary Schools: Issues, Prospects and Perceptions of Critical Stakeholders in Osun State, Nigeria

This paper examines the possibility of adoption of computer-assisted language learning (CALL) in Nigerian secondary schools especially those in Osun State, Nigeria in the light of recent developments necessitating greater familiarity with the computer by the students for their external examinations. The paper argues that secondary school students are more positioned now than ever to harness the benefits of CALL in developing necessary competences to excel in their English language exams and generally develop adequate proficiency in the language for their growth in the Nigerian nation and beyond. Data for the study comprise responses to questionnaires designed for secondary school students, English language teachers and Ministry of Education senior officials in Osun State Nigeria. The sample comprised 120 teachers across the three senatorial districts in Osun State, 36 teachers and 15 Ministry of Education officials. Data analysis revealed that there is great awareness of CALL among all the categories of subjects. In addition, the subjects emphasise the potential agency of CALL for enhancing the delivery of English language topics and the professional development of teachers, even though about 30 percent of the teachers sampled doubted its feasibility. The study therefore concludes that given the positive disposition of students to the introduction of CALL for English language teaching and being primary actors in the education process, there is need for immediate full-fledged adoption for English language teaching in Nigerian secondary schools.

Advancing language learning: The impact and challenges of Computer-Assisted Language Learning (CALL)

Abstract. Computer-Assisted Language Learning (CALL) represents a transformative approach in modern education, integrating technology to enhance language acquisition effectively. This comprehensive review examines the various dimensions of CALL, focusing on its deployment through interactive software, online courses, mobile applications, and the accompanying challenges. Quantitative analyses reveal significant improvements in learner engagement and cognitive load management due to multimedia and interactive content. Moreover, the adaptability of CALL to cater to individual learning needs through personalized and adaptive learning systems demonstrates a superior proficiency acquisition rate. Despite these advancements, CALL implementation faces substantial barriers including technological limitations, the need for extensive teacher training, and concerns regarding data privacy and security. This article assesses the statistical impact of these elements on language learning outcomes and suggests strategies for overcoming the associated challenges. Through mixed-methods research and longitudinal studies, the effectiveness of CALL platforms is substantiated, highlighting their pivotal role in reshaping language education across diverse demographic and geographic spectrums.

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&amp;lt;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 &amp; 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.

AI Concepts Integration in Developing E-Muhadathat Kits For Non-Arabic Speakers

Recent educational reforms focus on strategically integrating technology into teaching, aligning with the principles of the fourth industrial revolution (IR 4.0). Among the swiftly evolving technologies in language learning is Artificial Intelligence (AI), particularly in the form of Natural Language Processing (NLP). This technology has been applied to create the E-Muhadathat kit, an interactive Arabic conversation simulation tool designed to enhance communication skills for non-Arabic speaking students in Malaysian public universities. This study aims to investigate the role of AI in education, address the challenges in developing AI-based Arabic language learning software, and propose a model for the E-Muhadathat kit tailored for non-Arabic speakers. This literature review uses documentary methods to gather information from journals, conference proceedings, articles, dissertations, and digital books from databases such as Google Scholar, Springer Link, Science Direct, and Research Gate. The collected data undergoes descriptive analysis based on thematic categorization. The findings indicate that the concept of AI consists of three core components: machine learning, deep learning, and neural networks. Arabic linguists have recognized several challenges in developing AI-based software, such as orthographic, morphological, syntactic, anaphoric, and semantic ambiguities. Moreover, the E-Muhadathat kit's design integrates machine learning and deep learning techniques, featuring applications for both spoken and written language. The study's findings support educators and researchers aiming to create Arabic language software tailored for non-native speakers in Malaysian higher education institutions. As a result, the study advocates for further investigation into the use of AI in Arabic language education to enhance the communication abilities of non-native speakers in Malaysia.

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 &amp;lt; 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.