Model Of Language Learning Research Articles

The impact of linguistic background on the nature of classroom dyadic interactions: evidence from L2 and L3 learners of English as a foreign language

ABSTRACT Framed within the perspective of Vygotskyan sociocultural theory and models of third language learning, this study examined the impact of differing linguistic backgrounds and learning experience on the nature of dyadic interaction during task completion in the divide between true L2 and L3 learners. It was conducted in an EFL classroom with eight leaners of English as first and eight learners of English as second foreign language after French. Their measured English proficiency corresponded to A2 level. They were presented with two similar grammar-based tasks on sessions 11 and 12 with different pairing configurations and an isomorphic version of both tasks on session 13 to be completed individually. Data from pair talks and individual performances were analyzed in three stages to: a) find the major factors distinguishing the quality of interactions, b) compare the quality of L2ers and L3ers’ interactions when they were matched or mixed, and c) examine the learning benefits of the interactions across L2ers and L3ers in terms of transferring knowledge from interactions to their subsequent individual productions. The findings indicated a role for linguistic background in mediating the nature of pair interactions and shaping dyadic relationships by impacting L2ers and L3ers’ perception of each other’s proficiency.

Indonesian EFL students’ views on their explicit and implicit language aptitude profiles, TOEFL scores and language proficiency.

This study is designed to find out students’ views on their explicit and implicit language aptitude profiles, TOEFL scores and language proficiency. Based on purposive sampling scheme, the data were collected from sixty-five students in the end of their eighth semester at the English department in faculty of teacher training in UIN Raden Fatah Palembang. The students were asked to respond to the five points Likert scale questionnaire which consists of four parts: Part A elicits the participants’ demographic information, Part B on language experience, Part C on language aptitude, and Part D especially on TOEFL and language proficiency. The findings revealed that language learning success is attributed to a number of individual factors. The individual factors related to foreign language learning can be divided into affective factors (e.g., motivation, attitude, and personality) and cognitive factors (e.g., intelligence, aptitude). The cognitive factors interact with affective factors for learning a particular language which may explain why a person is better able to learn a language over another language. Thus, a full model of language learning that considers the impact of language aptitude on learning should also investigate the combined mediating role of individual differences such as motivation, anxiety, and learner’s beliefs.

ACAT-G: An Interactive Learning Framework for Assisted Response Generation

In this paper, we introduce ACAT-G, an interactive dialogue learning framework that incorporates constant human feedback into fine-tuning language models in order to assist conditioned dialog generation. The system takes in a limited amount of input from a human and generates personalized response corresponding to the context of the conversation within natural dialog time-frame. By combining inspirations from online learning, reinforcement learning, and large scale language models, we expect this project to provide a foundation for human-in-the-loop conditional dialog generation tasks.

Examining English Language Learning Apps from A Second Language Acquisition Perspective

This study set out to explore dedicated language learning apps pedagogically while focusing mainly on aspects of second language acquisition. A total of 20 English language learning apps were collected for analysis. The study took one model of analysing course book materials and another, computer-assisted language learning model and combined them into one analytical framework with bespoke criteria, ensuring the analysis was most suitable for our case. The analytical framework which was developed reached a number of conclusions about dedicated language learning apps (DLLAs). The findings revealed that DLLAs tend to provide mechanical forms-focused practice without facilitating collaborative learning nor focusing on developing users’ communicative competence, which suggests that DLLAs reflect a behaviouristic view of language learning. The conclusion offers some suggestions to improve DLLAs and proposes that, for the time being, educators should look beyond DLLAs and instead investigate how can apps that are not designed for language learning (generic apps) be used in the manner of DLLAs to avoid the issues that this paper identifies with them.

Rise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic

Social media (e.g., Twitter) has been an extremely popular tool for public health surveillance. The novel coronavirus disease 2019 (COVID-19) is the first pandemic experienced by a world connected through the internet. We analyzed 105+ million tweets collected between March 1 and May 15, 2020, and Weibo messages compiled between January 20 and May 15, 2020, covering six languages (English, Spanish, Arabic, French, Italian, and Chinese) and represented an estimated 2.4 billion citizens worldwide. To examine fine-grained emotions during a pandemic, we built machine learning classification models based on deep learning language models to identify emotions in social media conversations about COVID-19, including positive expressions (optimistic, thankful, and empathetic), negative expressions (pessimistic, anxious, sad, annoyed, and denial), and a complicated expression, joking, which has not been explored before. Our analysis indicates a rapid increase and a slow decline in the volume of social media conversations regarding the pandemic in all six languages. The upsurge was triggered by a combination of economic collapse and confinement measures across the regions to which all the six languages belonged except for Chinese, where only the latter drove conversations. Tweets in all analyzed languages conveyed remarkably similar emotional states as the epidemic was elevated to pandemic status, including feelings dominated by a mixture of joking with anxious/pessimistic/annoyed as the volume of conversation surged and shifted to a general increase in positive states (optimistic, thankful, and empathetic), the strongest being expressed in Arabic tweets, as the pandemic came under control.

The effect of perceived competence on second language communication frequency: the mediating roles of motivation, willingness to communicate, and international posture

The primary purpose of this study was to investigate the mediating effects of second language (L2) motivation, willingness to communicate (WTC), and international posture on the relationship between L2 Self-perceived communication competence (SPCC) and L2 frequency of communication (L2 FC) in online informal English learning contexts among tertiary students in Malaysia. The predictor variables were drawn from Gardner’s (1985) socio-educational model of language learning and MacIntyre and Charos’ (1996) WTC model and adapted to examine the frequency of communication in online contexts. A total of 296 valid responses were collected from university students in Malaysia. The data were collected using online survey and were analysed through partial least squares structural equation modelling (PLS-SEM) technique. The findings suggested that higher level of L2 SPCC would lead to more frequent L2 use in online contexts. Furthermore, the mediating roles of L2 WTC and international posture were found to be statistically significant. However, the mediating effect of L2 motivation on the relationship between L2 SPCC and communication frequency was not supported in this study. Implications of the research findings for understanding the relationship among the study’s variables are also discussed.

Deep Learning Language Model and Chinese Grammar - Focusing on the Prediction Model of Directional Complements using BERT

Deep Learning Language Model and Chinese Grammar - Focusing on the Prediction Model of Directional Complements using BERT

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

THE EFFECTIVENESS OF TEACHING MODULE COMPUTER BASED DRILLING MODEL AND PRACTICE IN IMPROVING LEARNING ACHIEVEMENT OF INDONESIAN LANGUAGE

This study aims to determine the effectiveness of the computer-assisted learning module for drill and practice model of Indonesian language learning in SMA/MA East Lombok District. This research includes development research through the stage of requirement analysis, limited sample testing and testing of action research settings. The research subject consists of teachers and SMA/ MA students in East Lombok district. Research Data is collected via questionnaire, observation sheets, tests, and interview guidelines. Data was analyzed using the component's anáolysis technique and continued with test-t analysis. The results showed that (1) th majority of teacher prepare the teaching materials independently than work collaboratively; (2) The result of learning Indonesian language through the use of a computer-assisted drill and practice based learning Module at each cycle increases, (3) There are differences in the results of Indonesian language learning between students who have been taught using a computer-assisted learning module drill and practice model with students who are taught without using a computer-assisted learning Module drill and practice model, and (4) students ' responses to learning modules developed and are in a good category.

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation.

BackgroundPatient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.ObjectiveThis study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.MethodsClinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.ResultsAmong 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.ConclusionsAutomated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

WITHDRAWN: A study on the role of natural language processing in the healthcare sector

Abstract During the COVID-19 pandemic, Natural Language Processing (NLP) plays a vital role in detecting the disease, stabilizing the intensive care, discovering the drugs and controlling the spreading of infections. It energizes chat programs to assist in the reduction of outbreaks during the initial stages of coronavirus infection. NLP technologies have reached new heights in terms of utility, and are at the heart of the success of a multilingual conversation system, Chatbots, and Deep learning language models. It supports a total of 13 languages around the world. NLP powered AI such as Health map and Cobweb platforms track patient requests and perform incident detections. This chapter looks at the role of NLP and its technologies, challenges, and future possibilities using AI and machine learning for crisis mitigation and easier EHRs in the health care industry.

Examining Teachers’ Beliefs on Developing a Digital Pedagogical Framework Based on the SAMR Model for Undergraduate English Language Learning

Nowadays, technology is considered as an integral rather than a supplementary aspect of high-quality education within the area of TESOL. Accordingly, English language teachers need to be equipped with effective ways to integrate technology into instructional practices not only for enhancing academic outcomes but for confronting the challenges of dealing with the digital native students as well. The purpose of this case study is to examine teachers’ beliefs on technology integration into pedagogy based on the measurement of the SAMR (Substitution, Augmentation, Modification, and Redefinition) model. Results of data analysis show teachers’ high frequency of technology integration at the Substitution and Augmentation levels compared to the advanced levels of Modification and Redefinition. The study resulted in a recommended SAMR based framework that may help English language teachers move towards effective technology integration that may lead to a more effective learning environment.

Multimedia with Social Learning Networks (SNL): As Learning Innovation in the 4.0 Industrial Era

Technological advances have opened the door to present instructional material in various forms such as audio, visual, printed text, images, and moving images. All types of multimedia are found in one computer, which has opened the way to present learning more effectively. In addition, there were also many networking sites specifically used in learning to facilitate online learning. However, to integrate technology in learning cannot be adopted directly, it is necessary to innovate and adapt to the curriculum and learning. Based on that, it is necessary to develop a language learning model based multimedia using Social Lerning Network (SNL). This type of research is Research & Development (R&D) with a 4-D development model (define, design, develop, and disseminate). The results showed that the learning model developed was categorized as very valid and could be tested in schools.

Deep Entity Matching

Entity matching refers to the task of determining whether two different representations refer to the same real-world entity. It continues to be a prevalent problem for many organizations where data resides in different sources and duplicates the need to be identified and managed. The term “entity matching” also loosely refers to the broader problem of determining whether two heterogeneous representations ofdifferent entitiesshould be associated together. This problem has an even wider scope of applications, from determining the subsidiaries of companies to matching jobs to job seekers, which has impactful consequences.In this article, we first report our recent system DITTO, which is an example of a modern entity matching system based on pretrained language models. Then we summarize recent solutions in applying deep learning and pre-trained language models for solving the entity matching task. Finally, we discuss research directions beyond entity matching, including the promise of synergistically integrating blocking and entity matching steps together, the need to examine methods to alleviate steep training data requirements that are typical of deep learning or pre-trained language models, and the importance of generalizing entity matching solutions to handle the broader entity matching problem, which leads to an even more pressing need to explain matching outcomes.

Multi-Domain Aspect Extraction Using Bidirectional Encoder Representations From Transformers

Deep learning and neural language models have obtained state-of-the-art results in aspects extraction tasks, in which the objective is to automatically extract characteristics of products and services that are the target of consumer opinion. However, these methods require a large amount of labeled data to achieve such results. Since data labeling is a costly task, there are no labeled data available for all domains. In this paper, we propose an approach for aspect extraction in a multi-domain transfer learning scenario, thereby leveraging labeled data from different source domains to extract aspects of a new unlabeled target domain. Our approach, called MDAE-BERT (Multi-Domain Aspect Extraction using Bidirectional Encoder Representations from Transformers), explores neural language models to deal with two major challenges in multi-domain learning: (1) inconsistency of aspects from target and source domains and (2) context-based semantic distance between ambiguous aspects. We evaluated our MDAE-BERT considering two perspectives (1) the aspect extraction performance using F1-Macro and Accuracy measures; and (2) by comparing the multi-domain aspect extraction models and single-domain models for aspect extraction. In the first perspective, our method outperforms the LSTM-based approach. In the second perspective, our approach proved to be a competitive alternative compared to the single-domain model trained in a specific domain, even in the absence of labeled data from the target domain.

Lifelong Language Learning With the Most Forgotten Knowledge

Lifelong language learning enables a language model to accumulate knowledge through training on a stream of text data. Recent research on lifelong language learning is based on samples of previous tasks from an episodic memory or generative model. LAMOL, a representative generative model-based lifelong language learning model, preserves the previous information with the generated pseudo-old samples, which are suboptimal. In this paper, we propose an improved version of LAMOL, MFK-LAMOL, which constructs a generative replay using a more effective method. When a new task is received, MFK-LAMOL replays sufficient previous data and retrieves important examples for training alongside the new task. Specifically, it selects the examples with the most forgotten knowledge learned from previous tasks based on the extent to which they include knowledge that has been forgotten after learning new information. We showed that the proposed method outperforms LAMOL on a stream of three different natural language processing tasks.

MODEL PENGEMBANGAN DAN EVALUASI PEMBELAJARAN BAHASA INDONESIA DI MTs KARANGKAJEN

This study describes the development and evaluation model of learning in Indonesian language lessons at MTs Karangkajen. The research objective was to determine the model of development and evaluation of learning to improve the quality of students towards Indonesian language lessons. This type of research uses descriptive qualitative research. Methods of data collection using interviews, observation and documentation. Data analysis with three stages: reduction, presentation of data and concluding. The research results found 1) the Indonesian language learning development model at MTs Karangkajen using the reading, listening, writing and speaking learning model, 2) the evaluation of the Indonesian language learning model at MTs Karangkajen was carried out based on daily tests, midterm tests, quizzes fast, as well as the results of discussions and questions and answers to students. The school principal conducts class visits and reports on learning outcomes at the end of the semester by the teacher as an evaluation of learning to increase student ability and achievement in Indonesian.

KEEFEKTIFAN METODE CONTEXTUAL TEACHING AND LEARNING DALAM MENULIS NARASI DI SEKOLAH DASAR

The purpose of this study was to find out whether the CTL model was effective in improving the narrative essay writing skills of grade 6 elementary school students. The research design used was a Quasi experimental study. Based on the results of research conducted, it is known that the pretest data of the experimental and control classes are normally distributed and homogeneous. The pretest t-value is smaller than the t-table price (-0.081 <1.647) and the significance (0.935> 0.05), meaning that HO is accepted. This shows that there is no difference in the average score of narrative essay writing skills between the control class and the experimental class at the time of the pretest. Posttest t-count value is greater than t-table price (2.153> 2,000) and significance (0.036 <0.05). Ha was accepted and HO was rejected. Ha accepted means that there is a difference in the average score of narrative essay writing skills between the control class and the experimental class, ie the average experimental class is higher with a mean difference of 0.1552. Large increase in the experimental class can be seen in the average normalized gain of 0.357 or included in the medium category, while the control class 0.209 included in the low category. The effectiveness of the CTL model is expected to be a consideration for grade 6 teachers to apply the CTL model to Indonesian language learning with other aspects or material. The application of the CTL model is expected to be one of the innovative models for effective Indonesian language learning

Model Arabic Language Teaching for Islamic Senior High Schools/ نموذج إدارة تعليم اللغة العربية للمدارس الثانوية

Management is the organizational process that involves planning, organizing, leadership and controlling. The objectives of this study were to describe the planning model, the instructional leadership model, and explain the quality control model of management of learning Arabic in MA Nuris 1 Jember, MANPK Jember, MA Al Qodiri 1 Jember. The research approach used is a qualitative approach with a type of case study with a multisite design. The analysis technique uses individual case analysis and cross-case analysis. The validity of the data in this study uses a test of credibility, transferability, dependability, and conformability. The findings of this study are (1) Arabic language learning planning model is integrated models, which begins environmental analysis with standardization of caregivers and quality assurance. Planning is developed through the integration of the K13 curriculum with the pesantren curriculum, integration of madrasah Arabic learning with Islamic boarding schools referring to technical instructions from the ministry of religion, and student exchange programs. (2) Instructional leadership model in Arabic language learning is participative delegatif and non participative delegatif, with. The socialization of the vision, mission, and objectives are carried out by the quality assurance team with the aim of the madrasa community, parents of students and the community both directly and using print and electoral media. Both learning management is carried out centrally with a hierarchical system and a specialization system developed by the teacher in a centralized manner with various models that refer to boarding learning technical guidelines and guidebooks, thirdly constructing a climate of learning Arabic using a modern education system and time compaction. (3) The first Arabic language quality control model, input quality control students memorized Taqrib 250 verses and Imriti 250 verses, able to speak Arabic and English, have memorized Al-Quran, MTs, and pesantren. Input teacher master Arabic, pesantren alumni, can read books and teachers built by Dalwa Islamic Boarding School. Second, control of the learning process specifically for santri mukim and santri kalong, full-day school-based Islamic boarding schools, and supported by Arabic language olympiad, student exchanges, LPBA and Yaumul Arabi and involving, systems, media and personnel. Third, control of output based learning IT, supported by Khitobah, Yaumul Arabiy and weekly, monthly and semester evaluations. Based on the findings above, the formal findings of this research are the integrative delegated management model of Arabic language learning in Aliyah madrasas based Ma'had- can improve the quality of students in mastering all Arabic language skills.

Functional organisation for verb generation in children with developmental language disorder

Developmental language disorder (DLD) is characterised by difficulties in learning one's native language for no apparent reason. These language difficulties occur in 7% of children and are known to limit future academic and social achievement. Our understanding of the brain abnormalities associated with DLD is limited. Here, we used a simple four-minute verb generation task (children saw a picture of an object and were instructed to say an action that goes with that object) to test children between the ages of 10–15 years (DLD N = 50, typically developing N = 67). We also tested 26 children with poor language ability who did not meet our criteria for DLD. Contrary to our registered predictions, we found that children with DLD did not have (i) reduced activity in language relevant regions such as the left inferior frontal cortex; (ii) dysfunctional striatal activity during overt production; or (iii) a reduction in left-lateralised activity in frontal cortex. Indeed, performance of this simple language task evoked activity in children with DLD in the same regions and to a similar level as in typically developing children. Consistent with previous reports, we found sub-threshold group differences in the left inferior frontal gyrus and caudate nuclei, but only when analysis was limited to a subsample of the DLD group (N = 14) who had the poorest performance on the task. Additionally, we used a two-factor model to capture variation in all children studied (N = 143) on a range of neuropsychological tests and found that these language and verbal memory factors correlated with activity in different brain regions. Our findings indicate a lack of support for some neurological models of atypical language learning, such as the procedural deficit hypothesis or the atypical lateralization hypothesis, at least when using simple language tasks that children can perform. These results also emphasise the importance of controlling for and monitoring task performance.