Model Of Language Learning Research Articles

Improved outcomes from reflex comprehensive genomic profiling-guided precision therapeutic selection across a major US healthcare system.

6622 Background: An ever-increasing number of biomarker-guided therapies, some with pan-cancer indications have expanded the oncologist’s toolbox for fighting advanced cancer. Despite this, not all advanced cancer patients receive tumor genomic testing, or are tested for a limited number of targets. Still others are tested too late in their care journey to benefit from precision therapy. To assess the impact of removing testing barriers, we developed a reflex testing protocol where comprehensive genomic profiling (CGP) was routinely ordered by pathologists at time of diagnosis for advanced cancer patients. Methods: Reflex CGP testing was primarily initiated by the pathologist at the time of advanced cancer diagnosis. Testing was performed between 2019 and 2021 via CGP using the ProvSeq 523 lab-developed test, and testing was performed at no cost to the patient. Post-CGP, stage 4 patients were followed for ≥ 12 months. We assessed time to therapy, therapy selection, and overall survival (OS). Therapies were stratified by presence of biomarkers associated with approved targeted therapies (TT), presence of biomarkers for immunotherapies (IO), and/or therapies that were guideline based (GB) and not associated with a specific biomarker. As much key patient information is only consistently available in free-text medical charts, we implemented a novel natural-language processing (NLP) approach based on deep learning and large language models to accelerate abstraction. Results: A cohort of 1,423 advanced cancer patients met the study criteria. The median age was 66 years, 53% were female, and 82% white. The 3 most tested tumor types were 22% non-small cell lung cancer, 16% colorectal cancer, and 12% breast. Overall, 49% (N=704) of patients had a biomarker result considered actionable for an approved TT or IO. Median (IQR) time-to-treatment initiation post-CGP was 19 (2-70) days. In patients with no actionable TT or IO biomarkers (N=719), 63% were treated with chemotherapy-based regimens, 11% with GB, 17% with unmatched TT, and 8% with unmatched IO. Of patients who had only an actionable TT biomarker (N=287), 18% received matched TT and 13% received GB. 36% of patients with only an actionable IO biomarker (N=317) received matched IO monotherapy. 48% of patients with both actionable TT and IO biomarkers (N=100) received matched TT or IO monotherapy, and 5% received GB. Across all tumor types, patients receiving a TT had better OS compared to patients receiving chemotherapy with 12 months OS (%) of 70.1 (95% CI=64.4 - 76.3) for TT-treated patients, compared to 62.9 (95% CI=58.8 - 67.3) for chemotherapy only. Conclusions: CGP-guided precision therapy use is associated with significantly higher survival in a reflex testing population. A reflex protocol can overcome key barriers to the use and timing of genomic testing to improve access to these life-extending treatment modalities.

Sign Language Detection without Sensors

bstract: The time period time device assists communication between the hearing impaired and the general public. We are proud to present the design and implementation of language learning models. In most cases, we create a trust model that distributes signatures regularly. Also, this method is a good way for beginners to use sign language to practice the language. As part of this study, various human-computer interactions for gesture recognition were investigated and analyzed. It has been found that the best way to detect human movement is layered image processing. Even with very little background, the system can detect certain characters in the language with 70-80% accuracy.

Coming out of the shadows: investing in English private tutoring at a transition point in Kazakhstan’s education system during the global pandemic

ABSTRACT Unequal access to language learning resources has been exacerbated by the global expansion of English private tutoring (EPT). Despite its popularity, no study has examined the implications of EPT during the COVID-19 pandemic. Therefore, this mixed-methods study explored the nature and effectiveness of EPT that first-year Kazakhstani undergraduate students had experienced over the previous 12 months during the COVID-19 pandemic. It was informed by Benson’s (2011) model of language learning beyond the classroom. Data were collected through a close-ended questionnaire and semi-structured online interviews. The study found that 318 out of 750 (42.4%) had experienced EPT, and 64% of respondents had received face-to-face EPT although it was considered a health risk during the pandemic. All the interviewees perceived EPT sessions as an encouraging environment for coaching towards the university entrance examination and expanding their knowledge. They attributed this mainly to the individual attention they obtained from their tutors, which was lacking in online classes with their English teachers due to teachers’ indifferent attitude to students’ questions and the limited duration of video conferencing sessions. The participants acted agentively by evaluating the advantages and drawbacks of online EPT. Pedagogical implications and areas for further research are suggested.

MEMBANGUN KESADARAN KRITIS BAHASA JAWA MELALUI MODEL PEMBELAJARAN PEMAKNAAN DI SEKOLAH DASAR

Until now, learning Javanese from elementary school to high school is only a teaching routine, namely only providing material according to the curriculum. This study aims to develop a learning model of meaning to build students' critical awareness in Javanese. This research refers to the 4-D development model. The research subjects were public elementary school students in Madiun City. Data were collected by observation techniques, tests, questionnaires, and data analysis using descriptive statistical techniques. The study results showed that the Javanese language learning models and tools researched and developed are valid, practical, and effective and can increase critical awareness of moral sensitivity, thinking skills, and mastery of concepts in elementary school students. The meaning learning model combines problem-based learning with ethical aspects of learning that can increase students' critical awareness of the Javanese language.

Model Word Square : Konsep Simantik Siswa Mengenal Mufrodat pada Pembelajaran Bahasa Arab

This study aims to see the concept of the word square model presented by the teacher to students as an effort to get to know mufrodat in learning Arabic. This study uses a qualitative approach to the type of case study. This research is located at MI Ar-Rofi'iyah, Semampir Village, Kraksaan District, Probolinggo, East Java. The source of the data was obtained through observation and interviews with 3 informants, namely the head of the Madrasah, the fifth grade teacher and the Arabic teacher. The data analysis used is owned by Milles and Hubberman namely data reduction, data display and data verification. The results of the study stated that MI Ar-Rofi'iyah applying the word square model was able to introduce students to various mufrodats and their meanings properly and correctly. The Arabic language learning model with word square is modified with 4 learning activities, namely istima', takrar, bahtsul kalimah, and tashih. Not only that, this learning model makes it easier for students to practice concentration, be observant and careful in choosing and finding answers. It can be said that this research is a new way of modifying the learning model so that it is not stuck in a boring learning concept for students by adjusting to the characteristics of students and learning needs will make it easier for teachers and students to quickly achieve learning goals.

Unveiling the Thematic Landscape of Generative Pre-trained Transformer (GPT) Through Bibliometric Analysis

Introduction: the Generative Pre-trained Transformer (GPT) is a deep learning language model architecture developed by OpenAI. Aim: to describe the knowledge networks (both at the theoretical and country levels) of the Generative Pre-trained Transformer (GPT) as an emerging technology. Results: 222 Documents were identified, of which 69 were articles, 50 were conference papers, 36 were editorials, 29 were notes, 19 were letters, 14 were reviews, 3 were conference reviews, and 2 were short surveys. In terms of the number of documents per year, 2 were found in 2019, 10 in 2020, 22 in 2021, 44 in 2022, and 144 in 2023. The year-on-year growth rate was over 100 % in all years. The subject area with the highest number of documents was Computer Science with 90 documents. The most productive countries in relation to GPT were the United States with 60 documents, followed by China with 19, the United Kingdom with 18, India with 15, and Australia with 12. Co-occurrence illustrated the centrality of Artificial Intelligence, Natural Language Processing, Deep Learning, and the term Human around ChatGPT and GPT. Conclusions: this bibliometric study aimed to describe the knowledge networks of the Generative Pre-trained Transformer (GPT) as an emerging technology. Although only 222 documents were found, this study revealed a high level of international scientific collaboration in the field. The results suggest that GPT is a highly relevant technology with a wide range of potential applications in natural language processing, artificial intelligence, and deep learning. Moreover, the study was able to qualitatively characterize the main thematic areas surrounding GPT, including its applications in chatbots, text generation, machine translation, sentiment analysis, and more.

Error‐Correction Mechanisms in Language Learning: Modeling Individuals

AbstractSince its first adoption as a computational model for language learning, evidence has accumulated that Rescorla–Wagner error‐correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla–Wagner rule by using it to explain the behavior of participants across a range of tasks, we focus on testing predictions generated by the model in a controlled natural language learning task and model the data at the level of the individual learner. By adjusting the parameters of the model to fit the trial‐by‐trial behavioral choices of participants, rather than fitting a one‐for‐all model using a single set of default parameters, we show that the model accurately captures participants’ choices, time latencies, and levels of response agreement. We also show that gender and working memory capacity affect the extent to which the Rescorla–Wagner model captures language learning.

THE USE OF A COMPREHENSIVE INSTRUCTIONAL MODEL TO EVALUATE AND DEVELOP A TEACHING PRACTICE

The present paper demonstrates the possibility of using the new Comprehensive Instructional Model of Language Learning’s criteria in order to evaluate and develop a practice in a teaching context. This practice is one of the six practices of one language class session of two teaching hours. The evaluation and development of the practice are done on the basis of the three language learning components, input, competency building, and communicative acts. As a first step, we started with the description of the practice and then moved to the evaluation stage, which includes its internal comprehensiveness, how it fits in the learning sequence, and its discursive formation. In the preparatory technique of the practice, we started with the objectives, then the tools, and the practices that might be included within the same practice. The evaluation of the practice in terms of scoring is done on the basis of two different matrices, the internal comprehensiveness of the practice, and the discursive formation of the practice. The new comprehensive instructional model’s role is to detect the anomalies or any irregularities in the practice and find adequate answers to surmount them. It is a model that teaches teachers how to reconsider and develop their work without adopting any new teaching methodology.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0029/a.php" alt="Hit counter" /></p>

The language situation in the classroom and models of Bulgarian language learning in the initial stage

The article analyzes the role of the language situation in the classroom for learning the Bulgarian language. The characteristics of a bilingual and multilingual classroom are indicated, which have a key role in planning and organizing the language learning. Various normative documents are presented, which provide support for a smooth transition to Bulgarian language learning at the primary stage for students with a different native /family language, and possibilities for implementing different models for language learning in a bilingual and multilingual classroom are discussed.

Emerging technologies and language learning: mining the past to transform the future

AbstractThis paper is an expanded version of a keynote presentation for the 2022 ChinaCALL Conference on the theme of “emerging technologies”. Today’s emerging technologies—artificial intelligence, machine learning, conversational robots, virtual worlds, virtual reality, augmented reality, automated assessment, and so on—are full of promise and seem poised to revolutionize language teaching and learning over the next decade. However, rather than looking forward, I review lessons I have learned over a four-decade career in CALL, focusing on those lessons that have continuing relevance for accommodating these and future technologies as they emerge. In the first part, I present a simple model for technology-mediated language learning as a foundation for the remaining discussion. In the second, I review seven challenges that I worked on in CALL, starting in the 1980s. I describe how I came to be aware of the issues involved and how through a combination of reviewing research, collaborating with colleagues, and drawing on my own experience, I came to learn lessons of enduring value. The final part briefly explores the potential for converting these and other lessons learned into principles to guide current and future encounters with technologies for language teaching and learning.

Promoting a World Englishes Perspective through Podcasts

As the most widely studied and spoken language worldwide, English is a medium of communication between speakers of varied language backgrounds. Global English users need not converge on any one variety; rather, they need adaptive, flexible skills that support communication with speakers of diverse World English (WE) varieties (Kirkpartick 2007). Nevertheless, many English learners and teachers around the world continue to adhere to ‘native speaker’1 models that prioritize language varieties from English-dominant nations, such as the United States or the United Kingdom (Tseng 2019). We have encountered this perspective first-hand in our work in Indonesia. Hanung has taught English and trained teachers at an Islamic University in Central Java for over twenty years. Tabitha has nearly twenty years of experience in ELT, including three years as a visiting instructor at Hanung’s institution. Among the students and teachers we have worked with in Central Java, the dominant language learning model is the ‘native English speaker’. For instance, on a survey given to our first-year English-major students, 81 percent reported wanting to ‘sound like a native speaker’. To challenge this tendency, Simanjuntak and Lien (2021) encourage the use of materials from global and local language communities. Using diverse listening materials can help students understand a wide variety of language varieties and accents and adapt to dialects they have never encountered before. We found podcasts, free audio recordings which are automatically delivered to users’ devices, to be a particularly rich and convenient source of listening materials featuring WE speakers. In the sections below, we describe our teaching context and use of podcasts. We close by discussing how our approach could transfer to other contexts where students and teachers continue to aspire to ‘native speaker’ models, and offer recommendations for teachers interested in shifting to a WE approach and exposing students to varied linguistic norms through podcasts.

AI Creates the Message: Integrating AI Language Learning Models into Social Work Education and Practice

AI Creates the Message: Integrating AI Language Learning Models into Social Work Education and Practice

Implementation of The Special Arabic Language Learning Program (PKPBA) for New Student at The Tarbiyah and Teacher Training Faculty UIN Sultan Maulana Hasanuddin Banten | Implementasi Program Khusus Perkuliahan Bahasa Arab (PKPBA) Bagi Mahasiswa Baru Fakultas Tarbiyah dan Keguruan UIN Sultan Maulana Hasnauddin Banten

The purpose of this study was to determine the implementation of the Special Arabic Language Learning Program at the Tarbiyah and Teacher Training Faculty of UIN SMH Banten. To determine the Arabic language skills of students at the Tarbiyah and Teacher Training Faculty of UIN SMH Banten. In this study, the researcher used a qualitative method design, which is a method that is often referred to as the naturalistic method because the research was carried out in natural conditions. The method, which is based on the philosophy of positivism, is used to examine the condition of a natural object where the researcher is the key instrument, sampling data sources is done purposively. The special Arabic learning program that the researchers will do is to focus on the Arabic language learning model for each department in the FTK environment by taking a sample of one major in one class. The results of the research are that in teaching second language material, you must understand the principles, namely the principle of priority, the principle of correctness and accuracy, the principle of gradation, the principle of emotionality and the principle of understanding the essence and multiplying applications in learning Arabic.

Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction

AbstractRecent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability to automatically build a domain-specific dictionary. As an illustrative use case of our approach, we create an objective dictionary that can infer conflict intensity from text data. We train the neural networks on a corpus of conflict reports and match them with conflict event data. This corpus consists of over 14,000 expert-written International Crisis Group (ICG) CrisisWatch reports between 2003 and 2021. Sensitivity analysis is used to extract the weighted words from the neural network to build the dictionary. In order to evaluate our approach, we compare our results to state-of-the-art deep learning language models, text-scaling methods, as well as standard, nonspecialized, and conflict event dictionary approaches. We are able to show that our approach outperforms other approaches while retaining interpretability.

Religious exemplars’ experience of indebtedness to God: employing innovative machine learning to explore a novel construct

ABSTRACT Acquiring gratitude and self-transcendence are critical to positive socioemotional development. Indebtedness to God may provide a pathway for this healthy development, yet it remains a largely unexplored construct. The purpose of the present work was to use two innovative qualitative methods; 1) exemplar sampling method (N = 15) and 2) cutting-edge machine learning and language models to visually describe the data (N = 5,598) and provide semantic maps. The thematic coding analyses resulted in 11 inquiry-driven themes (five describing the experience of indebtedness to God and six describing the outcomes of indebtedness to God). The experience of indebtedness to God was transcendent and positive. The machine learning language model analysis resulted in 1,920 unique tags and over 100 independent triangulated semantic themes, and several topographical views of the data to spur future research on this novel construct.

Modeling Bilingualism as a Dynamic Phenomenon in Healthy and Neurologically Affected Speakers Across the Lifespan: A Commentary on "Computational Modeling of Bilingual Language Learning: Current Models and Future Directions".

Modeling Bilingualism as a Dynamic Phenomenon in Healthy and Neurologically Affected Speakers Across the Lifespan: A Commentary on "Computational Modeling of Bilingual Language Learning: Current Models and Future Directions".

Are AI language models such as ChatGPT ready to improve the care of individuals with epilepsy?

Are AI language models such as ChatGPT ready to improve the care of individuals with epilepsy?

Teacher’s perceptions of separate language learning models for students with immigrant background in Austrian schools

ABSTRACT The majority of European educational systems regard the provision of high-quality language and learning support for students with immigrant background as the greatest challenge and consider separate language support classes as ‘the solution’ to the educational disadvantages these students face. Austria recently implemented separate language support programmes (‘German language support classes and courses = GLSCC’) for students with beginner-level German language skills. There is a growing body of literature which indicates that separate language learning programmes result in lower academic achievement of students with immigrant background compared to inclusive programmes. This study presents teachers’ perceptions (n = 1.267) of positive or negative effects of the GLSCC in Austrian schools. With regard to positive effects, teachers view the GLSCC as an opportunity for individualised and differentiated instruction. However, many teachers reported negative effects in the social dimension which refer to social exclusion and processes of othering. Furthermore, the findings indicate a variety of deficiencies on a didactic (e.g. students miss content in other subjects) and organisational level (e.g. lack of adequate rooms) and indicate a strong need for improved teacher education and training to teach heterogeneous classes.

On the Identification of the Work Nature of Generative Artificial Intelligence Creation

In the new scientific and technological revolution caused by the cognitive paradigm of law and the development of disciplinary update is on the rise, the "generative artificial intelligence" chat machine represented by ChatGPT, Wenxin Yiyi, etc., came into being. Based on the characteristics of "generative artificial intelligence", it is very likely to trigger new thinking about the right attribute and attribution of "Works of art" in the field of copyright." Generative AI" chat machine is a natural language processing tool developed based on deep neural network architecture, which is essentially a neural network machine learning language model. This paper analyzes the development of copyright law by literature analysis and historical analysis, and the origin of the concept of "work".Under the idea of distinction, the identification of works should be discussed separately from the ownership of rights. According to the standards of the Copyright Law for the identification of works, when the content generated by "generative artificial intelligence" meets the basic requirements of "thought - expression + originality + intellectual achievement" and meets the field limitation of "literature, art and science", it should be included in the scope of work protection.

Synergy of PBA IAIM Sinjai Students in Grounding Arabic in Remote Villages

The lack of attention to learning Arabic makes Arabic a language that is marginalized from people's lives. Getting around this phenomenon, PBA IAIM Sinjai students initiated an Arabic language training program as an effort to attract the attention and interest of remote communities in learning Arabic and change their perspective that Arabic is not a primitive language. The type of research used is field research using a qualitative descriptive approach by means of which the researcher is directly involved with the community regarding the program to be implemented. Methods of data collection using observation, interviews, implementation of learning programs and documentation. The data analysis technique uses Miles and Huberman's theory which includes data reduction, display or presentation of data, then conclusions. The efforts made in this study were to use an Arabic language learning model which was packaged in an attractive way so as to show the enthusiasm of the local community and foster their love for learning Arabic.