Model Of Language Learning Research Articles

Implementation of Neuroscience-Based Multiple Intelligences Theory: To Improve the Intelligence of Students in Ma'had

The development of the flow of Arabic knowledge at the pesantren level (Madrasah Aliyah, Madrasah Diniyah, and Madrasah Ta'wiliyah). Demanding increased Arabic learning to santri. However, this is inversely proportional to the fact that students are declining in developing Arabic mufrodat and linguistics material. This study aims to analyze the Arabic language learning model based on multiple intelligences from a neuroscience perspective. The setting of this research is located in the Himmatul ‘Amilin Islamic Boarding School in Yogyakarta. The subjects of this research consisted of the leaders of Islamic boarding schools, musyrif, and ustadz. Subject selection was carried out using the purposive sampling technique to obtain objective and valid data. The approach in this research is to use a case study-based qualitative method. Researchers in collecting data using observation methods, direct interviews, and documentation. The data analysis technique was followed by Milies and Huberman, namely by displaying data, reducing data, and verifying data. The results of this study explain that learning Arabic based on multiple intelligences increases the intelligence of students in learning Arabic mufradat and linguistics (mufradat, Arabic morphology, and Arabic syntax). From some of the material taught, namely about everyday mufradat and understanding of shorof in the isim file chapter. In this case, multiple intelligences are combined with neuroscientific theory between the interpersonal and intrapersonal intelligence centers in the right brain and left brain. This research has implications for the improvement and development of students in understanding Arabic material (quwaidul arabiyah) in today's Islamic boarding schools.

Model Pembelajaran Kooperatif Team Games Tournament dalam Pembelajaran Mahārah Kitābah di Perguruan Tinggi

The study aims to describe TGT (Team Games Tournament) cooperative learning model as an alternative learning strategy to increase student motivation and creativity in various aspects of language skills, including Arabic. This research method uses qualitative and quantitative research. The subjects in this study were students majoring in Madrasah Ibtidaiyah Teacher Education at STAI Al-HIKAM Malang. Data collection techniques in this study were obtained through observation, interviews, and documentation. The research instrument used the implementation observation sheet. The results of this study are: a) Based on the presence and recap of the student learning outcomes score sheet. b) Based on the structured interview sheet the implementation of the students after using the TGT (Team Games Tournament) cooperative learning model for Arabic language learning. The study results indicate that learning using the TGT (Team Games Tournament) learning model can improve Arabic learning abilities by making students more active and enthusiastic in the learning process.

Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning-Based Text Simplification Approach.

In most cases, the abstracts of articles in the medical domain are publicly available. Although these are accessible by everyone, they are hard to comprehend for a wider audience due to the complex medical vocabulary. Thus, simplifying these complex abstracts is essential to make medical research accessible to the general public. This study aims to develop a deep learning-based text simplification (TS) approach that converts complex medical text into a simpler version while maintaining the quality of the generated text. A TS approach using reinforcement learning and transformer-based language models was developed. Relevance reward, Flesch-Kincaid reward, and lexical simplicity reward were optimized to help simplify jargon-dense complex medical paragraphs to their simpler versions while retaining the quality of the text. The model was trained using 3568 complex-simple medical paragraphs and evaluated on 480 paragraphs via the help of automated metrics and human annotation. The proposed method outperformed previous baselines on Flesch-Kincaid scores (11.84) and achieved comparable performance with other baselines when measured using ROUGE-1 (0.39), ROUGE-2 (0.11), and SARI scores (0.40). Manual evaluation showed that percentage agreement between human annotators was more than 70% when factors such as fluency, coherence, and adequacy were considered. A unique medical TS approach is successfully developed that leverages reinforcement learning and accurately simplifies complex medical paragraphs, thereby increasing their readability. The proposed TS approach can be applied to automatically generate simplified text for complex medical text data, which would enhance the accessibility of biomedical research to a wider audience.

Computational protein design with data‐driven approaches: Recent developments and perspectives

AbstractA fundamental and challenging task of computational protein studies is to design proteins of desired structures and functions on demand. Data‐driven approaches to protein design have been gaining tremendous momentum, with recent developments concentrated on protein sequence representation and generation by using deep learning language models, structure‐based sequence design or inverse protein folding, and the de novo generation of new protein backbones. Currently, design methods have been assessed mainly by several useful computational metrics. However, these metrics are still highly insufficient for predicting the performance of design methods in wet experiments. Nevertheless, some methods have been verified experimentally, which showed that proteins of novel sequences and structures can be designed with data‐driven models learned from natural proteins. Despite the progress, an important current limitation is the lack of accurate data‐driven approaches to model or design protein dynamics.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning

The Influence of Arab Spring on the Existence of Arabic Language Learning in the Multicultural Society of North Sulawesi

This was a survey study which looked at the influence of Arab Spring phenomenon on the development of Arabic language learning in the multicultural society of North Sulawesi as well as its implications for the Arabic language learning model. The Arab Spring phenomenon that occurred from 2010 to 2011 that demanded a revolution in Arab countries reaped crucial impacts in various countries including Indonesia in terms of political, social, religious, cultural, and educational issues. The research method used the technique of proportionate stratified random sampling and involved 300 respondents from several regions in North Sulawesi Province based on three categories, namely areas that had a strong Muslim urban culture, areas that had a thick Islamic tradition, and areas that had Muslim minority issues. The results showed that the value of tcalculation > ttable (6.768 > 1.971). The foregoing demonstrated that Ho was rejected. Thus, it could be concluded that the Arab Spring had an effect on the existence of Arabic learning in the multicultural region of North Sulawesi in terms of interest and motivation. It also affected the content of Arabic teaching materials as well as the purpose and orientation of Arabic learning on the importance of Arabic as a language identity and Arabic as a communication language. Hence, the reconstruction of Arabic learning led to an increase in communication skills that contained local wisdom.Keywords: Arab Springs, Arabic Language Learning, Multicultural Society

IDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, F2-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.

Language models for the prediction of SARS-CoV-2 inhibitors.

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Long-term memory predictors of adult language learning at the interface between syntactic form and meaning.

Recent neurocognitive models of second language learning have posited specific roles for declarative and procedural memory in the processing of novel linguistic stimuli. Pursuing this line of investigation, the present exploratory study examined the role of declarative and procedural memory abilities in the early stages of adult comprehension of sentences in a miniature language with natural language characteristics (BrocantoJ). Thirty-six native Italian young adults were aurally exposed to BrocantoJ in the context of a computer game over three sessions on consecutive days. Following vocabulary training and passive exposure, participants were asked to perform game moves described by aural sentences in the language. Game trials differed with respect to the information the visual context offered. In part of the trials processing of relationships between grammatical properties of the language (word order and morphological case marking) and noun semantics (thematic role) was necessary in order reach an accurate outcome, whereas in others nongrammatical contextual cues were sufficient. Declarative and procedural learning abilities were respectively indexed by visual and verbal declarative memory measures and by a measure of visual implicit sequence learning. Overall, the results indicated a substantial role of declarative learning ability in the early stages of sentence comprehension, thus confirming theoretical predictions and the findings of previous similar studies in miniature artificial language paradigms. However, for trials that specifically probed the learning of relationships between morphosyntax and semantics, a positive interaction between declarative and procedural learning ability also emerged, indicating the cooperative engagement of both types of learning abilities in the processing of relationships between ruled-based grammar and interpretation in the early stages of exposure to a new language in adults.

Fusing topology contexts and logical rules in language models for knowledge graph completion

Knowledge graph completion (KGC) aims to infer missing facts based on the observed ones, which is significant for many downstream applications. Given the success of deep learning and pre-trained language models (LMs), some LM-based methods are proposed for the KGC task. However, most of them focus on modeling the text of fact triples and ignore the deeper semantic information (e.g., topology contexts and logical rules) that is significant for KG modeling. For such a reason, we propose a unified framework FTL-LM to Fuse Topology contexts and Logical rules in Language Models for KGC, which mainly contains a novel path-based method for topology contexts learning and a variational expectation–maximization (EM) algorithm for soft logical rule distilling. The former utilizes a heterogeneous random-walk to generate topology paths and further reasoning paths that can represent topology contexts implicitly and can be modeled by a LM explicitly. The strategies of mask language modeling and contrastive path learning are introduced to model these topology contexts. The latter implicitly fuses logical rules by a variational EM algorithm with two LMs. Specifically, in the E-step, the triple LM is updated under the supervision of observed triples and valid hidden triples verified by the fixed rule LM. And in the M-step, we fix the triple LM and fine-tune the rule LM to update logical rules. Experiments on three common KGC datasets demonstrate the superiority of the proposed FTL-LM, e.g., it achieves 2.1% and 3.1% Hits@10 improvement over the state-of-the-art LM-based model LP-BERT in the WN18RR and FB15k-237, respectively.

Using Machine Learning Language Models to Generate Innovation Knowledge Graphs for Patent Mining

To explore and understand the state-of-the-art innovations in any given domain, researchers often need to study many domain patents and synthesize their knowledge content. This study provides a smart patent knowledge graph generation system, adopting a machine learning (ML) natural language modeling approach, to help researchers grasp the patent knowledge by generating deep knowledge graphs. This research focuses on converting chemical utility patents, consisting of chemistries and chemical processes, into summarized knowledge graphs. The research methods are in two parts, i.e., the visualization of the chemical processes in the chemical patents’ most relevant paragraphs and a knowledge graph of any domain-specific collection of patent texts. The ML language modeling algorithms, including ALBERT for text vectorization, Sentence-BERT for sentence classification, and KeyBERT for keyword extraction, are adopted. These models are trained and tested in the case study using 879 chemical patents in the carbon capture domain. The results demonstrate that the average retention rate of the summary graphs for five clustered patent texts exceeds 80%. The proposed approach is novel and proven to be reliable in graphical deep knowledge representation.

English Language Learning Pattern Matching Based on Distributed Reinforcement Learning

The rapid development of a new generation of information technology, the promotion of network technology, and the emergence of complex and diverse requirements for control objects make the structure of language learning models more and more distributed. Distributed learning theory emphasizes the central position of learners in the learning process and the universality of learning scenes. This paper explores the significance and value of various learning modes to improve students’ learning effect. By analyzing the research data and explaining various effective language learning models, this paper aims to establish a theoretical framework of English language learning models and explore more effective language model matching schemes. This paper analyzes the adaptive multiagent, reward function, Markov model, probability function model, etc. and conducts experiments on the basis of the designed model. The linear correlation parameters of the model and the English language pattern matching efficiency are analyzed and judged on several important indicators. Because the algorithm designed in this paper has a good effect on the control of error, the error reduction rate has reached 85.6%.

Blended Learning Model in the Indonesian Language Learning during the Covid-19 Period

Throughout the COVID-19 epidemic, learning activities were conducted online. Students’ capacity to comprehend the content offered by the teacher is significantly impacted when they learn through this online technique. This also occurs in the subject of Indonesian. As a result, innovation is required to enhance students’ comprehension and abilities when learning Indonesian, namely through blended learning. This study aims to collect data on the blended learning model’s implementation in Indonesian language subject during the Covid-19 pandemic. This descriptive qualitative research method entails locating data sources through literature searches for subsequent analysis. The results indicated that the blended learning model is an excellent model for Indonesian language learning during the epidemic and the new normal. It allows students and teachers to learn autonomously, be helpful, and improve. The blended learning model’s purpose is to assist students in developing their learning styles and preferences for learning; to provide practical, realistic opportunities for educators and students to learn independently, be helpful, and continue to grow; to increase scheduling flexibility for learners by combining the best features of face-to-face and online instruction; to engage students in interactivity during face-to-face classes. At the same time, the online component gives students rich multimedia content at any time and from any location as long as they have access to the objective.

Development of Arabic Language Learning Model Based on Bi'ah Lugawiyyah to Improve Student's Speaking Skills in Islamic Religious College

Learning Arabic in universities has not yet utilized the language environment as a learning resource. Improve speaking skills in Arabic cannot be separated from the language environment. This study aims to produce a valid, practical, and effective Arabic learning model to improve students' Arabic speaking skills at Islamic religious colleges. This research is research and development by adopting the ADDIE design by Branch, which consists of five phases: analysis, design, development, implementation, and evaluations. The research subjects involved 23 university students for the practicality and effectiveness test and two experts for the validity test. Data collection techniques were carried out using instruments such as questionnaires, interviews, observation sheets, and tests of Arabic speaking skills. Qualitative and quantitative methods then analyzed the data. The data analysis showed that the bi'ah lugawiyyah-based Arabic learning model was tested to be valid, practical and effective in improving students' Arabic speaking skills at Islamic universities.

THE BUILDING OF A NEW LANGUAGE LEARNING MODEL BASED ON THE CHOMSKYAN CONCEPT

<p>In the light of the wide spread variety of teaching and learning methods and approaches, the building of a new Language Learning Model rises as a serious contribution that aims to surmount the problem of learning acquisition among language learners. Hence, this study comes into existence to investigate some time in such a way as to come up with some solutions on the basis of the surveyed literature in this field, especially on the basis of the Chomskyan concept of learning. By a Language Learning Model, we mean a model of learning which is based on four meaningful and required dimensions. The first one is the cross-linguistic applicability of the model to all the existing natural languages. The second one is the covering of all the cognitive and non-cognitive factors. The third one is the contextualisation of the learning factors. The fourth one is related to the process of learning and how it should take into account the three learning components, input and its richness, the students’ building competency, and communicative acts that lead to the students’ performance, in accordance with four evaluative value, quantity, quality, manner, and relation. The new conceptualized Language Learning Model is a model that helps in the activation of the learners’ expressivity in terms of creativity, adaptation, and engagement in the learning process through meaningful and comprehensible activities. A learning device that is comprehensive and instructional and that allows the learners to engage in a continuous dialogic context that enables them to learn a language by resolving the conflict between their expressivity and the limitations super-imposed by their brain.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0558/a.php" alt="Hit counter" /></p>

Engaging pre-service English teachers in Sheltered Instruction Model – mediated content and language learning

This study employs the Sheltered Instruction (SI) model and a micro-ethnographic classroom-based study to develop the content (i.e., quantitative research in English Language Teaching) and language learning advancement of pre-service English teachers (henceforth PETS), which includes lesson planning and preparation, building a backdrop, comprehensible input, strategies, interaction, practice/application, lesson delivery, and assessment. Seventy English Education students enrolled in the Quantitative Research in English Language Teaching (ELT) course at Universitas Singaperbangsa Karawang, Indonesia, volunteered to take part in the study. In this study, data was gathered using students' work artifacts: questionnaires, in-class discussion notes, and reflective journals (daily notes). The findings of a qualitative study indicate the SI model of content and language learning helps PETS improve their academic language. This study also shows that employing SI as an intervention model could help pre-service English teachers improve their academic language skills in the content or subject area in the context of English as a foreign language.

An Emotional Analysis Method for the Analysis of Cognitive and Psychological Factors in the Change of Second Language Learning Model of Chinese Mainland Students in the Post-epidemic Era.

Since the sudden outbreak of the coronavirus disease 2019 (COVID-19) epidemic in 2020, the second language learning patterns of students in mainland China have encountered new challenges that have had a psychological impact on mainland Chinese students. The epidemic has not only inconvenienced students’ normal second language learning but also greatly affected the second language learning patterns of mainland Chinese students. In the post-epidemic era, more and more students are becoming accustomed to studying and learning a second language online. The level of informatization of second language learning patterns of students in mainland China has increased significantly. This study first analyses the mechanisms of change in second language learning patterns and further analyses the influence of knowledge background on the perception of second language learning patterns on this basis. To design the influencing factors of second language learning patterns, a questionnaire was used to investigate the influence of knowledge background on the perception of second language learning patterns. The survey was conducted on students who were learning a second language in mainland China. Then, the survey data were statistically analyzed. In analyzing the influence of effect on second language learning behaviors of students in mainland China, observed variables were designed, including observed variables of affective factors and learning behaviors. After that, the findings of the experiment were summarized based on the results of the questionnaire survey, and the positive influence of emotional factors on second language learning behaviors of mainland Chinese students in the post-development era was concluded.

Websites Use as Learning Resources in Developing Intercultural Communicative Competence in English Language Teaching Classroom

This classroom action research (CAR) aims at developing students’ intercultural competence (ICC) and exploring English language learning models integrated to ICC on the basis of websites use as the learning resource. This research was conducted in Intensive Course online class involving 24 students of English study program in Universitas Lancang Kuning. This CAR was conducted in one cycle consisting of 6 meetings and 2 evaluations. The data was collected from (1) observation fieldnotes, (2) observation checklist, (3) collecting documents (students’ comments and accessed websites, and (4) the scores taken from the 5 groups’ discussion. This research reveals the students’ ICC dimensions development exhibited in their class discussions with the increasing percentage from 46.15% to 92.30%). Some language learning models integrated to ICC on the basis of websites use were identified: (1) Accessing websites related to ICC learning topics, and (2) accessing websites that provide authentic materials (texts, pictures, audio-videos, and (3) motivating students to communicate and collaborate with others via online learning media. This CAR concludes that “students’ ICC could be developed through language learning on the basis of websites use which contains ICC dimensions”.

An exploratory foreign language curriculum: The creative application of RICH in senior high school English instruction

With the rapid development of globalization, foreign language learners are supposed to have the ability to use the language in intercultural communication accurately and appropriately. However, guided by the traditional teacher-centered language teaching and learning model, learners cannot meet the requirements of social development because the knowledge that students acquire is rather limited, let alone learning and applying abilities. Meanwhile, their interest in learning a foreign language cannot be fully stimulated. RICH, a creative curriculum concept, combines Research-based Learning, Integrated Curriculum, Cooperative Learning, and Humanistic Outcomes. The new curriculum concept aims to lead students to enter the language classroom as thinkers and researchers, developing their humanistic qualities and their abilities to use the English language in practice through interdisciplinary studies so as to promote their intercultural communicative ability eventually. To test the applicability of RICH in senior high English school instruction, qualitative and quantitative research methods were employed, including one academic year’s teaching experiment lasting from 2020 through 2021, video recordings of instructing cases, and semi-open interviews with some subjects. Research data has demonstrated that RICH can be of great help in creatively designing and implementing key competence cultivation-oriented English instructions in the context of the New Curriculum Standards for senior high school English instruction.

Team Games-Language Learning Model in Improving Students’ Speaking and Listening Skills Viewed from Creativity

Speaking and listening are language skills that are supporting each other. Students who have good speaking competence will have good listening skills as well. Unfortunately, students who have good listening skills do not indicate that they have good speaking skills. To help students in improving speaking and listening, this study applied team games-based language learning (TG-BLL) model. This model was designed with appropriate learning materials and activities, and English experts validated the quality of learning materials. Therefore, this current study was aimed at investigating the use of the TG-BLL model to improve students’ speaking and listening skills viewed from their creativity at secondary schools at Algeria. To attain the research goal, this study employed quasi experimental research with using pre-test post-test control group design. 69 students were involved in this study as research samples. The samples were divided into two groups which are experimental and control groups. The samples were selected using cluster random sampling. The variable of creativity in this study was pointed as a helping variable to see whether the level of students’ creativity come to affect students’ speaking and listening skills. To see the significant effect of team games-based language learning on speaking and listening skills, the researcher applied t-test statistical analysis. Meanwhile, ANOVA statistical analysis was applied to see the interaction between the TG-BLL model and students’ creativity level on speaking and listening skills at the secondary level. The result showed that the TG-BLL model had good effect on speaking and listening skills in the experimental group. It was proven that the score of the t-test was higher than t-table with 0.05 significant level. Moreover, there was good interaction between the GBLL model and students’ creativity level to affect students’ speaking and listening skills at secondary schools. The implementation of the TG-BLL model was useful in improving students’ pronunciation, grammar, and knowledge of how sounds get reduced.