Model Of Language Learning Research Articles

Detection and Prediction of Future Mental Disorder From Social Media Data Using Machine Learning, Ensemble Learning, and Large Language Models

Detection and Prediction of Future Mental Disorder From Social Media Data Using Machine Learning, Ensemble Learning, and Large Language Models

An Examination of the Use of Large Language Models to Aid Analysis of Textual Data

The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample texts taken from existing interviews. Next, we created a codebook and inputted the sample text and codebook into an LLM. We asked the LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding. The sample texts were inputted 160 times to record changes between iterations of the LLM response. Each iteration was analogous to a new coder deductively analyzing the text with the codebook information. In our results, we present the outputs for these recursive analyses, along with a comparison of the LLM coding to evaluations made by human coders using traditional coding methods. We argue that LLM analysis can aid qualitative researchers by deductively coding transcripts, providing a systematic and reliable platform for code identification, and offering a means of avoiding analysis misalignment. Implications of using LLM in research praxis are discussed, along with current limitations.

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning.

To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combination with various biochemical and structural features of protein residues to discriminate neutral vs. deleterious mutations. However, the power of these methods is often limited because they either assume known protein structures or treat residues independently without fully considering their interactions. To address the above limitations, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 20 network centrality scores to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively (via machine learning). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods (and optionally predictive scores from other methods) via machine learning to predict hotspot sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.

English Language Learning Model Based on the Five-Step Model for High Schools Students in Jinan, China

English Language Learning Model Based on the Five-Step Model for High Schools Students in Jinan, China

MA'HAD Al-JAMI’AH IN INDONESIA: PORTRAIT OF THE ARABIC LANGUAGE LEARNING MODEL

The study aims to identify the tangible forms of Arabic language learning programs and to describe the models of Arabic language learning at the ma’had al-jamiah of Fatmawati Sukarno Bengkulu State Islamic University and Ali Bin Abi Thalib Surabaya Islamic College. This research adopts a descriptive qualitative type. The sources of information include observations during the Arabic language learning process and interviews with instructors or teachers. The Data analysis in this research is conducted using the interactive analysis theory by Miles and Huberman. The analysis technique involves data collection, presentation, reduction, and conclusion. The results of this research indicate that: Firstly, the forms of Arabic language learning programs at the ma’had al-jamiah of Fatmawati Sukarno Bengkulu State Islamic University include: ta’lim al-lughoh al-arabiyah, muhadtsah yaumiyah fi al-lughoh al arabiyah, provision of vocabularies, and khitabah. The forms of Arabic language learning programs at the ma’had al-jamiah of Ali bin Abi Thalib Surabaya Islamic College include: insya bi’ah lughowiyah, fashl ilaaji, fashl idhofi, muhadharah arabiyah, and muhadtsah usbuiyah. Secondly, the models of Arabic language learning at the ma’had al-jamiah of Fatmawati Sukarno Bengkulu State Islamic University and Ali bin Abi Thalib Surabaya Islamic College include direct learning and learning with presentations.

Effects of ChatGPT on Students Academic Performance:Mediating Role of Prompt Engineering

This paper focuses on the use of advanced machine learning models, such as ChatGPT, in education. ChatGPT, a language learning model by OpenAI, has shown potential for reshaping education through enhanced teaching methods, increased student engagement, and personalised learning experiences. Despite these promising features, the potential drawbacks of the technology remain unclear. This paper seeks to examine the impacts of ChatGPT on students' academic performance, with a special emphasis on the role of 'prompt engineering' in this context. This research employs a quantitative method to examine the impact of ChatGPT on student academic performance in various universities across Pakistan. An online survey was distributed, garnering responses from 37 students at several institutions. The questions focused on demographic details and the impact of ChatGPT on academic performance parameters such as learning, quality of work, and creativity, with the role of prompt engineering as a mediating factor. Data was analysed using SPSS, with Cronbach's alpha used to ensure reliability and internal consistency of the responses, which showed a high degree of correlation among responses. The results indicated a positive relationship between the use of ChatGPT and academic performance (Naveed et al., 2023). However, while most students were aware of ChatGPT's capabilities, the majority did not use it for examination preparation. The study also found that prompt engineering played a mediating role between ChatGPT usage and student academic performance, highlighting the importance of effective prompt design in optimising the benefits of AI in educational settings.

English learning model based on social media

This research endeavours to address the pressing need for a tailored English language learning model for students enrolled in Polytechnic institutions specializing in Food and Beverage studies. Recognizing the pivotal role of English proficiency in global culinary communication, we aim to design a comprehensive learning model. This model harnesses the potential of social media as an innovative pedagogical tool to enhance language skills, equipping students with the linguistic competence essential for success in their field. A mixed-methods approach was employed, combining quantitative and qualitative research methodologies. Initially, a thorough needs analysis was conducted to determine the specific language requirements of Food and Beverage students. Subsequently, an adaptive learning model was designed, incorporating social media platforms as supplementary tools for language acquisition. The model was tested within the curriculum of a Polytechnic institution, utilizing a pre-test/post-test design to assess the impact on students' language proficiency. The results of our study indicate a significant improvement in the English language proficiency of students following the implementation of the learning model. Quantitative analysis of pre-test and post-test scores revealed a statistically significant increase in language skills. Moreover, qualitative feedback from students and educators highlighted the model's efficacy in enhancing engagement and motivation, thereby fostering a more dynamic and effective learning environment. This research contributes to the intersection of language education and the Food and Beverage industry by providing a specialized English language learning model. The innovative integration of social media platforms as supplementary tools demonstrates the adaptability and effectiveness of this approach. The findings affirm the model's potential to bridge the gap between industry-specific language requirements and educational curricula, empowering students with the linguistic competence necessary for global success in the Food and Beverage sector. This study underscores the transformative potential of technology-enhanced language learning in specialized fields and lays the foundation for future research and pedagogical innovation. Ultimately, this research seeks to empower students, educators, and the industry at large, enriching the landscape of culinary education and communication on a global scale.

Navigating GPT-4 and BERT: A Dual Perspective on Financial and Political Sentiment Analysis

Abstract: This research delves into the dynamic capabilities of large language models (LLMs) by combining insights from two distinct studies. The first study, "Linking Microblogging Sentiments to Stock Price Movement: An Application of GPT-4," explores the efficacy of the GPT-4 Language Learning Model (LLM) compared to BERT in modeling same-day daily stock price movements for Apple and Tesla in 2017. The study leverages sentiment analysis of microblogging messages from the Stocktwits platform and employs a novel method for prompt engineering, emphasizing the contextual abilities of GPT-4. Logistic regression is utilized to evaluate the correspondence between extracted message contents and stock price movements, revealing GPT-4's substantial accuracy, outperforming BERT in five out of six months. However, practical considerations, including deployment costs and the need for fine-tuning prompts, are also acknowledged. The second study, "Do We Still Need BERT in the Age of GPT? Comparing the Benefits of Domain-Adaptation and In-Context-Learning Approaches to Using LLMs for Political Science Research," investigates the choices researchers face when employing LLMs in political science tasks. The study establishes benchmarks for various natural language processing (NLP) tasks within political science and compares two common approaches: domain-adapting smaller LLMs like BERT with unsupervised pre-training and supervised fine-tuning and querying larger LLMs like GPT-3 without additional training. Preliminary results suggest that, when labeled data is available, a finetuning-focused approach remains superior for text classification. By synthesizing these studies, this research contributes to the broader understanding of LLMs' capabilities and their applicability in diverse domains. It emphasizes the significance of prompt engineering in unlocking the contextual abilities of modern LLMs, providing insights into financial sentiment analysis and political text classification. The findings underscore the nuanced choices researchers must make in selecting appropriate LLMs, adapting them to specific domains, and designing effective prompts for optimal performance in different research contexts.

The Development of a Reflective Language Learning Model: A Structural Equation Modeling Approach

Background: Several language learning theories exist to explain the language learning process. Reflective learning is one of the models that has received attention and has been employed to explain language learning and teaching.
 Purpose: The present study aimed to develop and validate a close-ended reflective language learning instrument to discover EFL learners’ reflective learning strategies while learning a language.
 Method: Based on a comprehensive literature review, 358 concepts were extracted for reflective language learning. Then, experts’ opinions on reflective language learning were gathered, leading to the extraction of 50 general themes. After the experts’ approval, the researchers transformed the concepts into statements and constructed the final version of the questionnaire. In the next step, the initial version of the questionnaire was piloted with 100 participants, reducing the number of items to 47. Then, the piloted instrument was administered to a sample of 398 students. The obtained data were entered into SPSS and LISERL for exploratory and confirmatory factor analyses. Principle Component Analysis (PCA) was employed to run an explanatory factor analysis. Varimax rotation was performed on the underlying construct of the 47-item questionnaire. The result was the removal of four items and forming a 43-item questionnaire. A six-factor model of second language learning encompassing twelve behavioral cognitive items, twelve behavioral evaluative items, six behavioral metacognitive items, six behavioral interactional items, four behavioral reflective journal items, and three behavioral retrospective items was obtained. Then, the researchers performed confirmatory factor analysis to verify the six factors. Finally, a reflective language learning model was developed.
 Results: The results showed that the newly developed Reflective Language Learning Questionnaire (RLLQ) was valid and reliable. The model formulated based on the data gathered from the administration of RLLQ also enjoyed acceptable fitness indices.
 Conclusion: The questionnaire could be used in future studies. Researchers interested in reflective language learning, language teachers intending to follow reflective practices in their classes, and syllabus designers believing that reflection promotes learning can employ RLLQ.

Analysis of Descriptive Text Writing Skill Characteristics Based on Performance-Based Assessment for Junior High School Level

This research aims to develop a performance-based assessment model for descriptive text writing skills at the junior high school level. The success of the learning process for descriptive text writing skills in junior high school can be seen in the learning outcomes achieved by students. These learning outcomes are obtained through assessments conducted by teachers at school. However, in reality, teachers face difficulties in developing assessment tools for descriptive text writing skills. These difficulties are addressed by developing a performance-based assessment model for descriptive text writing skills. The research method used in developing the ADDIE model for skills assessment is Research and Development. The ADDIE model consists of five steps: (1) analysis; (2) design for performance-based descriptive text writing; (3) development; (4) implementation; and (5) evaluation. The results of this research include the characteristics of the assessment model for Indonesian language learning at the junior high school level.

Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models.

This paper provides a comprehensive overview of affective computing systems for facial expression recognition (FER) research in naturalistic contexts. The first section presents an updated account of user-friendly FER toolboxes incorporating state-of-the-art deep learning models and elaborates on their neural architectures, datasets, and performances across domains. These sophisticated FER toolboxes can robustly address a variety of challenges encountered in the wild such as variations in illumination and head pose, which may otherwise impact recognition accuracy. The second section of this paper discusses multimodal large language models (MLLMs) and their potential applications in affective science. MLLMs exhibit human-level capabilities for FER and enable the quantification of various contextual variables to provide context-aware emotion inferences. These advancements have the potential to revolutionize current methodological approaches for studying the contextual influences on emotions, leading to the development of contextualized emotion models.

Data Set Formation Method for Checking the Quality of Learning Language Models of the Transitive Relation in the Logical Conclusion Problem Context

A method for data set formation has been developed to verify the ability of pre-trained models to learn transitivity dependencies. The generated data set was used to test the quality of learning the transitivity dependencies in the task of natural language inference (NLI). Testing of a data set with a size of 10,000 samples (MultiNLI) used to test the RoBerta model. It was found that this model is good at studying transitive dependencies in the task of logical inference because all samples from the formed dataset were correctly classified as belonging to the class similar, contradiction and neutral. It was also investigated that in the task of logical inference, the class similarity is more directed than contradiction and neutral. Because if the premise and hypothesis in the data set are swapped, the accuracy of the RoBerta model decreases by a factor of 2.97,1.17,1.26 for the similar (0.98→0.33) , neutral (0.90→0.77) , and contradiction (0.98→0.78) classes, respectively. The study iteration time is 0.0028 seconds, so only half of the data set requires approximately 84 hours of collection. This research is relevant because the ability of natural language models to explore such dependencies as transitivity, which is not explicitly specified in the training data set, is an important element of the model’s ability to generalize. It was found that RoBerta’s model is good at studying transitive dependencies in the logical inference task because it correctly classified belonging to the class similar, contradiction, and neutral on all samples from the generated data set.

DEVELOPMENT OF AN AI-POWERED EMOTIONALLY INTELLIGENT, INTERACTIVE AGENT PROTOTYPE TO SUPPORT INFORMAL CAREGIVERS

Abstract Long-term informal caregiving for older adults with dementia is exhausting and poses risks to caregivers’ health outcomes. Most informal caregivers are not well prepared for their new role, facing various challenges and receiving limited support. With the advent of artificial intelligence (AI) and information and communication technologies (ICTs), assistive technology is increasingly useful for supporting caregivers. However, limited research assesses the potential for AI to facilitate a privacy-preserving computation environment to provide proactive assistance to caregivers. This project aims to fill this gap by leveraging the tremendous potential of Multimodal Machine Learning and Large Language Models (LLMs) to develop a prototype of an AI-powered emotionally intelligent, interactive agent for informal caregivers. We introduce an effective context-driven instruction tuning technique that enables LLMs to finetune various caregiving-related tasks (tips generation, answering questions, elaborating a specific training module) with instructions. The project will also deliver a first-of-its-kind custom dataset augmented with manualized instructions for relevant caregiving tasks, which is used for finetuning several state-of-the-art LLM models. Our intelligent interactive agent prototype currently has the ability to evaluate caregivers’ unique affective states at query times to provide personalized assistance, addressing various types of caregivers’ situation-specific queries (e.g., managing agitation, wandering, or medications). The prototype is capable of understanding and responding to the user’s natural language queries and designing short, machine-driven persuasive conversations on a pre-defined set of topics. Our prototype has the potential to provide individualized and contextualized support for caregiving tasks which in turn may greatly reduce caregiver burden.

An artificial neural network approach for the language learning model

The current study provides the numerical solutions of the language-based model through the artificial intelligence (AI) procedure based on the scale conjugate gradient neural network (SCJGNN). The mathematical learning language differential model is characterized into three classes, named as unknown, familiar, and mastered. A dataset is generalized by using the performance of the Adam scheme, which is used to reduce to mean square error. The AI based SCJGNN procedure works by taking the data with the ratio of testing (12%), validation (13%), and training (75%). An activation log-sigmoid function, twelve numbers of neurons, SCJG optimization, hidden and output layers are presented in this stochastic computing work for solving the learning language model. The correctness of AI based SCJGNN is noted through the overlapping of the results along with the small calculated absolute error that are around 10–06 to 10–08 for each class of the model. Moreover, the regression performances for each case of the model is performed as one that shows the perfect model. Additionally, the dependability of AI based SCJGNN is approved using the histogram, and function fitness.

Procesy modelowania glottodydaktycznego w ujęciu wybranych współczesnych teorii lingwistycznych

Contemporary glottodidactics uses numerous models of foreign language learning and teaching. Although glottodidactics is a highly interdisciplinary science and its development is determined by many scientific disciplines, linguistic studies have significantly contributed to the development of theoretical foundations for glottodidactics as an independent science for decades. The paper discusses and summarizes the state of research on glottodidactic modeling processes (Grucza 1978, Woźniewicz 1987, Pfeiffer 2001, Dakowska 2010, Grucza 2010b, Gębal 2013 and the author's model) and proposes a new model that integrates the most important elements of earlier systems. The author distinguishes two levels of the glottodidactic modeling process: internal (referring to individual language skills and intrapersonal properties of the learner in the sense of F. Grucza's anthropocentric linguistics, as well as selected theories of cognitive linguistics) and external (teacher and multidimensional determinants of the glottodidactic process that the teacher encounters in this process). The special positioning of the learner in the system emphasizes the individual and unique dimension of the foreign language teaching process of each learner, who, by constructing the structures of a foreign language and, as a result, also communicative competence in a given foreign language, significantly broadens and develops its cognitive framework.

ChatGPT's performance in German OB/GYN exams - paving the way for AI-enhanced medical education and clinical practice.

Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial learning and large language model tool developed by OpenAI in 2022. It utilizes deep learning algorithms to process natural language and generate responses, which renders it suitable for conversational interfaces. ChatGPT's potential to transform medical education and clinical practice is currently being explored, but its capabilities and limitations in this domain remain incompletely investigated. The present study aimed to assess ChatGPT's performance in medical knowledge competency for problem assessment in obstetrics and gynecology (OB/GYN). Two datasets were established for analysis: questions (1) from OB/GYN course exams at a German university hospital and (2) from the German medical state licensing exams. In order to assess ChatGPT's performance, questions were entered into the chat interface, and responses were documented. A quantitative analysis compared ChatGPT's accuracy with that of medical students for different levels of difficulty and types of questions. Additionally, a qualitative analysis assessed the quality of ChatGPT's responses regarding ease of understanding, conciseness, accuracy, completeness, and relevance. Non-obvious insights generated by ChatGPT were evaluated, and a density index of insights was established in order to quantify the tool's ability to provide students with relevant and concise medical knowledge. ChatGPT demonstrated consistent and comparable performance across both datasets. It provided correct responses at a rate comparable with that of medical students, thereby indicating its ability to handle a diverse spectrum of questions ranging from general knowledge to complex clinical case presentations. The tool's accuracy was partly affected by question difficulty in the medical state exam dataset. Our qualitative assessment revealed that ChatGPT provided mostly accurate, complete, and relevant answers. ChatGPT additionally provided many non-obvious insights, especially in correctly answered questions, which indicates its potential for enhancing autonomous medical learning. ChatGPT has promise as a supplementary tool in medical education and clinical practice. Its ability to provide accurate and insightful responses showcases its adaptability to complex clinical scenarios. As AI technologies continue to evolve, ChatGPT and similar tools may contribute to more efficient and personalized learning experiences and assistance for health care providers.

Complex Dynamic Systems Theory in Second Language Learning and Teaching: A Textometric Review from 2008 to 2022

Complex Dynamic Systems Theory (CDST) has significantly reshaped our understanding of second language learning and teaching across the micro, meso, and macro levels for several decades since its systematic introduction by Larsen-Freeman and Cameron (2008). This study conducts a comprehensive review of empirical research on second language learning and teaching within the framework of CDST from 2008 to 2022. Textometric analysis, a computer-supported qualitative method for textual analysis, was employed to conduct the review by using the IRAMUTEQ software. A total of 198 publications were identified, leading to the emergence of three prominent research themes: research on the language system, research on the second language learner and learning, and research on the second language teacher and teaching. The chronological trends and distinguishing features of the three research themes were discussed against a mainstream model of language learning and teaching at the micro, meso, and macro levels (The Douglas Fir Group, 2016). The findings of this review hold the potential for enlightening future research endeavors in this field.

Students' English Skills and Their Ways of Learning

at the Faculty of Languages and Literature, Makassar State University. The subjects of this research were fourth-semester students of the English Education Study Program, Department of English, because in that semester there were Learning and Teaching courses. This research wants to see what the student's language learning model is. Students in the fourth semester of the 2021/2022 academic year who took this course in 5 classes consisted of 210 students. However, this research was only carried out in 2 (two) classes, namely classes D and E with a total of 82 students. This research uses a qualitative approach, namely Qualitative Descriptive research. The results of this research show that Students' English language skills are obtained a little differently with only 2 (two) ways of learning English, namely formal and informal, Students' ways of learning English are slightly varied and utilize technology, including: Watching English films/videos; Listening to English songs; YouTube; TikTok; Internet/ Google; Memorizing vocabulary; English podcasts; Practice; Social media; book; and Games.

Active and Innovative Indonesian Language Learning Model for 8-Year-Old Children

Mastery of Indonesian language skills for children is still a challenge for teachers and parents. Teachers need an effective learning model to improve Indonesian language skills and knowledge. This research aims to develop an active and innovative Indonesian language learning model for children aged 8 years by collaborating with the Numbered Heads Together, Sample Non-Examples, and Guess Words (NET) learning models. This research is a test of the effectiveness of the learning model with a small group trial study carried out at MI Kompa, and a large group trial carried out at MI Adda'wah and MIN I Sukabumi Parungkuda. Data collection techniques were carried out using a mixture of two types of data, qualitative and quantitative. The research results show that the development of the NET learning model in Indonesian language subjects has succeeded in improving children's learning outcomes. Therefore, the results of this study can be used as a starting point for further research. This study has limitations in using descriptive statistical data that do not yet explain the factors that influence the success of the proposed learning model. The results can also provide a basic approach to support further analysis of the impact of using learning strategies in the classroom, both in increasing student and teacher activity. Keywords: numbered heads together, non-examples, guess the words, Indonesian, eight-year-old children

Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph

Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.