Learning Stage Research Articles

Gender-specific factors of suicidal ideation among high school students in Yunnan province, China: A machine learning approach.

Suicidal ideation (SI) assumes a pivotal role in predicting suicidal behaviors. The incidence of SI among high (junior and senior) school students is significantly higher than that of other age groups. The aim of this study is to explore the gender differences in SI among high school students in Yunnan Province. A total of 6180 students in grades 7-12 in Yunnan province, China from May 2021 to May 2022 participated in this survey. Univariate analysis was employed to describe the influencing factors of male and female students' SI. Subsequently, data were stratified by gender. Adopting machine learning technique, including Least Absolute Shrinkage and Selection Operator (Lasso) and Boruta algorithm, and logistic regression model to estimate the direction and effect magnitude of the influencing factors. The prevalence of SI was significantly higher for females (31.34%) than males (16.73%). The logistic regression model was established using the variables screened by Boruta algorithm, indicated that anxiety, depression, suffering emotion abuse or emotion neglect in childhood, non-suicidal self-injury, evening chronotype are common risk factors for SI in male and female students. Notably, female students who exhibited aggressive behavior, have experienced bullying, and were in the junior high school learning stage were more likely to report SI than their male counterparts. Females showed more vulnerability to SI than males especially among females in junior high school, reporting aggressive behavior and bullying experiences. Tailored prevention strategies, informed by these gender-related distinctions, should be developed and implemented.

A Novel Dictionary Learning Algorithm Based on Prior Knowledge for fMRI Data Analysis

ABSTRACTTask‐based functional magnetic resonance imaging (fMRI) has been widely utilized for brain activation detection and functional network analysis. In recent years, the K‐singular value decomposition (K‐SVD) algorithm has gained increasing attention in the research of fMRI data analysis methods. In this study, we propose a novel temporal feature region‐growing constrained K‐SVD algorithm that incorporates task‐based fMRI temporal prior knowledge and utilizes a region‐growing algorithm to infer potential activation locations. The algorithm incorporates temporal and spatial constraints to enhance the detection of brain activation. Specifically, this paper improves the three stages of the traditional K‐SVD algorithm. First, in the dictionary initialization stage, the automatic target generation process with an independent component analysis algorithm is utilized in conjunction with prior knowledge to enhance the accuracy of initialization. Second, in the sparse coding stage, the region‐growing algorithm is employed to infer potential activation locations based on temporal prior knowledge, thereby imposing spatial constraints to limit the extent of activation regions. Finally, in the dictionary learning stage, soft constraints and low correlation constraints are applied to reinforce the consistency with prior knowledge and enhance the robustness of learning for task‐related atoms. The proposed method was validated on simulated and real fMRI data, showing superior performance in detecting brain activation compared with traditional methods. The results indicate that the algorithm accurately identifies activated brain regions, providing an effective approach for studying brain function in clinical applications.

Exploration and Practice of Classification Talent Training Model in Local Agricultural Institutes

With the rapid development of modern society, the demand for professional talents in the veterinary industry is increasing and showing a trend of diversification. In order to better adapt to this change and meet the industry's demand for high-quality and versatile veterinary talents, the reform of traditional veterinary professional education is particularly urgent. Taking Shanxi Agricultural University as an example, the veterinary specialty of a local agricultural university actively explored and implemented the "2-2-4" classified talent training model of "three stages, three modules and seven links". This innovative model divides veterinary education into three learning stages, each with targeted teaching modules, and ensures the systematic transmission of knowledge and comprehensive cultivation of skills through seven specific teaching links. In this way, the model effectively solves many problems in traditional teaching, such as weak professional awareness among students, insufficient teacher-student interaction, single ability cultivation among students, and disconnection between teaching and research. It emphasizes the close integration of theory and practice, emphasizes students' active participation and innovation ability cultivation, thereby significantly improving the quality of talent cultivation. In addition, this reform practice of Shanxi Agricultural University not only provides a broader development space and employment opportunities for veterinary students in our university, but also provides useful reference and inspiration for the educational reform of veterinary majors in peer universities. Its successful implementation proves that emphasizing innovative teaching models and diversified talent cultivation in veterinary education is an important way to improve education quality and meet social needs. Therefore, this model has important promotion value and is expected to cultivate more high-quality talents with innovative spirit and practical ability for the veterinary industry.

Neural changes in sign language vocabulary learning: Tracking lexical integration with ERP measures

The present study aimed to investigate the neural changes related to the early stages of sign language vocabulary learning. Hearing non-signers were exposed to Catalan Sign Language (LSC) signs in three laboratory learning sessions over the course of a week. Participants completed two priming tasks designed to examine learning-related neural changes by means of N400 responses. In a semantic decision task, participants evaluated whether written Catalan word pairs were semantically related or not. The experimental manipulation included prime-target phonological overlap (or not) of the corresponding LSC sign translations. In a LSC primed lexical decision task, participants saw pairs of signs and had to determine if the targets were real LSC signs or not. The experimental design included pairs of signs that were semantically related or unrelated. The results of the LSC lexical decision task showed N400 lexicality and semantic priming effects in the third session. Also in the third session, N400 effects related to the activation of LSC phonology were observed during word processing in the semantic decision task. Overall, our findings suggest rapid neural changes occurring during the initial stages of intensive sign language vocabulary training. The results are discussed in relation to the temporality of lexicality and semantic effects, as well as their potential relation to linguistic features of sign languages.

Workplace affordances and learning engagement in a Thai paediatric intensive care unit.

Workplace learning in critical care settings is complex and challenging. Research has explored learner-, teacher-, and context-related factors that influence medical residents' engagement in critical care workplaces in Western but not in non-Western cultures. This limits our understanding of workplace learning globally and how we can better support resident learning in diverse cultures. To explore how paediatric residents engage in workplace learning in a Thai Paediatric Intensive Care Unit (PICU) and how this culturally situated workplace shapes their learning. In this qualitative study, we recruited paediatric residents (n = 16) from a tertiary care hospital in Thailand for semi-structured interviews. We used reflexive thematic analysis to describe, analyse and interpret residents' experiences of workplace learning, and to capitalise on our own experience as an analytic resource. We constructed three themes to represent participants' narratives: PICU cases and context as dynamic affordances; impact of psychological safety; and the role of attending physicians. While Thai PICU cases and context could afford participation and thus learning, Thailand's collectivist culture, which prioritises group needs over individual needs, contributed to a sense of psychological safety within culturally-endorsed, professional and social hierarchies, and set the stage for workplace learning. Despite their higher status in these hierarchies, attending physicians facilitated resident learning by fostering open dialogue, joint problem-solving and a low-stress atmosphere. Workplace learning in a Thai PICU while challenging, is uniquely facilitated by Thailand's collectivist culture that fosters psychological safety and attending physicians' invitation in, and learn from, the workplace optimises learning.

Writing Effective Cover Letters: Do Undergraduates Have the Written Communication Skills to Achieve the Spirit of SDG4 Quality Education?

Introduction: This study investigates the impact of English language instructors’ intervention on the cover letter writing process for job applications among specific ESL learners enrolled in a workplace communication course at a public university in Malaysia. This study adopts an action research technique, incorporating content analysis conducted before and after the intervention. The goal of this action research is to identify inaccuracies or issues in the format and content of cover letters written by the selected learners and implement instructor intervention to resolve the identified problems or errors. Generally, the results of this study revealed that the effectiveness of the learners’ cover letters improved after receiving extra input and feedback from their language instructors. A lack of learner-readiness and job knowledge resulted in some learners experiencing difficulty in writing coherent cover letters. More importantly, the learners indicated that their writing experiences raised greater awareness of the many aspects of cover letters and enhanced their confidence in writing cover letters. Overall, this study contributes the initiative to enhance learners' understanding of the ever-changing employment market and develop not only their ability to write cover letters but also their self-confidence and proficiency in the English language. Most significantly, the written communication skills will provide them with lifelong learning opportunities that are taught in higher education as stipulated in the fourth goal (SDG4) of the United Nations’ Sustainable Development Goals, which emphasizes quality education. Objective: The objective of this study is to assess the learners' proficiency in writing cover letters by identifying their strengths and weaknesses. The aim is to evaluate the efficacy of the writing methods employed in cover letters and determine their appropriateness. Theoretical Framework: Kolb’s experiential learning theory (ELT) serves as the primary conceptual framework to define the sequence of activities in the research procedure. David Kolb's (1984) cycle of experiential learning stages perceives learning as a dynamic interaction between learners and their environment (Kolb, 2014). The Experiential Learning Theory (ELT) underscores a cyclical learning process that includes experiencing, reflecting, thinking, and experimenting (Kolb & Kolb, 2005). Method: This study adopts the action research methodology and employed a six-phase method, which comprises the first phase, the diagnostic test, the second phase, the evaluation of cover letters, the third phase, the pre-writing strategies, the fourth phase, the writing phase, the fifth phase, the instructor feedback phase, and the sixth phase is the re-writing phase. Results and Discussion: The results obtained revealed the necessity for graduating learners to possess enhanced skills and knowledge prior to entering the challenging work market. There is currently a strong demand for graduates to develop more modern and thorough tactics to successfully secure employment. The results of this study confirm the need for innovative methods in language acquisition, specifically grammar mastery. Research Implications: Learners need a helping hand with their language and should be guided closely beginning with the rudiments of writing and other integrated language skills that would help bolster their confidence in writing cover letters and improve their communication skills. They need to learn to be independent and proactive in the job-search process. As instructors empower learners with lifelong skills of writing cover letters, the learners themselves should constantly keep abreast of the changes in the dynamic job market. Originality/Value: This study helps learners comprehend the shifting job market, prepare cover letters, and improve their English and self-confidence. Most importantly, it will offer lifelong learning, as stated in the UN's fourth Sustainable Development Goal (SDG4).

Rolling bearing fault diagnosis method based on adaptive signal diagnosis network and its application

Aiming at the problems of fixed convolution kernel size and poor targeting of extracted features in the application of deep learning in fault diagnosis, this paper develops a fault diagnosis framework called adaptive signal diagnosis network (ASDN), in which the EEMD of multi-scale signal decomposition is improved in the adaptive preprocessing stage to adaptively capture the transient changes of signals. Meanwhile, SSA is improved to further extract fault trends and periodic components to optimize the signal representation. In the adaptive deep learning stage, an innovative temporal convolutional network (TCN) with a dynamic adjustment mechanism was developed to enable the neural network to adjust its convolutional kernel size according to different frequency components so as to accurately process signals of different frequencies. Validation on datasets from Case Western Reserve University and Xi'an Jiaotong University shows the superior performance of the proposed method, with diagnostic accuracies of 100% and 97.42% on these two public datasets, respectively.

Solo music performance assessment criteria: a systematic review.

Assessment is a crucial aspect of music performance. In pedagogical contexts, an effective assessment process can measure student achievement and inform instructional decisions that contribute to improving teaching and learning. However, music performance assessment is particularly challenging due to its inherent subjectivity, involving personal expression and interpretation, which can lead to divergent opinions. In this PRISMA systematic review (registration DOI: 10.17605/OSF.IO/CSM8Q), we aimed to delimit and analyze solo music performance assessment systems found in the literature to date, including their corresponding evaluation categories and descriptive criteria, rating methodology, and target audience. A search in three main scientific databases (Web of Science, Scopus, ERIC) was conducted using keywords associated with the topic of assessment in the field of solo music performance. Ultimately, 20 papers were selected and examined, resulting in 26 original assessment systems for analysis. Regarding sample characteristics, we found that studies mainly focused on evaluating high school and university students, with music teachers and faculty members serving as primary evaluators. Many assessment systems were designed to be applicable across various instruments, although some were tailored to specific instruments (e.g., piano, voice) and families (e.g., brass, woodwind). Systems typically structured evaluation around technical, interpretative/expressive, and various musical feature categories (e.g., pitch, rhythm, intonation), further elaborated with descriptive items. While five-point Likert scales were commonly used, recent studies indicated a shift towards rubrics for detailed feedback, which aids examiners' understanding and supports student progress. No differentiation was found in assessment criteria based on students' learning stages, suggesting an area for improvement in refining these assessment methods. This study identifies gaps and proposes improvements in existing assessment systems, providing a foundation for educators and policymakers to enhance curriculum design and instructional practices in music education.

Resilience assessment of FPSO leakage emergency response based on quantitative FRAM

FPSO production process is prone to leakage, and failure to respond promptly and effectively will lead to accident escalation and serious consequences. However, traditional safety assessment methods cannot handle the nonlinear relationships between human, technology, and organization in emergency response process. This study proposes a quantitative FRAM to evaluate the emergency response resilience of FPSO leakage. This method establishes a resilience evaluation framework including three tiers: function, ability, and system, which can quantify system resilience based on the variability of function. First, identify basic functions according to the four stages of monitoring, response, learning and anticipation in the emergency response process, and establish the FRAM model of FPSO leakage emergency response. Then, the quantitative FRAM and Monte Carlo simulation are combined to calculate the variabilities of functions under different operating conditions. Finally, based on the simulation results, the variabilities of basic functions are aggregated and statistically analyzed to quantify system resilience. The implementation process of this method is illustrated by a case study. The influence of different factors on the system resilience is analyzed by setting various operation scenarios, and critical functions are identified by sensitivity analysis, which can provide reference for improving system resilience and ensuring FPSO safety production.

Bias in Machine Learning: A Literature Review

Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in “unfair” outcomes. Bias in the “outside world” and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the said types of AI bias and providing a detailed overview of ways to identify and mitigate them. The different types of algorithmic bias that exist could be divided into categories based on the origin of the bias, since bias can occur during the different stages of the Machine Learning (i.e., ML) lifecycle. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches that have been proposed to identify and mitigate them. This study not only provides ready-to-use algorithms for identifying and mitigating bias, but also enhances the empirical knowledge of ML engineers to identify bias based on the similarity that their use cases have to other approaches that are presented in this manuscript. Based on the findings of this study, it is observed that some types of AI bias are better covered in the literature, both in terms of identification and mitigation, whilst others need to be studied more. The overall contribution of this research work is to provide a useful guideline for the identification and mitigation of bias that can be utilized by ML engineers and everyone who is interested in developing, evaluating and/or utilizing ML models.

Pure anomaly detection via self-supervised deep metric learning with adaptive margin

We address the problem of anomaly detection (AD) by a deep network pretrained using self-supervised learning for an auxiliary geometric transformation (GT) classification task. Our key contribution is a novel loss function that augments the standard cross-entropy by an additional term that plays a significant role in the later stages of self-supervised learning. The proposed enabling innovation is a triplet centre loss with an adaptive margin and a learnable metric, which relentlessly drives the GT classes to exhibit continuously improving compactness and inter-class separation. The pretrained network is finetuned for the downstream task using non-anomalous data only, and a GT model for the data is constructed. Anomalies are detected by fusing the output of several decision functions defined using the learnt GT class model. In contrast to the majority of existing methods, our approach strictly adheres to the pure AD design philosophy, which relies on the use of purely non-anomalous data for the design. Extensive experiments on four publicly available AD datasets demonstrate the effectiveness of the proposed contributions and lead to significant performance gains compared to the state-of-the-art (1.8% on F-MNIST, 1.0% on CIFAR-10, 1.2% on CIFAR-100, and 1.7% on CatVsDog). https://github.com/12sf12/Deep-Anomaly-Detection

Exploring the concept of <i>language awareness</i> in the Curriculum in English in Norway: discussing the potential of picture books for enhancing young learners’ language awareness

This exploratory article provides an introduction to language awareness and discusses the potential of picture books as a textual resource to enhance young learners’ understanding of how language works. The construct of language awareness is investigated and discussed according to its three main components: the cognitive, affective and social dimensions. It is argued that language awareness represents a holistic approach to language teaching and needs to be naturally integrated into teachers’ pedagogical practices. Furthermore, the article looks at how the last revision of the English subject curriculum in Norway, introduced in 2020, involves language awareness and how this concept is communicated. The curriculum seems to reflect a language learning theory that correlates strongly with the theoretical basis for a language awareness approach. It is noted that the teacher’s knowledge of language and engagement with language are crucial factors for facilitating discovery-oriented learning activities, which are considered critical to scaffolding learners’ engagement with language. Picture books are considered a valuable resource in this respect; the qualities that make this literary genre such a useful resource are outlined and reflected on. The article ends by presenting some selected picture books and exploring their potential for using them in the English classroom to promote learners’ engagement with language and hence, contribute to cultivating their language awareness.

No effects of cerebellar transcranial random noise stimulation on cerebellar brain inhibition, visuomotor learning, and pupil diameter.

Cerebellar brain inhibition (CBI) is an inhibitory output from the cerebellum to the primary motor cortex, which is decreased in early motor learning. Transcranial random noise stimulation (tRNS) is a noninvasive brain stimulation to induce brain plastic changes; however, the effects of cerebellar tRNS on CBI and motor learning have not been investigated yet to our knowledge. In this study, whether cerebellar tRNS decreases CBI and improves motor learning was examined, and pupil diameter was measured to examine physiological changes due to the effect of tRNS on motor learning. Thirty-four healthy subjects were assigned to either the cerebellar tRNS group or the Sham group. The subjects performed visuomotor tracking task with ten trials each in the early and late learning stages while receiving the stimulus intervention. CBI and motor evoked potentials were measured before the learning task, after the early learning stage, and after the late learning stage, and pupil diameter was measured during the task. There was no change in CBI in both groups. No group differences in motor learning rates were observed at any learning stages. Pupil diameter was smaller in the late learning stage than in the early learning stage in both groups. The cerebellar tRNS was suggested not to induce changes in CBI and improvement in motor learning, and it did not affect pupil diameter.

Scaffolding Learners’ Comprehension in Extensive Reading

The present study serves as an insightful teaching summary that focuses on students’ reading comprehension, through investigating the effectiveness and application of a teacher’s scaffolding in a one-semester extensive reading course. The teacher provides scaffolding throughout an entire semester of the extensive reading course. Students’ performance on multiple-choice questions of the first as well as the last unit has been collected and analyzed to determine whether they have enhanced their reading comprehension abilities or not. Based on the both qualitative and quantitative data, the findings reveal that students’ comprehension competence of extensive reading materials has notably increased after a semester of the teacher’s scaffolding. The result suggests that reading skills such as guessing, outlining, identifying topic sentence of each paragraph and determining the main idea of a passage can be applied as effective scaffolding techniques in extensive reading courses.

Unsupervised Adaptation Learning for Real Multiplatform Hyperspectral Image Denoising.

Real hyperspectral images (HSIs) are ineluctably contaminated by diverse types of noise, which severely limits the image usability. Recently, transfer learning has been introduced in hyperspectral denoising networks to improve model generalizability. However, the current frameworks often rely on image priors and struggle to retain the fidelity of background information. In this article, an unsupervised adaptation learning (UAL)-based hyperspectral denoising network (UALHDN) is proposed to address these issues. The core idea is first learning a general image prior for most HSIs, and then adapting it to a real HSI by learning the deep priors and maintaining background consistency, without introducing hand-crafted priors. Following this notion, a spatial-spectral residual denoiser, a global modeling discriminator, and a hyperspectral discrete representation learning scheme are introduced in the UALHDN framework, and are employed across two learning stages. First, the denoiser and the discriminator are pretrained using synthetic noisy-clean ground-based HSI pairs. Subsequently, the denoiser is further fine-tuned on the real multiplatform HSI according to a spatial-spectral consistency constraint and a background consistency loss in an unsupervised manner. A hyperspectral discrete representation learning scheme is also designed in the fine-tuning stage to extract semantic features and estimate noise-free components, exploring the deep priors specific for real HSIs. The applicability and generalizability of the proposed UALHDN framework were verified through the experiments on real HSIs from various platforms and sensors, including unmanned aerial vehicle-borne, airborne, spaceborne, and Martian datasets. The UAL denoising scheme shows a superior denoising ability when compared with the state-of-the-art hyperspectral denoisers.

A Critique of Stanley and Williamson's Intellectualist Account of Skill

One prominent intellectualist position in the debate on the nature of skill, famously defended by Stanley and Williamson (2001; 2017), claims that skill and knowing-how are reducible to knowledgethat. To defend this claim, Stanley and Williamson argue that skill and knowledge-that develop in a sufficiently similar way through different learning stages. In this paper we offer a novel argument to reject this version of intellectualism on methodological, descriptive, and conceptual grounds. We do so by drawing on the work of Heidegger, Dreyfus, and Ryle on skill. We first offer a descriptive account of skilful action based on the work developed by these authors, and we then move on to show that skill and knowledge-that exhibit significant differences. First, while skills cannot be imparted merely linguistically, some forms of knowledge-that can be exclusively imparted and learned verbally. Second, weshow that the temporal learning curves of skill and knowledge-that are importantly different: the former usually requires a more or less extensive training process, whereas the latter may be acquired in a relatively sudden fashion. Finally, we show that, while the structure of most forms of knowledge-that is aggregative, the structure of skilful understanding is dynamic and holistic, which entails that prior skill stages cannot be retrieved and re-enacted at will.

Conceptual Change and Developing Mental Motivation in Physics: Effects of Transformational Learning Theory

This study to investigate the effect of using transformative learning theory (TLT) -based teaching model on a conceptual change and developing mental motivation in Physics for secondary school students. The study follows the descriptive, analytical, and experimental method with a quasi-experimental design for experimental and control groups. sample consisted of (70) students from 1st-grade secondary school who are divided into two groups: the experimental group (34) students and the control group (36) students. To achieve the objectives of the study, A Conceptual Change Test (CCT) and A Mental Motivation Scale (MMS) were constructed. The results revealed that a TLT-based teaching model has effects on changing concepts and developing mental motivation in Physics. Moreover, there is a statistically significant correlation (r= 0.358) between the growth of mental motivation and the inducing of a conceptual change in physics for secondary school students. In light of the results, the study recommended conducting further studies on transformational learning theory (TLT) and conceptual change and mental motivation in various fields and learning stages.

Sifting the debris: Patterns in the SNR population with unsupervised ML methods

Context. Supernova remnants (SNRs) carry vast amounts of mechanical and radiative energy that heavily influence the structural, dynamical, and chemical evolution of galaxies. To this day, more than 300 SNRs have been discovered in the Milky Way, exhibiting a wide variety of observational features. However, existing classification schemes are mainly based on their radio morphology. Aims. In this work, we introduce a novel unsupervised deep learning pipeline to analyse a representative subsample of the Galactic SNR population (~50% of the total) with the aim of finding a connection between their multi-wavelength features and their physical properties. Methods. The pipeline involves two stages: (1) a representation learning stage, consisting of a convolutional autoencoder that feeds on imagery from infrared and radio continuum surveys (WISE 22 μm, Hi-GAL 70 μm and SMGPS 30 cm) and produces a compact representation in a lower-dimensionality latent space; and (2) a clustering stage that seeks meaningful clusters in the latent space that can be linked to the physical properties of the SNRs and their surroundings. Results. Our results suggest that this approach, when combined with an intermediate uniform manifold approximation and projection (UMAP) reprojection of the autoencoded embeddings into a more clusterable manifold, enables us to find reliable clusters. Despite a large number of sources being classified as outliers, most clusters relate to the presence of distinctive features, such as the distribution of infrared emission, the presence of radio shells and pulsar wind nebulae, and the existence of dust filaments.

Revisiting Conversation Discourse for Dialogue Disentanglement

Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute. In this paper, we propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. First of all, in feature encoding stage , we construct the heterogeneous graph representations to model the various dialogue-specific discourse structural features, including the static speaker-role structures (i.e., speaker-utterance and speaker-mentioning structure) and the dynamic contextual structures (i.e., the utterance-distance and partial-replying structure). We then develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Second, in model learning stage , we perform optimization with a hierarchical ranking loss mechanism, which groups dialogue utterances into different discourse levels and carries training covering pair-wise and session-wise levels hierarchically. Third, in inference stage , we devise an easy-first decoding algorithm, which performs utterance pairing under the easy-to-hard manner with a global context, breaking the constraint of traditional sequential decoding order. On two benchmark datasets, our overall system achieves new state-of-the-art performances on all evaluations. In-depth analyses further demonstrate the efficacy of each proposed idea and also reveal how our methods help advance the task. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.

KoPer: School Lıteracy Movement Strategy To Strengthen Readıng And Wrıtıng Skılls

The School Literacy Movement is an activity program that focuses on reading and writing skills. The School Literacy Movement was formed due to the low interest in reading and writing of students. So that the purpose of this school literacy movement is to achieve maximum quality of education quality related to reading and writing skills. The School Literacy Movement is a very important thing to develop through a strategy, namely KoPer (Literacy Group). KoPer (Literacy Group) has the purpose of strengthening reading and writing skills. This study aims to strengthen students' reading and writing skills through the school literacy movement strategy at Gonilan 02 State Elementary School, namely with KoPer (Literacy Group). The methods used in this research are observation, interview and documentation methods with the subjects of students, class teachers and the principal of Gonilan 02 State Elementary School. The results of this study are that there are three stages in the school literacy movement; the habituation stage, the learning stage and the development stage. Of the three stages, Gonilan 02 State Elementary School has implemented well through 15-minute reading activities, guided reading, shared reading, organizing a literacy-rich environment, compiling and making schedules, library visits, wall magazines and reading corners in class. In this KoPer (Literacy Group) there are strategies implemented by the school, namely, Digit Literacy, Library, Around the Region, and Literacy-Based Environments. This shows that KoPer(Literacy Group) is an alternative way to strengthen reading and writing skills for primary school students reading and writing skills for primary school students.