Effect Of Learning Model Research Articles

Applying a Meta-synthesis Approach to Present a Blended Learning Model for Talent Development

Background: With the rapid development of healthcare, traditional teaching has been unable to meet the learning needs of hospital staff training, which has led to talent development. With the development of Internet technology, blended learning seems to be a new available choice to solve the current predicament. However, the effectiveness of blended learning is still controversial. In addition, most studies have primarily evaluated the teaching effect unilaterally. Objectives: The purpose of this study was to apply a meta-synthesis approach to synthesize research on blended learning models for talent development in healthcare settings. By examining a body of evidence from peer-reviewed studies, the authors aimed to identify common elements of effective blended learning models applied to hospital employee training. This synthesis provided a foundation for developing a comprehensive blended learning model tailored to the specific needs of hospital employees. Methods: In this study, the meta-synthesis approach was used to synthesize qualitative evidence in relation to blended learning factors in talent development. A comprehensive search strategy was used to retrieve articles from ScienceDirect, Emerald, PubMed, Web of Science, and Scopus within 1990 to 2023. To find additional studies, the bibliographic search of included studies and review articles and a random search in Google Scholar, Research Gate, and Google were carried out. A total of 751 studies were identified. After reviewing the full text of the articles, 89 studies were selected. This review evaluated studies that explained blended learning and talent development among healthcare employees. Eighty-six studies were selected and included in the meta-synthesis process after critical appraisal using the Critical Appraisal Skills Program. Results: The findings consisted of the model’s dimensions, components, and indices. The five identified dimensions include specifying learning needs, learning campaign elements, learning proposal, content strategy, and facilitation model consisting of 22 components and 76 indices. Conclusions: The identification and application of the obtained components will allow the policymakers and managers of the health system field to replace the traditional training of employees with blended learning, culminating in employee talent development.

A Model for Effective Learning in Competition: A Pedagogical Tool to Enhance Enjoyment and Perceptions of Competency in Physical Education Lessons for Young Children.

To date, little research on competition has focused on young children (6-7-year-olds). A total of ninety-seven participants (51 boys and 46 girls) from two English primary schools completed two physical education (PE) lessons, which included three different activity challenges. The control group undertook the same activities in both lessons. The experimental group did likewise but were set high-, low-, or mid-level targets in lesson two based on individual scores from lesson one. The children completed a post-session questionnaire to assess (i) enjoyment levels and (ii) which activity they perceived they performed best in. The results found that children both improved and enjoyed the lesson most when low- or mid-level targets were set. Indeed, when targets were absent (in the control group), children's competency scores regressed. Likewise, children perceived that they performed best in the activity where lower targets were set. Their perceived competency included both tangible and intangible reasons. From these results, it is recommended that for practitioners working with 6-7-year-old children, the most effective learning in competition uses individualised and competitive targets and challenges as a means to garner greater enjoyment in PE. Understanding each child's self-efficacy and motivation is key, which requires ongoing evaluation and assessment during PE lessons.

Feature selection and its combination with data over-sampling for multi-class imbalanced datasets

Feature selection aims at filtering out some unrepresentative features from a given dataset in order to construct more effective learning models. Furthermore, ensemble feature selection by combining multiple feature selection methods has shown its outperformance over single feature selection. However, the performances of different (ensemble) feature selection methods have not been fully examined over multi-class imbalanced datasets. On the other hand, for class imbalanced datasets, one widely considered solution is to re-balance the datasets by data over-sampling, which generates some synthetic examples for the minority classes. However, the effect of performing (ensemble) feature selection on over-sampling multi-class imbalanced datasets has not been investigated. Therefore, the first research objective is to examine the performances of single and ensemble feature selection methods by fifteen well-known filter, wrapper, and embedded algorithms in terms of classification accuracy. For the second research objective, two orders of combining the feature selection and over-sampling steps are compared in order to find out the best combination procedure as well as the best combined algorithms. The experimental results based on ten different domain datasets containing low to very high feature dimensions show that ensemble feature selection methods slightly perform better than single ones. However, their performance differences are not big. To combine with the Synthetic Minority Oversampling Technique (SMOTE) over-sampling algorithm, performing feature selection first and over-sampling second outperforms the other procedure. Although the best combined algorithms are based on ensemble feature selection, eXtreme Gradient Boosting (XGBoost), as the single best feature selection algorithm, combined with SMOTE provides very similar classification performance to the best combined algorithms. To consider the issues of classification performance and compactional cost, the optimal solution is based on the combined XGBoost and SMOTE.

Movies Rating Prediction using Supervised Machine Learning Techniques

Social media are the most enormous and fast data technology on the internet. A massive amount of data is generated from the internet day by day. The efficient processing of such massive records is hard, so we require a system that learns from these facts and makes a useful prediction like machine learning. Machine learning strategies make the systems learn it. Applying K-nearest neighbors (KNN), Support Vector Machine (SVM), as well as Random Forest Model (RF), three supervised machine learning approaches, we attempted to develop a model for a movie recommendation system. This research not only compares the three models but also assesses how they compare favorably against one another in the areas of accuracy, accuracy, recalls, and F1 score. First of all, we discuss a summary of machine learning techniques used for recommendations of items in the past. We subsequently detail the most effective machine learning model for making predictions and recommendations about movie ratings. Predictions of movie reviews are made using K-nearest neighbor (KNN) and Support Vector Machine (SVM) models as well as the Random Forest Model (RF). The efficacy of all models was tested with Movie Lens 100k and 1M dataset. The random forest Model performs better as compared to other models. Furthermore, we summarize the current challenges based on state of art approaches. Finally, we present open issues and future directions for further research.

A cross-domain state monitoring method for high-speed train brake pads based on data generation under small sample conditions

The proper functioning of the high-speed train brake system is of utmost importance to ensure the train comes to a halt at the intended distance. Among the key components, brake pads are susceptible to abnormal wear over prolonged usage, posing a safety risk to train operations. However, limited real-world data on brake pad damage poses challenges for effective monitoring. This paper proposes an effective transfer learning model that utilizes a small amount of data at different speeds, which can solve the small sample data problem on brake pad damage. Initially, a deep convolutional generative adversarial network (DCGAN) is employed to simulate and expand a limited dataset of vibration signals. The generated signals accurately preserve both the high-frequency and low-frequency characteristics observed in the actual signals. Subsequently, a domain-adversarial neural network (DANN) is utilized to map features from the source and target domains onto a shared feature space, enabling multi-domain feature extraction and adaptation. Finally, an intelligent monitoring network is employed to identify the health status of unlabeled brake pads under fluctuating speeds. Experimental results demonstrate the proposed approach outperforms other commonly used algorithms, providing evidence of the feasibility to monitor the small sample conditions of high-speed train brake pads.

Enhancing real human detection and people counting using YOLOv8

The ability to accurately recognize and count persons is crucial in many real-world applications, including surveillance, security, and crowd management, making it one of computer vision’s most fundamental tasks. You Only Look Once (YOLO) is one of the most effective deep learning models for object identification and counting in recent years. This research seeks to learn more about the YOLOv8 algorithm for precisely counting people in still photos and moving videos. The YOLO method has been at the forefront of computer vision due to its ability to recognize things in real time. People in a crowd typically overlap and block one other, and perspective effects can result in enormous changes in human size, shape, and appearance in the image, all of which make accurate headcounts challenging.The YOLO methodology and its adaptation for population census are the subject of this research. Results from experiments support the usefulness of the proposed approach. Surveillance, crowd control, traffic monitoring, retail analytics, event management, and urban planning are just some of the potential uses highlighted by the findings of this study. Mean Average Precision (MAP) numbers demonstrate that the identification procedure was successful, and the counting process was accurate to within 100%.

A real-time air-writing model to recognize Bengali characters

<abstract><p>Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages.</p></abstract>

Применение моделей машинного обучения для контроля качества вибродиагностических данных

Article aims to determine the most effective machine learning model for clustering vibration diagnostics data. The research includes analysis of various models and methods such as-means, Agglomerative Clustering, TimeSeriesKMeans and CatBoost. The goal is to select a method that can best identify the data structure and improve understanding of the characteristics of vibration signals. The results of the study can be useful for the development of effective monitoring and diagnostic systems for equipment, as well as for improving the reliability and performance of technical systems.

A SHAP and LIME based Explainable AI Solution for Predicting Chronic Kidney Diseases

Chronic Kidney Disease (CKD) presents a major global health issue, contributing to renal failure, cardiovascular problems, and elevated mortality rates. This research focuses on creating an effective machine learning (ML) model for CKD prediction, utilizing 25 features that represent different health indicators. We implemented three main algorithms: Logistic Regression (LR), K-Nearest Neighbors (KNN), and Decision Tree, along with extensive preprocessing, feature selection, and hyperparameter optimization. Based on accuracy, the models were evaluated, along with the confusion matrix, and ROC curves. Furthermore, we employed SHAP (SHapley Additive exPlanations) for model interpretability, offering insights via summary plots, waterfall plots, force plots, and dependence plots. Our results indicate high prediction accuracy, with a 10% increase in performance with the Decision Tree model achieving near-perfect performance, highlighting its potential for early CKD detection and contributing to timely medical interventions.

Lack of visualization on virtual platforms

Education is the most powerful weapon that can be used to change the world, whether in a developed or underdeveloped country. The present e-learning frameworks are changing rapidly with the change in technology and the need for education. However, one of the main problems is the unavailability of effective learning models and collaborative tools for complex topics, especially in science courses. This is due to the need for more visualization in learning platforms, one of the major problems that need attention. Otherwise, users might need help with falling short of potential in effective teaching shortly. This paper investigates the issue of the need for visualization methods. It gives an idea of how to solve this problem by testing it through various phases and forms of evaluation. Users surveyed the use of visualization methods in practice, recorded the results, and will carry out research based on the results obtained. An extensive literature survey was done on academic and industry reports in the context of the effectiveness of visualization on virtual platforms. Additionally, a primary survey was conducted using a research questionnaire to gather opinions from the primary stakeholders of the research, namely, students and instructors. Empathy tools were employed to comprehend the emotional state of stakeholders, providing enhanced insights into the identified problem. The work progressed by employing defining tools to concentrate on user needs and finalize the problem statement.

Development of Controversial Issues Learning Models in Improving Student's Critical Thinking Skills

This research aims to develop a learning model for controversial issues in Civic Education at Jakarta State University to enhance students' critical thinking skills. The study follows the Development and Research (R&D) method and was conducted from March to August. The participants were students taking the Civic Education course at The State University of Jakarta, which is a mandatory course. Data on critical thinking skills were collected through observation, interviews, and assessments. The development of the learning model for controversial issues in the Civic Education course involved planning, processing, designing, material preparation, trials, and feasibility analysis. The research resulted in an efficient and effective learning model. Students' critical thinking skills and learning outcomes improved significantly, as observed through their active participation in discussions, analysis, and argumentation related to controversial issues. Students responded to these issues based on facts and data, thereby demonstrating their improved ability to engage in critical thinking. Overall, this research contributes to the enhancement of critical thinking skills among students by providing them with a structured learning model that enables them to analyze and respond to controversial issues based on reliable information.

Machine Learning for Predicting Distant Metastasis of Medullary Thyroid Carcinoma Using the SEER Database.

We aimed to establish an effective machine learning (ML) model for predicting the risk of distant metastasis (DM) in medullary thyroid carcinoma (MTC). Demographic data of MTC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the National Institutes of Health between 2004 and 2015 to develop six ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1-score, and area under the receiver operating characteristic curve (AUC). The association between clinicopathological characteristics and target variables was interpreted. Analyses were performed using traditional logistic regression (LR). In total, 2049 patients were included and 138 developed DM. Multivariable LR showed that age, sex, tumor size, extrathyroidal extension, and lymph node metastasis were predictive features for DM in MTC. Among the six ML models, the random forest (RF) had the best predictability in assessing the risk of DM in MTC, with an accuracy, precision, recall rate, F1-score, and AUC higher than those of the traditional binary LR model. RF was superior to traditional LR in predicting the risk of DM in MTC and can provide a valuable reference for clinicians in decision-making.

Hardware Efficient Direct Policy Imitation Learning for Robotic Navigation in Resource-Constrained Settings.

Direct policy learning (DPL) is a widely used approach in imitation learning for time-efficient and effective convergence when training mobile robots. However, using DPL in real-world applications is not sufficiently explored due to the inherent challenges of mobilizing direct human expertise and the difficulty of measuring comparative performance. Furthermore, autonomous systems are often resource-constrained, thereby limiting the potential application and implementation of highly effective deep learning models. In this work, we present a lightweight DPL-based approach to train mobile robots in navigational tasks. We integrated a safety policy alongside the navigational policy to safeguard the robot and the environment. The approach was evaluated in simulations and real-world settings and compared with recent work in this space. The results of these experiments and the efficient transfer from simulations to real-world settings demonstrate that our approach has improved performance compared to its hardware-intensive counterparts. We show that using the proposed methodology, the training agent achieves closer performance to the expert within the first 15 training iterations in simulation and real-world settings.

Leveraging Ensemble Learning with Generative Adversarial Networks for Imbalanced Software Defects Prediction

Software defect prediction is an active research area. Researchers have proposed many approaches to overcome the imbalanced defect problem and build highly effective machine learning models that are not biased towards the majority class. Generative adversarial networks (GAN) are one of the state-of-the-art techniques that can be used to generate synthetic samples of the minority class and produce a balanced dataset. However, it was not investigated thoroughly in the area of imbalanced defect prediction. In this paper, we proposed to combine GAN-based methods with boosting ensembles to yield robust defect prediction models. GAN-based methods were used to balance the defect datasets, and the AdaBoost ensemble was employed to classify the modules into defective and non-defective modules. Our proposed approach was investigated within the context of 10 software defect datasets with different imbalance ratios. Wilcoxon effect size and Scott–Knott effect size difference tests were used as statistical tests to quantify the model’s performance differences statistically. Empirical results indicated that GAN-based methods need hyperparameter optimization when used for imbalanced software defect prediction. In comparison to the traditional sampling techniques, GAN methods outperformed all traditional techniques when used for imbalanced defect prediction. Lastly, results demonstrated that GAN-based methods should not be combined with undersampling to handle imbalance problems.

Validity of a Game-Based Learning Model for Teaching Floor Exercise Roll Basic Techniques to Junior High School Students in Indonesia

The goal of this instructional style is to help students become more proficient at the floor exercise roll by using a game-based approach and other forms of media. The purpose of this research is to provide evidence for the efficacy of a model for teaching the fundamentals of the floor exercise roll through the medium of a video game in junior high school. Three subject matter experts and three media specialists evaluated the research and validated the learning model using a set of established criteria. This study is occuring within the product development phase of the Borg and Gall research and development paradigm. A questionnaire was utilised to obtain data for determining the model's accuracy. Descriptive percentages were used to assess the data. The average model validity ratings found in this research show that the material expert validator is 83.33 percent valid and the media expert validator is 89.4 percent valid. The validity score generated by the combination of the three validators is very high quality. Consequently, it is reasonable to infer that the floor exercise roll basic technique learning model based on a game is genuine and can be applied in the context of school-based sports education. Sports educators and curriculum designers can use the favourable feedback from material and media experts to inform the design of more effective learning models for students to become proficient in floor exercises and fundamental rolling technique

Improving Students Liveliness in Islamic religius education toward PBL Learning Models Application

The main focus of PAI subjects is the development of student character but in reality when expected with field situations it can be seen that student participation in class is often lacking. This study aims to determine the effect of problem-based learning (PBL) on increasing student learning activities in PAI learning. Using a qualitative research approach, this research uses classroom behavior as a research design and involves high school students as participants. This study conducted interviews, observations and documentation in its data collection techniques. The results showed that there was a significant increase in student activity after implementing PBL. Students also reported better learning experiences and higher learning success. This research contributes to the development of an interactive and effective learning model to increase students' understanding and awareness of Islam.

Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein–protein interactions

BackgroundProtein–protein interactions (PPIs) are heavily involved in many biological processes. Consequently, the identification of PPIs in the model plant Arabidopsis is of great significance to deeply understand plant growth and development, and then to promote the basic research of crop improvement. Although many experimental Arabidopsis PPIs have been determined currently, the known interactomic data of Arabidopsis is far from complete. In this context, developing effective machine learning models from existing PPI data to predict unknown Arabidopsis PPIs conveniently and rapidly is still urgently needed.ResultsWe used a large-scale pre-trained protein language model (pLM) called ESM-1b to convert protein sequences into high-dimensional vectors and then used them as the input of multilayer perceptron (MLP). To avoid the performance overestimation frequently occurring in PPI prediction, we employed stringent datasets to train and evaluate the predictive model. The results showed that the combination of ESM-1b and MLP (i.e., ESMAraPPI) achieved more accurate performance than the predictive models inferred from other pLMs or baseline sequence encoding schemes. In particular, the proposed ESMAraPPI yielded an AUPR value of 0.810 when tested on an independent test set where both proteins in each protein pair are unseen in the training dataset, suggesting its strong generalization and extrapolating ability. Moreover, the proposed ESMAraPPI model performed better than several state-of-the-art generic or plant-specific PPI predictors.ConclusionProtein sequence embeddings from the pre-trained model ESM-1b contain rich protein semantic information. By combining with the MLP algorithm, ESM-1b revealed excellent performance in predicting Arabidopsis PPIs. We anticipate that the proposed predictive model (ESMAraPPI) can serve as a very competitive tool to accelerate the identification of Arabidopsis interactome.

Effectiveness of Learning Breaststroke Swimming with a Visualization Model of Basic Swimming Movement in High Grade Students at SD Negeri 8 Metro Center

This study aims to identify the effectiveness of learning breaststroke swimming with a visualization model of basic swimming motion in elementary school students at SD Negeri 8 Metro Pusat. The method used is the experimental method with a population of 20 students. The sample used is total sampling by taking a sample where the entire population is students of SD Negeri 8 Metro Pusat, totaling 20 people. From the results of the data analysis research, it was obtained that t-count = 15 > t-table = 2.262 significant level α 5% for male students and t-count = 13.856 > t-table = 2.262 significant level α 5% for female students, degrees of freedom dk = N-1 = 10-1 = 9, which means that there is a significant difference in the pre-test and post-test data for boys and girls. Because t-count 15 > t-table 2.262 for male students and t-count 13.856 > t-table 2.262 it can be concluded that there is an application of an effective visualization learning model for the basic motion of breaststroke swimming in high grade elementary school students at SD Negeri 8 Metro Pusat (accepted).

Pendampingan Penerapan Model Pembelajaran Kecerdasan Majemuk bagi Guru di SMA 16 Bone

Teachers are required to have the ability to use various methods or combine several relevant methods so that they are able to integrate the development of students' multi-intelligences in the learning process. Based on the results of observations, information was obtained from the school that they experienced several problems in the teaching and learning process, namely that teachers in the classroom learning process were still classical, teachers had never received training in multiple intelligences learning models, while on the other hand students were required to be creative in developing knowledge in the learning process. This assistance is intended to improve teachers' abilities in implementing effective learning models by implementing multiple intelligence learning models which can be used as fun learning models in the classroom. This assistance aims to provide understanding to high school teachers about effective learning by implementing the multiple intelligence learning model, increasing the ability of high school teachers in planning, implementing and evaluating using the multiple intelligence learning model, implementing this learning model in high school according to the conditions and potential of students. The methods used include lecture, question and answer, discussion, practice and assignment methods.

Effect of project-based learning models and achievement motivation on students' short story text writing skills

The implementation of learning short story writing skills is still oriented towards completing tasks designed by the teacher. Also, it seems conventional, so developing students' short story writing skills takes time and effort. Through this research, the authors review the effect of the project-based learning model on learning outcomes for students with high and low achievement motivation; and the interaction between the project-based learning model and achievement motivation. This research belongs to the quantitative research that applies the experimental method, involving two sample classes: class X hospitality 2 as the experimental class and class X clothing 4 as the control class. The results of the data analysis provide conclusions. First, the project-based learning model influences student learning outcomes. Second, the project-based learning model influences students' learning outcomes with high achievement motivation. Third, project-based learning model influences the learning outcomes of students with low achievement motivation. The project-based learning learning model can be used as an effective alternative learning model in improving students' short story writing skills.