Implementation Of Learning Model Research Articles

Model Pembelajaran Berbasis Proyek (PJBL) Sebagai Implementasi Kurikulum Merdeka di TK Aba 25 Medan

This study aims to determine the implementation of the Project-Based Learning (PjBL) model as the implementation of the independent curriculum in Kindergarten Aba 25 Medan. This research uses the type of research and development (Research & Development). The data collection method is observation and documentation. Techniques in data analysis Techniques in data analysis used are qualitative descriptive. The development stage consists of planning, implementation, observation, and reflection stages. The results of the analysis show that in the first development stage when teachers implement the Project-Based Learning Model as the Implementation of the Independent Curriculum which is carried out reaches an average percentage of 83.33% with a very appropriate category while for child development during its implementation it reaches an overall average of 68.43% with a category that is still developing. The second development stage when teachers implemented the Project-Based Learning Model as the Implementation of the Independent Curriculum which was carried out reached an average percentage of 91.66% with the category being very appropriate, while for child development during its implementation it reached an overall average of 85.93% with the category of developing as expected

Implementation of the Angkola Culture-Based Experiential Learning Model in the Technological Era

Implementation of the Angkola Culture-Based Experiential Learning Model in the Technological Era

Applications of machine learning on electronic health record data to combat antibiotic resistance.

There is growing excitement about the clinical use of artificial intelligence and machine learning technologies. Advancements in computing and the accessibility of machine learning frameworks enable researchers to easily train predictive models using electronic health record data. However, there are several practical factors that must be considered when employing machine learning on electronic health record data. We provide a primer on machine learning and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct machine learning models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. Machine learning shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing potential dangers of, and barriers to, implementation of machine learning models in the clinic.

Implementasi Model Pembelajaran Berbasis Proyek dan Kooperatif Terhadap Pemahaman Siswa SMA

This study aims to examine the implementation of the Project-Based Learning (PBL) model and the Cooperative Learning (CL) model on the understanding of high school students. This study uses a qualitative approach with a focus on literature studies. The data sources that will be used in this study are scientific articles, books, journals, research reports, and other literature sources that are relevant and related to the influence of project-based and cooperative learning models in biology learning. The data analysis method used in this study is literature analysis or systematic literature review. The results of the study showed that both PBL and CL had a positive effect on the understanding of high school students. PBL provides opportunities for students to be actively involved in learning, build deep understanding, and develop critical thinking skills. CL enhances student understanding through interaction and collaboration, facilitates the exchange of understanding, and improves communication and cooperation skills. This research has important implications for educational practice, such as helping teachers choose the right learning model and encouraging the development of teacher training programs on PBL and CL

ТЕОРЕТИКО-МЕТОДОЛОГІЧНИЙ (КОНТЕКСТНИЙ) АНАЛІЗ ЗМІШАНОГОНАВЧАННЯ ТА ПЕРСПЕКТИВИ ЙОГО ВПРОВАДЖЕННЯ УСУЧАСНІЙ ОСВІТНІЙ ПРАКТИЦІ

The article is devoted to the problem of blended learning, which is increasingly attracting the attention of both domestic and foreign researchers and practicing educators. Taking into account the challenges of globalization, which have directly affected the education system, the problem of applying pedagogical practices that enable the combination of different formats of education has become extremely relevant. In particular, it is about the combination of face-to-face learning with various formats of electronic learning (asynchronous and/or synchronous), which is the essence of blended learning. Currently, the support and further implementation of blended learning in the educational process of higher education are quite complicated. Among other problems, the authors of the article single out the lack of a clear definition of the essence of blended learning and the incomplete readiness (methodological, technological and technical) of the pedagogical staff for the implementation of the blended learning model in practice. The article is devoted to the theoretical-methodological (contextual) analysis of the concept of "blended learning", the definition of its polysemic essence based on the study of domestic and foreign scientific sources and the identification of prospects for implementation in modern domestic and foreign educational practice. It was determined that currently blended learning is usually interpreted as a model, technology, method or a special process of acquiring knowledge. Essential features were analyzed; the main components of combined training are defined. The most typical definitions of blended learning are systematized, the essence of which is represented by the first words of the definition: unifying method, model, technology, methodology, teaching method, a type of hybrid methodology, purposeful process of acquiring knowledge, skills and abilities, educational concept, training, teaching system It is substantiated that, despite the indisputable advantages, there are certain challenges - both objective and subjective - that stand in the way of the implementation of blended learning.

ТЕОРЕТИКО-МЕТОДОЛОГІЧНИЙ (КОНТЕКСТНИЙ) АНАЛІЗ ЗМІШАНОГОНАВЧАННЯ ТА ПЕРСПЕКТИВИ ЙОГО ВПРОВАДЖЕННЯ УСУЧАСНІЙ ОСВІТНІЙ ПРАКТИЦІ

The article is devoted to the problem of blended learning, which is increasingly attracting the attention of both domestic and foreign researchers and practicing educators. Taking into account the challenges of globalization, which have directly affected the education system, the problem of applying pedagogical practices that enable the combination of different formats of education has become extremely relevant. In particular, it is about the combination of face-to-face learning with various formats of electronic learning (asynchronous and/or synchronous), which is the essence of blended learning. Currently, the support and further implementation of blended learning in the educational process of higher education are quite complicated. Among other problems, the authors of the article single out the lack of a clear definition of the essence of blended learning and the incomplete readiness (methodological, technological and technical) of the pedagogical staff for the implementation of the blended learning model in practice. The article is devoted to the theoretical-methodological (contextual) analysis of the concept of "blended learning", the definition of its polysemic essence based on the study of domestic and foreign scientific sources and the identification of prospects for implementation in modern domestic and foreign educational practice. It was determined that currently blended learning is usually interpreted as a model, technology, method or a special process of acquiring knowledge. Essential features were analyzed; the main components of combined training are defined. The most typical definitions of blended learning are systematized, the essence of which is represented by the first words of the definition: unifying method, model, technology, methodology, teaching method, a type of hybrid methodology, purposeful process of acquiring knowledge, skills and abilities, educational concept, training, teaching system It is substantiated that, despite the indisputable advantages, there are certain challenges - both objective and subjective - that stand in the way of the implementation of blended learning.

Implementation of deep learning models in predicting ESG index volatility

The consideration of environmental, social, and governance (ESG) aspects has become an integral part of investment decisions for individual and institutional investors. Most recently, corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts. While stock market prediction is a complex and challenging task, several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices. To address this challenge, we propose an integrated computational framework to implement deep learning model architectures, specifically long short-term memory (LSTM), gated recurrent unit, and convolutional neural network, to predict the volatility of the ESG index in an identical environment. A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data, technical indicators, and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction. The performance of the constructed models was evaluated using standard assessment metrics. Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model. Furthermore, a series of statistical analyses was conducted to validate the robustness and reliability of the model. Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.

Shadows of quantum machine learning

Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our models ends with the generation of a ‘shadow model’ from which the classical deployment becomes possible. We prove that: (i) this class of models is universal for classically-deployed quantum machine learning; (ii) it does have restricted learning capacities compared to ‘fully quantum’ models, but nonetheless (iii) it achieves a provable learning advantage over fully classical learners, contingent on widely believed assumptions in complexity theory. These results provide compelling evidence that quantum machine learning can confer learning advantages across a substantially broader range of scenarios, where quantum computers are exclusively employed during the training phase. By enabling classical deployment, our approach facilitates the implementation of quantum machine learning models in various practical contexts.

A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction

Background:: Predicting cardiac arrest is crucial for timely intervention and improved patient outcomes. Machine learning has yielded astounding results by offering tailored prediction analyses on complex data. Despite advancements in medical expert systems, there remains a need for a comprehensive analysis of their effectiveness and limitations in cardiac arrest prediction. This need arises because there are not enough existing studies that thoroughly cover the topic. Objective:: The systematic review aims to analyze the existing literature on medical expert systems for cardiac arrest prediction, filling the gaps in knowledge and identifying key challenges. Methods:: This paper adopts the PRISMA methodology to conduct a systematic review of 37 publications obtained from PubMed, Springer, ScienceDirect, and IEEE, published within the last decade. Careful inclusion and exclusion criteria were applied during the selection process, resulting in a comprehensive analysis that utilizes five integrated layers- research objectives, data collection, feature set generation, model training and validation employing various machine learning techniques. Results and Conclusion:: The findings indicate that current studies frequently use ensemble and deep learning methods to improve machine learning predictions’ accuracy. However, they lack adequate implementation of proper pre-processing techniques. Further research is needed to address challenges related to external validation, implementation, and adoption of machine learning models in real clinical settings, as well as integrating machine learning with AI technologies like NLP. This review aims to be a valuable resource for both novice and experienced researchers, offering insights into current methods and potential future recommendations.

Enhancing Students’ Geometry Learning Outcomes and Critical Thinking Skills through the Implementation of Problem Based Learning Model

To enhance students' critical thinking abilities, it is essential to adopt an interactive learning model where students actively participate, and the instructor acts as a facilitator. This can be achieved through the implementation of the Problem-Based Learning (PBL) model in the teaching and learning process. This study aimed to evaluate the improvement of learning outcomes and critical thinking skills through the application of the PBL model among students. Conducted as classroom action research, it involved 40 students from Yogyakarta State University. The research comprised two cycles, each consisting of two sessions. Instruments used included observation sheets for learning implementation, critical thinking assessment sheets, and tests. Data analysis was performed quantitatively using descriptive analysis methods. The findings revealed that (1) the learning process was effectively implemented, with observation data from both cycles rated as good, with a mode of 4 (good); (2) students' learning outcomes improved, with the average post-test scores increasing from 70.85 in Cycle 1 to 80.63 in Cycle 2; (3) students' critical thinking skills also enhanced, with the average score rising from 68.54 in Cycle 1 to 82.08 in Cycle 2. The conclusion of this study is that implementing PBL in the learning process can significantly enhance both critical thinking and learning outcomes. The research implies that teachers' professional competence can be developed through the application of PBL. It is recommended that teachers integrate the PBL model into their teaching practices to foster students' higher-order thinking skills, such as critical thinking, creativity, and scientific reasoning abilities.

Deep learning models and methods for solving the problems of remote monitoring of forest resources

Deep learning models and methods for solving the problems of remote monitoring of forest resources

Analisis Penerapan Model Pembelajaran Kooperatif Tipe STAD Dilihat Dari Hasil Belajar IPA

This study aims to describe the application of the STAD type cooperative learning model to science learning outcomes. This research is a qualitative descriptive study. The subjects of this study were fourth grade students and teachers at MI Muhammadiyah 27 Surabaya. Data collection techniques used in this study are observation, interviews, and documentation. The results of the study show that the application of the STAD Type Cooperative learning model has been carried out properly and well. The STAD Type Cooperative Learning Model has 5 steps, namely the goal of directing learning and motivating students, presenting information, organizing and guiding students in groups, giving quizzes, and giving awards. The impact of applying this learning model can be seen during the learning process, namely by looking at the teachers and students who have implemented the 5 steps of the learning model correctly, so that the learning process goes well. Interaction between students and students with teachers is also visible and students can interact in groups. There was an increase in science learning outcomes after the implementation of the STAD Type Cooperative learning model.

Penerapan Model Problem Based Learning Untuk Meningkatkan Keaktifan Belajar Peserta Didik Kelas V.A

This study aims to provide a comprehensive analysis of the improvement in learning engagement among students with the implementation of the problem-based learning (PBL) model on the IPAS Lesson Eye. This study utilizes a research method known as class action research (PTK), which consists of two learning cycles. His research focused on the number of students in the VA class at SDN 112 Palembang, which amounted to 24. This study's data collection involves the use of observation and documentation approaches during the learning process. For further data analysis, the research uses quantitative descriptive approaches. The findings indicated an increase in student activity markers. In Cycle I, 12 out of the 24 students obtained a learning activity indicator with a 50% percentage. In Cycle II, there was a notable and positive increase. Out of the 24 pupil participants, 21 were able to achieve the learning activity indicator, resulting in a percentage of 87.5%. Thus, it can be inferred that using the problem-based learning (PBL) model can improve students' learning experiences, particularly those in the VA class, when studying the topic of "light features" in the IPAS curriculum..

Pengaruh Penerapan Model Pembelajaran Project Based Learning (PJBL) Terhadap Kemampuan Pemecahan Masalah Spltv Peserta Didik Kelas X

The ability to solve problems is essential in math learning activities. It is required to have a learning model that can improve the students' SPLTV problem-solving abilities. This research aims to determine whether the students's SPLTV problem-solving abilities that have been taught by the learning model of Project-Based Learning are higher than the students's thinking with a Conventional Learning Model. This research has been conducted in SMA Negeri 3 Bitung by using a population of tenth graders in the school year 2023/2024. It is divided into four classes, and the total is 133 students. The sample selected in this research is from two classes, which are Class X E1 and Class X E3. The total of the students by each classes are 33 students and 34 students.The results of post-test participants in experiment classes and control classes are used as data on their problem-solving abilities. The result explained that the students's SPLTV problem-solving abilities, which use the PjBL Learning Model, are 81,48. This shows a higher average than the students that have been taught by the Conventional Learning Model, which is 65,53. To the full extent of 0.05. Accordingly, the implementation of the PjBL Learning model has an effect on the students' SPLTV problem-solving abilities.

Analisis Keberhasilan Implementasi Model Pembelajaran Berbasis Proyek dalam Pembelajaran Matematika Tingkat Perguruan Tinggi

This study aims to analyse the successful implementation of project-based learning model in mathematics learning at university level. Literature studies and field observations were conducted to evaluate the implementation of project-based learning models in several higher education institutions. The results showed that the implementation of project-based learning model in mathematics learning in higher education has positive impacts, among others: 1) increasing students‘ active involvement and motivation in the learning process, 2) developing mathematical competence, critical thinking skills, and other 21st century skills, 3) encouraging meaningful and contextualised learning, and 4) improving students’ collaboration and communication skills. However, there are some challenges in its implementation, such as: lecturer readiness, resource availability, and curriculum adjustment. The implication of the results of this study is the need for innovative mathematics curriculum development, increased lecturer competence, and adequate institutional support to optimise the implementation of project-based learning in higher education. This study provides valuable insights for mathematics education practitioners in designing and implementing student-centred learning oriented towards the development of 21st century competencies.

Penerapan Model Pembelajaran Kooperatif Tipe Two Stay Two Stray untuk Meningkatkan Hasil Belajar Matematika XI IPA 3 SMAN 10 Medan

Class XI IPA 3 students at SMA Negeri 10 Medan will be using the TSTS cooperative learning paradigm to enhance their mathematical learning results. A total of thirty-two students participated in the research, which used Classroom Action Research (CAR). Conducted in two cycles, each cycle includes two sessions using the TSTS model and one session for evaluation. Data were collected through observations of teacher and student activities, as well as learning outcome tests. From the first to the second cycle, the findings demonstrate that both the teacher's and the students' actions significantly improve. During iteration two, teacher activity reached the "very good" category with an average score of 40 and a percentage of 90.90%. Student activity also improved to the "very good" category, with increased interest, attention, participation, and presentation. Student achievement significantly increased, with the average score rising from 77.34 in the first cycle to 86.56 in the second cycle. Another positive change was the increase in the percentage of students who reached mastery, which went from 65.625% to 93.75% between the two cycles. The implementation of the TSTS cooperative learning model has proven effective in enhancing student participation, interaction, and collaboration, as well as helping them better understand the subject matter.

Revolutionizing travel experiences: An in-depth analysis of intelligent booking systems and behavioral patterns

This study delves into the intricacies of booking behaviors, focusing on the preference for tropical coastal cities during colder seasons, given the burgeoning need for personalized and intelligent travel recommendations in the modern tourism landscape. Traditional booking systems, with their static algorithms, have often neglected the dynamic interplay of environmental factors and user preferences, necessitating an evolution towards more sophisticated, adaptive models. To address this, the research employed advanced machine learning and artificial intelligence techniques, amalgamating data from diverse sources like major tourism websites, hotel platforms, and social media. The methodology involved meticulous data preprocessing to ensure data quality and depth, followed by the implementation of deep learning models and time series analysis, which were instrumental in deciphering complex patterns and offering predictive insights. This comprehensive approach facilitated the integration of various environmental factors such as weather conditions and temporal events, enabling the system to craft hyper-personalized recommendations that resonate with user preferences and environmental nuances. The findings were illuminating, unveiling a significant inverse relationship between temperature and bookings and highlighting the influential role of temporal events in driving bookings. The advanced predictive models showcased commendable accuracy, underscoring the transformative potential of such intelligent systems in enhancing user experiences and engagement in the tourism sector. These insights pave the way for the development of innovative strategies that can significantly elevate user satisfaction and industry growth. The study concludes that the integration of machine learning and AI in booking systems can revolutionize the tourism industry by offering unprecedented levels of personalization and adaptability, catering to the diverse and evolving needs of modern travelers.

A comprehensive review on zero-shot-learning techniques

Advancements in computational capabilities have enabled the implementation of advanced deep learning models across various domains of knowledge, yet the increasing complexity and scarcity of data in specialized areas pose significant challenges. Zero-shot learning (ZSL), a subset of transfer learning, has emerged as an innovative solution to these challenges, focusing on classifying unseen categories present in the test set but absent during training. Unlike traditional methods, ZSL utilizes semantic descriptions, like attribute lists or natural language phrases, to map intermediate features from the training data to unseen categories effectively, enhancing the model’s applicability across diverse and complex domains. This review provides a concise synthesis of the advancements, methodologies, and applications in the field of zero-shot learning, highlighting the milestones achieved and possible future directions. We aim to offer insights into the contemporary developments in ZSL, serving as a comprehensive reference for researchers exploring the potentials and challenges of implementing ZSL-based methodologies in real-world scenarios.

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Purpose: The aim of the study was to examine the role of machine learning in enhancing risk management strategies in financial institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that integration of machine learning into risk management strategies within financial institutions has demonstrated significant potential for enhancing decision-making processes and mitigating various risks. The study have consistently shown that machine learning algorithms outperform traditional statistical methods in areas such as credit risk assessment, fraud detection, market risk management, and loan portfolio optimization. These advancements have led to improved accuracy, efficiency, and timeliness in risk assessment, enabling financial institutions to make more informed decisions while reducing losses and enhancing overall performance. Unique Contribution to Theory, Practice and Policy: Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH) & Agency Theory may be used to anchor future studies on role of machine learning in enhancing risk management strategies in financial institutions. Invest in building robust data infrastructure and governance frameworks to support the implementation of machine learning models in risk management practices. High-quality data is crucial for training accurate and reliable machine learning algorithms. Establish regulatory guidelines and standards for the responsible use of machine learning in risk management within the financial industry. These guidelines should address issues such as model transparency, fairness, and accountability to ensure ethical and responsible practices.

Application of the Machine Learning Method to Determine Spring Load Limits and Winter Weight Premium

Freight transportation plays a crucial role in sustaining the Canadian economy. However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Premium (WWP) is used to reduce the impact of SLR on trucking operations by allowing higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of the pavement. Different methods have been proposed to improve the application of SLR and WWP; however, they rely mainly on indirect indices, such as the cumulative thawing index and cumulative freezing index, which pose challenges in their calculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a novel approach, machine learning models directly derive the start and end dates of SLR and WWP from frost and thaw depths in the pavement structure which are determined by pavement temperature and moisture content. In contrast to previous studies that neglected the influence of soil moisture content on determining the start and end dates of SLR and WWP, this study examines the variation in soil moisture content to evaluate the validity of existing theories. The findings reveal a high level of agreement between the machine learning model’s estimations of frost and thaw depths and the measured values, with R2 values exceeding 0.91.