Mathematical Learning Model Research Articles

자기조절학습을 위한 지오지브라 클래스룸 기반 수학학습 모델 개발 및 효과성 분석

This study developed a mathematics learning model utilizing GeoGebra Classroom, and analyzed how the use of this model in mathematics learning affected high school students’ self-regulated learning abilities and strategies. The researchers of the current study constructed an 11-session mathematics learning model using GeoGebra Classroom, and implemented this model to actual mathematics classrooms. A total of 107 participants completed a self-regulated learning abilities and strategies measurement survey, which included 45 questions. The students' self-regulated learning abilities were measured before and after the GeoGebra Classroom-based learning model was implemented. Collected data were analyzed, and the findings of the study indicated statistically significant differences between the pre- and post-test results. This study demonstrated that the implementation of GeoGebra Classroom-based mathematics learning model positively impacted high school students' self-regulated learning abilities and strategies in their mathematics learning.

IMPLEMENTASI MODEL PEMBELAJARAN PROBLEM BASED LEARNING (PBL) DALAM MENINGKATKAN MOTIVASI BELAJAR MATEMATIKA SISWA

Motivation to learn plays a crucial role in achieving learning success in school. Low motivation to learn among students results in low learning outcomes. The Problem-Based Learning model was chosen because it requires students to be active in research and problem-solving processes during learning. This study aims to increase the motivation to learn among class XI IPA 2 students at SMAN 2 Denpasar for the 2023/2024 academic year. The research subjects were 45 students from class XI IPA 2. The study used two cycles with four stages in each cycle: planning, implementation, observation, and reflection. Data collection was done by distributing questionnaires related to student motivation. The obtained data was then analyzed qualitatively. The pre-cycle motivation to learn among students was 44.02, while the first cycle questionnaire results showed an achieved result with an average of 62.84. Subsequently, the second cycle questionnaire results showed an achieved result with an average of 81.20. Based on the research results, it was found that the implementation of the Problem-Based Learning model in mathematics learning for class XI IPA 2 students at SMA Negeri 2 Denpasar in the 2023/2024 academic year can increase student motivation.

PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM GAMES TOURNAMENT (TGT) PADA PEMBELAJARAN MATEMATIKA SISWA

Mathematics is one subjects taught at all levels and types of formal education with a major contribution to various fields such as technology, industry, finance, and so on. However, mathematics is not a subject that are interested because difficult, scary, uninteresting and boring. This mindset creates negative impressions and experiences. Therefore, teachers should use appropriate models, strategies, methods and approaches so that they can build and improve positive attitudes towards mathematics. The aim of this research is to determine the application of the Team Games Tournament (TGT) type cooperative learning model in mathematics learning for students in class VII H at SMPN 8 Denpasar. The TGT model allows students to learn to collaborate, compete and be responsible while still being guided by learning objectives. This research uses test and non-test instruments in the form of observation sheets, interviews and student learning outcomes tests. The results of this research were that in Cycle I the percentage of students who completed was 56.10%, then increased in Cycle II with a completion percentage of 80.49%. Therefore, it can be said that TGT type cooperative learning can improve the learning outcomes of class VII H students at SMPN 8 Denpasar.

Mathematics Learning Model for Children with Dyscalculia through Special Intervention

This research aimed to formulate a model of mathematical learning difficulties for children with dyscalculia to help children with dyscalculia overcome mathematics problems at school. This research is important to carry out considering that dyscalculia is a serious problem experienced by many students throughout the world. The SDTA model was built by analogizing the STIR model to non-communicable diseases. In this study, of 1247 students who conducted a series of tests (screening tests), there were 121 students, or 9.7% were initially identified as having dyscalculia. Furthermore, observations were carried out and confirmation was carried out with parents and teachers so that 119 students tested positive for dyscalculia. The research results showed that after intervention through treatment by the teacher, dyscalculia students experienced a very drastic decrease, especially in the material on recognizing and ordering numbers. Likewise, the results of model simulations on multiplication operation material illustrated the decline in students with dyscalculia. Specifically for division operations, the treatment process took relatively longer. Therefore, the simulation results in the SDTA model reflected students who are identified as having dyscalculia, along with planned intervention/ treatment, students could pass through the mathematics learning phase successfully.

A Mathematical and Hybrid Deep Learning Model for Real-Time Intrusion Detection in IoT-Based Electric Vehicle Charging Stations

A Mathematical and Hybrid Deep Learning Model for Real-Time Intrusion Detection in IoT-Based Electric Vehicle Charging Stations

Efektivitas Model Pembelajaran Problem Based Learning dan Problem Solving Ditinjau dari Kemampuan Pemecahan Masalah Matematika Siswa Sekolah Dasar

This study aims to determine the difference in the effectiveness of Mathematics problem-solving between fifth-grade students of Gugus Dwija Wiyata Elementary School who use Problem-Based Learning (PBL) and Problem Solving (PS) learning models. The research method used is an experiment with a pretest-posttest control group design. Experimental Group 1 (PBL) consisted of 44 students, while Experimental Group 2 (PS) consisted of 43 students. Data were collected through pretests and posttests using test questions, and data analysis was performed using the t-test. The results showed a difference in effectiveness between Experimental Group 1 and Experimental Group 2, with a significance value (2-tailed) of 0.001 < 0.05. This indicates that the PBL learning model is more effective in improving Mathematics problem-solving skills compared to the PS model. Therefore, it is recommended that educators consider using the PBL learning model in Mathematics teaching at the elementary school level.

Peningkatan Hasil Belajar Siswa melalui Model Kooperatif Tipe TGT dalam Pembelajaran Matematika di Kelas XI

Purpose – Several phenomena have been observed, including a notable lack of active student engagement, which has led to poor learning outcomes. Consequently, this research aims to investigate the enhancement of student learning outcomes through the implementation of the TGT (Teams-Games-Tournaments) cooperative learning model in eleventh-grade mathematics education.Method – The research method employed is Classroom Action Research (CAR), which comprises two cycles, each encompassing four stages: planning, action, observation, and reflection. The research instrument consists of test questions designed in accordance with the learning objectives. The success indicator for this research is that 75% of the students achieve learning mastery.Findings – The integration of the TGT (Teams-Games-Tournaments) cooperative learning model can improve student learning outcomes. Based on the research results and data analysis in cycle 1, the percentage of students achieving learning completeness was 42%. Since this did not meet the success indicators, the research continued into cycle 2, where an increase of 36% was observed. The percentage of students achieving learning completeness in cycle 2 was 78%, thereby meeting the success indicators, and thus, the research concluded at cycle 2. Research Implications – The implications of this research highlight the importance of selecting a learning model that promotes active student participation in the learning process to enhance students' learning outcomes in vector material. However, a limitation of this research is the restricted sample size, as it focuses solely on one school.

The Influence of Implementing Realistic Mathematics Learning Model on The Mathematics Learning Outcomes

The Influence of Implementing Realistic Mathematics Learning Model on The Mathematics Learning Outcomes

The influence of the Knisley mathematics learning model on the mathematical connection ability and self-confidence of high school students

This research aims to conduct a study that focuses on the effect of applying the Knisley mathematics learning model on students' mathematical connection abilities and self-confidence.This research is research using a quasi-experimental method with a research design in the form of a pretest-posttest control group design. The sampling technique used was cluster random sampling. The population in this study were all class XI students at SMAN 1 Tirtayasa, with a sample of 2 classes to be used as experimental classes, namely class The instruments used in this research were mathematical connection ability tests, self-confidence questionnaires, observation sheets and interviews. Based on data analysis, it was concluded that (1) Learning using the Knisley mathematics learning model had a positive effect on students' mathematical connection abilities, (2)Learning using the Knisley mathematics learning model has a positive effect on students' self-confidence.

Identifying Importation and Asymptomatic Spreaders of Multi-drug Resistant Organisms in Hospital Settings.

Healthcare-associated infections (HAIs) due to multi-drug resistant organisms (MDROs) are a significant burden to the healthcare system. Patients are sometimes already infected at the time of admission to the hospital (referred to as "importation"), and additional patients might get infected in the hospital through transmission ("nosocomial infection"). Since many of these importation and nosocomial infection cases may present no symptoms (i.e., "asymptomatic"), rapidly identifying them is difficult since testing is limited and incurs significant delays. Although there has been a lot of work on examining the utility of both mathematical models of transmission and machine learning for identifying patients at risk of MDRO infections in recent years, these methods have limited performance and suffer from different drawbacks: Transmission modeling-based methods do not make full use of rich data contained in electronic health records (EHR), while machine learning-based methods typically lack information about mechanistic processes. In this work, we propose NEURABM, a new framework which integrates both neural networks and agent-based models (ABM) to combine the advantages of both modeling-based and machine learning-based methods. NEURABM simultaneously learns a neural network model for patient-level prediction of importation, as well as the ABM model which is used for identifying infections. Our results demonstrate that NEURABM identifies importation and nosocomial infection cases more accurately than existing methods.

Pengaruh Model Pembelajaran Contextual Teaching And Learning (CTL) Terhadap Hasil Belajar Siswa Kelas IV SDN Murtajih 1 Pada Mata Pelajaran Matematika Materi Luas Bangun Datar

The purpose of this study was to determine the significant effect of the Contextual Teaching and Learning model on the learning outcomes of class IV students at SDN Murtajih 1 in the mathematic subject area of flat shapes. This study uses quantitative methods with a Pre-Experimental Design and One Group Pretest-Posttest Design model. The sample was 23 students using the Total Sampling technique. Data collection used test instruments for student cognitive learning outcomes and teacher activity observation sheets. Based on the paired simple t-test, tcount 15.495 > ttable 2.073 shows that Ho is rejected and Ha is accepted or there is a significant effect of the Contextual Teaching and Learning model on the learning outcomes of class IV students at SDN Murtajih 1 in the Mathematic subject area of flat shapes. Based on the N-Gain test, the influence of the effect was 61.52%. The application of the Contextual Teaching And Learning model in mathematics learning about the area of flat shapes in class IV at SDN Murtajih 1 took place over 2 meetings with a duration of 70 minutes for each meeting and was carried out very well with an average implementation percentage of 100%.

Comparison of Basic Learning Outcomes of Electricity and Electronics Using Realistic Mathematics Learning Models And Discovery in Electricity Students of Elfatah Christian Vocational School Manado

Comparison of Basic Learning Outcomes of Electricity and Electronics Using Realistic Mathematics Learning Models And Discovery in Electricity Students of Elfatah Christian Vocational School Manado

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.

Developing A Differentiated Learning Model Based on Artificial Intelligence : Implementation in The Mathematics Classroom

This study aims to produce a model that can meet the needs of Differentiated Learning based on AI. This method used research and development (R&D) with the Generic Desain Research Model (GDRM). The subject of this study was mathematics education experts and mathematics learning model experts. Instruments used in this study include quality validity assessment and feasibility assessment analysis. The data analysis techniques used were quantitative methods with learning model validity assessment criteria that use A Linkert Scale of 1-4. The test results of the AI-based Mathematics learning model show that (1) the Content Validity test resulted in an average of 3.47 which means the model is very valid, and (2) The average feasibility test is 3.43, which means it is also very valid. The results showed that the AI-based Mathematics learning model can be implemented in differentiated mathematics classes and train students to learn independently, think critically, provide solutions to problems, and have the courage to convey solutions.

Development of a 21st Century Based Mathematics Learning Model: A Validity Study on the Investigation-Construction Stages

<p style="text-align:justify">The purpose of this study was to develop a valid and theoretically sound 21st century skills-based mathematics learning model. Plomp's research and development theory and Nieveen's criteria were used to ensure the quality and reliability of the model. The model development process includes Initial Investigation, Design, and Realization/Construction. Data were collected from six schools through various instruments such as observation sheets, documents, 21st century skills tests, and interview guides. Model validation involved the participation of five experts. A blended data analysis approach was used to ensure a comprehensive understanding of the findings. The result of this study is a 21st century skills-based mathematics learning model named Model 21-BML. The model consists of five interconnected core components: Learning Syntax, Social System, Reaction Principles, Support System, and Instructional and Accompanying Impacts. The uniqueness of this model lies in the seven integrated learning trajectories: Object translation, decomposition, cognitive construction, cognitive development, communication, reflection, and extension. After going through a rigorous validation process, the five experts confirmed the high validity of the 21-BML Model. This model not only enhances innovative mathematics learning theory, but also provides practical alternatives for mathematics teachers to encourage the development of 21st century skills in students. The model is expected to make a significant contribution in bridging the gap between traditional mathematics education and the competency demands of the modern era, as well.</p>

Meta-Analisis investigasi model kelas terbalik pada keterampilan berpikir tingkat tinggi (HOTS) siswa matematika: Analisis efek gabungan dan heterogenitas

This research aims to test the effectiveness of the flipped classroom model on students' higher order thinking abilities (HOTS) in mathematics subjects. The research method uses meta-analysis. The sample in this study was 18 primary studies that met the specified inclusion criteria. Data analysis was performed using comprehensive meta-analysis (CMA) software. The results of the analysis using the random-effects approach showed that the combined effect size was (g = 1.82; p < 0.01) (very large effect). This shows that the implementation of the flipped classroom model has a big influence on students' mathematics HOTS when compared to the traditional learning model. The results of the moderator variable analysis show that the effect of implementing the flipped class model on students' mathematics HOTS differs significantly based on the publication type variable (Qb = 7.94; p = 0.01). However, it was not significantly different based on the variables of education level (Qb = 7.45; p = 0.06), experimental group capacity (Qb = 0.29; p = 0.62), and year of publication (Qb = 0.21; p = 0.64). These findings can be the basis for choosing a mathematics learning model so that the quality of mathematics learning using the flipped classroom model can be improved.

Integration of the STEAM Approach and the Problem Based Learning Model in Mathematics Learning at the Era of Industrial Revolution 5.0

The Industrial Revolution 5.0 era demands more complex skills, such as critical thinking, creativity, collaboration, innovation and digital literacy. Learning approaches and models in mathematics learning need to be adapted to be more relevant to the needs of the times. The integration of the STEAM approach and the Problem Based Learning model is very suitable for learning mathematics in the current era of the Industrial Revolution 5.0. This research aims to analyze the integration of the STEAM approach and the Problem Based Learning model in mathematics learning in the Industrial Revolution 5.0 era. This research is qualitative research using the literature study method. Data sources are quoted from scientific articles, national and international journals, manuals and digital books and other similar things. Then the data is collected, then the data is analyzed and conclusions are drawn. The results of this research conclude that through the integration of STEAM and PBL models in mathematics learning in the Industrial Revolution 5.0 era, students not only develop a deeper understanding of mathematical concepts, but also gain skills that are relevant to the real world, such as problem solving, critical thinking, collaboration , and communication. It prepares students to become lifelong learners capable of facing complex challenges in an ever-changing society.

An Accurate Disease Classification of COVID-19 and Pneumonia from Chest X-Ray Images Utilizing Mathematical Algorithm-Based Deep Learning Model

An Accurate Disease Classification of COVID-19 and Pneumonia from Chest X-Ray Images Utilizing Mathematical Algorithm-Based Deep Learning Model

An Analytic Model for COVID-19 Cases in Iran and Its Neighbors Using Deep Learning and Time Series Methods

Since the pandemic of the coronavirus (COVID-19) in 2019, it has rapidly become a major global health concern. Various mutations and rapid spread of the virus, a lack of specific treatment, and limited hospital facilities highlight the importance of anticipation, risk analysis, and timely treatment. The use of mathematical models, artificial intelligence, and simulation methods are effective tools in predicting the spread and providing effective solutions to prevent virus transmission. Analysis and forecasting require an integrated model to cover different aspects of the problem and use different methods to obtain appropriate results. In this research, a proposed model for analysis and prediction of COVID-19 cases in Iran and neighboring countries is presented. The performance of mathematical and deep learning models in the proposed model has been evaluated using data from Johns Hopkins University from January 29, 2020, to April 30, 2021. Evaluation of the predictive outcomes of daily cases was performed using RMSE criteria. Then, the effect of the trend of cases in neighboring countries of Iran on the rate of new cases in this country has been studied. These models can help governments predict the number of infections to provide the necessary solutions and prevent a new wave of the virus.

A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals

The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient’s condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.