Conventional Learning Methods Research Articles

Understanding Climate Change Material through Immersive Virtual Learning (IVL)

Immersive Virtual Learning is learning that is considered meaningful for students to study climate change material which is considered complex because of the many interactions between the systems involved in it, and is considered to be material that is quite challenging for students. It is known that there are still many students who have limited knowledge about climate change so that learning methods are needed that can help them understand the climate change material well. This study aims to see how students' understanding after learning using the Immersive Virtual Learning (IVL) learning method, compared to the understanding of students using conventional learning methods. Using the Pre-Post Experiment Control Group Design design and involving 59 students who were divided into treatment and control classes, produced research data showing that students' understanding in the two classes experienced significant differences in improvement, but the increase in question was very small, even though the class treatment experienced a better increase than the control class. Based on this, it can be concluded that students' understanding has increased slightly after learning to use the Immersive Virtual Learning method. Further research is needed to develop the IVL method in order to provide better learning outcomes.

Pengimplementasian Pendidikan Karakter Mengenai Kepedulian Lingkungan Pada Sekolah Sungai Di Gunung Anyar

Character education is important for every individual, especially in terms of environmental awareness. Good character can be formed through education at school. Caring about the environment is important because it will have many positive impacts on every individual. To make this happen, it can be done by instilling various character values in students from an early age. This study aims to strengthen understanding of the importance of protecting the surrounding environment, especially for students at the River School, Gunung Anyar, Surabaya. This study uses conventional learning methods, question and answer methods and technology-based learning methods. In fact, this school has slowly shaped the students' character regarding caring for the environment. It is known that students are able to understand how to protect the environment.

Analysis of Problem Solving Ability in Natural Sciences and Life Skills through Guided Discovery Learning in the View of Student Learning Independence in the Subject Matter of Changes in Energy Forms

The purpose of this study was to examine the effect of guided discovery learning on science problem solving ability and life skills in terms of student learning independence in the form of energy in grade IV elementary school. Method: The research design used quasi experiment with factorial design. The research sample was 50 fourth grade students from 2 elementary schools. Participants were tested using a problem solving ability test with essay form (pretest and posttest) and student life skills data using observation sheets. Data were analyzed by one way manova test followed by BNT test. Results: The guided discovery learning method (M = 67.8; SD = 14.0) had significantly better problem solving ability than the conventional learning method (M = 57.8; SD = 13.6), [F (5; 44) = 10.53, p = 0.000, ηp2 = 0.55]. The guided discovery learning method (M = 74.9; SD = 10.9) had significantly better life skills than the conventional learning method (M = 65.3; SD = 9.2); [F (5; 44) = 40,49, p = 0.000, ηp2 = 0.821].

Pengembangan Media Pembelajaran Matematika Berbasis IoT dengan Pendekatan Problem Based Learning

This research aims to develop an Internet of Things (IoT)-based mathematics learning medium using a Problem-Based Learning (PBL) approach and evaluate its effectiveness in enhancing students' problem-solving skills. The research method employed is Research and Development (R&D), consisting of stages such as needs analysis, media design, prototype development, field trial, and evaluation. The research subjects consist of 11th-grade students at MAN 1 Probolinggo. The IoT-based mathematics learning medium is designed to facilitate dynamic interaction between students and real-world objects through IoT connectivity. This medium allows students to experience direct learning and actively participate in solving mathematical problems in real-life contexts. The results of this research anticipate that the use of IoT-based learning media with a PBL approach significantly improves problem-solving skills compared to conventional learning methods. Furthermore, it is expected that positive responses from students and teachers to this medium can be integrated into existing mathematics curricula. Therefore, the development of IoT-based mathematics learning media with a PBL approach is expected to be an effective alternative in enhancing students' mathematical problem-solving skills. This medium enables students to actively engage in learning, fosters creativity, and connects mathematical concepts with the real world. Additionally, the results of this research are expected to make a positive contribution to the development of innovative and technology-relevant mathematics teaching methods.

HTML5 package media: How does it affect elementary school students’ concept understanding and self-efficacy?

Background: HTML5 Package media, equipped with features and displays, provides a clearer visual display in learning geometry compared to traditional lecture-based teaching. This approach facilitates students in observing and understanding geometry concepts, positively impacting their self-efficacy.Aim: The study aims to assess the impact of HTML5 packages on students' understanding of geometry concepts and their self-efficacy.Method: The research, a quasi-experimental study, used simple random sampling to select 63 students. Data were collected through tests and questionnaires, and the analysis was conducted using MANOVA.Result: The findings reveal a significant effect of the HTML5 Package on both concept understanding and self-efficacy. The experimental group scored higher than the conventional group, with significant values in Pilae Trace, Wilk Lambda, Hotelling Trace, and Roy's Largest Root analyses.Conclusion: The HTML5 Package media effectively enhances the understanding of geometry concepts and self-efficacy among students. While the effect size is modest, it is notably better than conventional learning methods. The study suggests broader application and research involving more students and diverse mathematical topics could yield more influential results.

THE EFFECT OF PROJECT BASED LEARNING MODEL WITH STEM APPROACH TO STUDENTS’ CRITICAL THINGKING SKILL ON HUMAN EXCRETION SYSTEM

This study was aimed to determine the effect of Project Based Learning Model with STEM approach to students' critical thinking skills on Human Excretion System material in class 8th grade at SMP Negeri 37 Medan. This research used quantitative methods with the type of research quasi experiment with pretest-posttest control group design. The sample of this study consisted of two classes randomly selected by simple random sampling technique, namely in class VIII-D and VIII-F. The first sample is an experimental class that was taught using the PjBL-STEM learning model, and the second sample is a control class that was taught using conventional learning methods. The research instrument that was used included critical thinking skills test instrument using essay questions totaling 5 questions. Analysis of research data used the Independent Sample T-Test test using SPSS version 26.0. The pretest data results of students' critical thinking skills in class VIII-D and class VIII-F are 33.71 and 29.35. After obtained the results of the initial ability of students by looking at the pretest results in both classes, class VIII-D was selected as the experimental class and class VII-F as the control class. The posttest data in the experimental class and control class are 80.00 and 52.58. Based on the hypothesis test, the calculated t value is 12.761 > t table 1.671, and the significance value is 0.000 < 𝛼, with 𝛼 = 0.05, so H0 is rejected and Ha was accepted, which meant that there was an effect of the PJBL-STEM model on the critical thinking skills of students on the material of the excretory system. The influence of the PJBL-STEM model on students' critical thinking skills was categorized as strong with a cohen's d value of 3.242. It can be concluded that the PJBL-STEM model have an effect on students' critical thinking skills with a strong effect category.

Effective deep learning approach for segmentation of pulmonary cancer in thoracic CT image

One of the most severe kinds of tumors in people is lung cancer. Identifying lung cancer and its types requires costly and time-consuming procedure research. Furthermore, lung nodules are difficult to identify because of their diversity and visual similarity to neighboring locations. Conventional machine learning methods either treat these components separately or rely on human integration, which can be time-consuming and could fail to fully capture the intricate relationships between these features. Deep learning methods' layered structures enable them to automatically incorporate many features and learn meaningful descriptions. Thus, this framework proposes an efficient deep learning technique for classifying pulmonary nodules using Computerized Tomography (CT) images. Initially, several pre-processing methods are taken into account to prepare the data. Then, T-Net based deep learning algorithm segments the lung nodule, and the CenterNet-based method extracts the texture and intensity attributes from the segmented image. Following that, the proposed NASNet-based classifier categorizes the nodules as cancerous or not, using the attributes that have been collected. Finally, the presented method will be assessed by the metrics like Dice similarity coefficient (DSC), Sensitivity, Positive predictive Value (PPV), f1-score, precision, recall, and accuracy on the LUNA-16 and Lung Image Database Consortium (LIDC-IDRI) datasets, and the outcomes are contrasted with other existing approaches.

Transfer learning model to predict flow boiling heat transfer coefficient in mini channels with micro pin fins

Flow boiling in mini channels with micro pin fins is a promising heat sink technique to achieve high-efficient aircraft thermal management. The accurate prediction of its heat transfer coefficient is critical for the practical design of two-phase heat exchanger based on mini channels with micro pin fins. Previous investigation shows that heat transfer coefficient prediction accuracy of the machine learning method is generally better than that of conventional empirical correlations. However, the machine learning method cannot guarantee its prediction accuracy among different data domains. To extend the application region of the conventional machine learning method, a transfer learning framework was proposed in present study. First of all, an experimental system was built to acquire test data from different sample domains (i.e., the diamond pin fins with different geometries). Then a conventional machine learning model was developed based on the deep learning method. Furthermore, the developed deep learning model was adjusted with transfer learning process, and the performance of these two kinds of models (i.e., conventional machine learning model and transfer learning model) was comprehensively evaluated. Results showed that the conventional machine learning model had a good prediction accuracy with an overall deviation of 4.11 % but was only confined in the same data domain as training data. Differently, the transfer learning model with 70 % data set from the new domain can achieve appropriate prediction in the new domains with deviation of 4.28 %. The results of this paper demonstrated that the conventional machine learning model can extend into different domains with reasonable prediction accuracy through transfer learning frameworks. It will benefit the practical design of high-efficiency heat exchangers for aircraft thermal management.

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed. Main Results. The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals ‘at risk’ and ‘not at risk,’ respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model. Significance. This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

Upaya Meningkatkan Hasil Belajar Siswa Melalui Penerapan Metode Integrated Curriculum Pada Siswa Kelas VII MTS Jam’iyah Mahmudiyah Tanjung Pura

From the explanation and research process carried out by the researcher, it is known that the Integrated Curriculum Method for class VII students at MTs Jam'iyah Mahmudiyah Tanjung Pura is very well implemented because it is able to improve learning outcomes. students who can be seen in the implementation of the second cycle. Students' understanding of learning is better with the implementation of the Integrated Curriculum method compared to the usual or conventional learning methods that have been applied so far. It can be concluded that the Integrated Curriculum method is effective. Efforts to improve student learning outcomes in learning moral aqidah in class VII MTs Jam'iyah Mahmudiyah Tanjung Pura were successfully carried out by implementing the Integrated Curriculum method in accordance with the plans that had been made. Efforts are made to actively involve students in every learning activity and explore students' potential so that students' creativity emerges. The increase in student learning outcomes by applying the Integrated Curriculum method is known through the student activity sheets that have been created and show good improvement through the application of the Integrated Curriculum method. Increasing student creativity in learning also influences student learning outcomes which also increase. In learning, students appear more active and enthusiastic in following the lesson material provided by the teacher in the learning process in class.

A Deep Learning Neural Network Method Using Linear Eigenvalue Statistics for Schizophrenic EEG Data Classification

Electroencephalography (EEG) signals can be used as a neuroimaging indicator to analyze brain-related diseases and mental states, such as schizophrenia, which is a common and serious mental disorder. However, the main limiting factor of using EEG data to support clinical schizophrenia diagnosis lies in the inadequacy of both objective characteristics and effective data analysis techniques. Random matrix theory (RMT) and its linear eigenvalue statistics (LES) can provide an effective mathematical modeling method for exploring the statistical properties of non-stationary nonlinear systems, such as EEG signals. To obtain an accurate classification and diagnosis of schizophrenia, this paper proposes a LES-based deep learning network scheme in which a series of random matrixes, consisting of EEG data sliding window sampling and their eigenvalues, are employed as features for deep learning. Due to the fact that the performance of the LES-based scheme is sensitive to the LES’s test function, the proposed LES-based deep learning network is embedded with two ways of combining LES’s test functions with learning techniques: the first is to have the LES’s test function assigned, while, using the second way, the optimal LES’s test function should be solved in a functional optimization problem. In this paper, various test functions and different optimal learning methods were coupled in experiments. Our results revealed a binary classification accuracy of nearly 90% in distinguishing between healthy controls (HC) and patients experiencing the first episode of schizophrenia (FES). Additionally, we achieved a ternary classification accuracy of approximately 70% by including clinical high risk for psychosis (CHR). The LES-embedded approach yielded notably higher classification accuracy compared to conventional machine learning methods and standard convolutional neural networks. As the performance of schizophrenia classification is strongly influenced by test functions, a functional optimization problem was proposed to identify an optimized test function, and an approximated parameter optimization problem was introduced to limit the search area of suitable basis functions. Furthermore, the parameterization test function optimization problem and the deep learning network were coupled to be synchronously optimized during the training process. The proposal approach achieved higher classification accuracy rates of 96.87% between HC and FES, with an additional 89.06% accuracy when CHR was included. The experimental studies demonstrated that the proposed LES-based method was significantly effective for schizophrenic EEG data classification.

Effect of Guided Discovery Methods on Students' Motivation and Learning Outcomes

This research aimed to determine the effect of guided discovery methods on students' motivation and learning outcomes by compare the result on guided discovery method and conventional learning method at MAN 2 Model Makassar. The research method was true experiment with randomized posttest only control group design. The sample of the research were determined randomly by using group random sampling technique and obtained two classes. The first class was the experiment class which was taught by guided discovery method and the second class was control class which was taught by conventional method. Each class consisted of 42 students. The result obtaint that physics learning motivation and physics learning outcome of the students who were taught by guided discovery learning method is higher than conventional learning method.

Demand forecasting and predictability identification of ride-sourcing via bidirectional spatial-temporal transformer neural processes

Understanding the spatial–temporal stochasticity in shared mobility is crucial for ride-sourcing demand forecasting, supply–demand management, and vehicle dispatch optimization. In contrast to conventional deep learning methods that typically provide point predictions or deterministic predictions, this paper introduces the bidirectional spatial–temporal Transformer neural processes (Bi-STTNP) prediction model, which stands out from conventional deep learning methods by providing probabilistic predictions and uncertainty estimations for ride-sourcing demand. Bi-STTNP captures the multivariate spatial–temporal Gaussian distribution of demand, offering not only demand expectations but also comprehensive uncertainty representations. We propose a predictability identification process based on predictive distributions to assess varying predictability across time slots and regions, improving interpretability. Our model, consisting of the bidirectional supply–demand attention module and spatial–temporal Transformer module, maintains interpretability while ensuring accurate demand expectation predictions. Extensive experiments on a real-world dataset of 15 million ride-sourcing orders in Hangzhou, China, demonstrate that Bi-STTNP outperforms baseline models in predicting demand expectation and quantifying demand uncertainty. Furthermore, we compute loose spatial–temporal predictability lower bounds and categorize regions by predictability, providing insights for optimizing passenger pricing strategies, driver incentives, and vehicle dispatching in ride-sourcing platforms.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

ObjectivesDramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome.MethodsIn total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months.ResultsBrain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age.ConclusionsBrain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome.Clinical relevance statementUnderstanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population.Key Points•Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes.•Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes.•The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine

The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.

Revolutionizing material design for protonic ceramic fuel cells: Bridging the limitations of conventional experimental screening and machine learning methods

The commercial viability of protonic ceramic fuel cells (PCFCs) is contingent upon developing highly active and stable cathode materials. The conventional trial-and-error process is time-consuming and costly for cathode material development, while the availability of sufficient and reliable datasets limits the recently emerging machine learning (ML) method. Here, we propose a novel approach based on the experimental design paradigm (EDP) to efficiently facilitate PCFC cathode materials’ development with a minimal dataset. As a rigorous systematic statistical approach, we employ the EDP for strategic variation of multiple elements and measure their effect on desired performance characteristics. We generate empirical models that reveal the optimal concentrations and interactions of the elemental composition and performance characteristics. In this study, we select the BaCoαCeβFeγYζO3-δ series as a proof-of-concept, and the optimal composition, BaCo0.667Ce0.167Fe0.083Y0.083O3-δ, was promptly determined—guided by the EDP—using only 16 independent conditions and 32 randomized experimental runs. We further demonstrate the EDP’s versatility by optimizing the widely-used and high-performing Ba0.5Sr0.5Co0.8Fe0.2O3-δ cathode material for solid oxide fuel cells. Our results highlight the potential of the EDP for effectively designing superior materials for solid-state electrochemical power generation systems, offering a reliable and practical alternative to conventional trial-and-error screening and ML methods.

Destructive computing with winner-lose-all competition in multi-layered neural networks

The present paper aims to propose a new learning method based on destructive computing, contrary to the conventional progressive computing or the steady-step learning. In spite of the existence of a large amount of biased or distorted information in inputs, the conventional learning methods fundamentally aim to gradually acquire information that is as faithful as possible to inputs, which has prevented us from acquiring intrinsic information hidden in the deepest level of inputs. At this time, it is permitted to suppose a leap to that level by changing information at hand not gradually but drastically. In particular, for the really drastic change of information, we introduce the winner-lose-all (WLA) to drastically destroy the supposedly most important information for immediately reaching or leaping to intrinsic information, hidden in complicated inputs. The method was applied to a target-marketing problem. The experimental results show that, with the new method, multi-layered neural networks had an ability to disentangle complicated network configurations into the simplest ones with simple and independent correlation coefficients between inputs and targets. This was realized by drastically changing the information content in the course of learning and, correspondingly, by mixing regular and irregular properties over connection weights.

Multi-scale time series analysis using TT-ConvLSTM technique for bearing remaining useful life prediction

This study advocates the utilization of a parallel neural network (PNN) architecture for the estimation of remaining useful life (RUL) of rolling element bearings. Conventional machine learning methods have often fallen short in processing machinery sensor data efficiently and accurately, given its vast nature and the temporal dynamics of component health conditions. To address this limitation, a robust PNN architecture is proposed which incorporates multiple parallel processing pathways. These pathways are equipped with diverse input neurons, capturing data from mechanical component condition sensors. The output neuron then efficiently predicts the RUL, capturing the degradation stages throughout the component's lifespan. The PNN structure provides better accuracy and computation time by efficiently handling huge amounts of data simultaneously and integrating both spatial and temporal information present. Additionally, time-transformer and recurrent neural networks (RNNs) are used to handle complex time series data. Improvement methodologies, such as positional encoding combined with a multi-headed self-attention mechanism and the convolutional long short-term memory (ConvLSTM) neural network, are employed. These methodologies adeptly handle spatiotemporal dependencies inherent in multidimensional features extracted across the time domain, frequency domain, and time–frequency domain, thereby considerably boosting the model's efficacy. Two case studies are conducted on XJTU-SY rolling element bearing dataset and FEMTO bearing dataset to validate and display the generalization capabilities of the proposed methodology, where PNN performed exceptionally in terms of accuracy and efficiency. It is concluded that with a novel implementation of variational stride temporal window strategy and utilizing the full capabilities of a PNN, the RUL of bearings can be predicted very accurately. The algorithm strategy is very robust and can be applied to other machine components as well. Subsequently, the code for the algorithm will be open-sourced.

Third Year MBBS - Gateway to Clinical Skills Learning.

clinical skills; communication; empathy.

MSNNet: Multi-scale-abled networks with multi-groups channel attention for vibrational spectrum matching

In recent years, deep learning methods have exhibited superior performance in many applications in vibrational spectroscopy especially compared with conventional machine learning methods such as Support Vector Machine (SVM) and Partial Least Squares (PLS). On the other hand, almost all of these methods employ such as LeNet and ResNet as their backbones which are not designed for processing spectra originally. In this paper, we demonstrate that these widely-used neural network models are insufficient when dealing with vibrational spectra. Particularly they lack the ability to process multi-scale information hidden in vibrational spectra. To address this, we further propose a novel multi-scale-abled network named MSNNet which combines a modified Res2Net with the Multi-Groups Channel Attention (MGCA). Experiments on two publicly available spectrum datasets show that the proposed MSNNet significantly outperforms existing methods. Moreover, using the Lorentz model we also generat a number of synthetic datasets where the scale in spectra can be deliberately controlled. Results on these synthetic datasets not only validate the superiority of the proposed method, but also support our hypothesis that the scale information can play a vital role in recognition of vibrational spectra.