Learning Procedure Research Articles

The Posthuman Pedagogy: A Learning Centric Educational Dimension

The article is based on a thematic analysis of available literature on posthuman educational practices and opportunities. It asserts that the posthuman context does not demoralize human space, but rather strengthens educational practices by going beyond formal and informal education and surpassing teacher-centered or student-centered teaching-learning activities. The article discusses perceptions of the posthuman context in general, as well as common educational practices about the posthuman context. Its primary focus is on posthuman pedagogical considerations and a method to incorporate digital realities as a strength to posthuman pedagogue. It proposes a multisensory pedagogy that incorporates human and nonhuman realities to promote lifelong learning procedures. It also asserts the concept of a diffractive teacher who transforms into a barycentric mass collaborating and functioning in a relational approach.

Strengthening the Accessibility of Persons with Disabilities through Political Education in the 2024 General Election in Medan City

The low participation of people with disabilities in elections is an issue that needs attention. Several studies have shown that people are not yet sensitive to the political rights of persons with disabilities. In addition, the lack of election accessibility for persons with disabilities is also an obstacle. Persons with disabilities are one of the large minority groups in Medan City. Therefore, this service aims to provide an understanding of strengthening the accessibility of persons with disabilities through political education in general elections in Medan City. Participatory Learning and Action methods through learning procedures that combine lecture techniques and participatory discussions to group learning are carried out in this activity. Firstly, this service activity was conducted with the Indonesian Blind Association (Pertuni) of Medan City, which includes learning about political education. Secondly, increasing the understanding of persons with disabilities about the importance of exercising their voting rights. Third, providing an understanding of political accessibility and political rights for persons with disabilities. The service results recommend that activity participants increase their understanding of the rights and obligations of citizens to vote. For the knowledge implemented in political education to run well, continuous coaching and mentoring are needed for the Indonesian Blind Association (Pertuni) in Medan City.

Sentiment Analysis Using Machine Learning

In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms’ performance.

What explains collective action: The impact of social capital, incentive structures and economic benefits

AbstractThis study focuses in testing the power of reciprocity and leadership as collective action incentive structures and cooperation economic benefits in explaining collective action initiation in the context of a post‐communist transition economy. The paper is based on a structured survey targeting Albanian export‐oriented farmers. Different from most previous studies, this paper uses both regression analysis and machine learning procedure which is better suited for analysing non‐linear relationships. The empirical findings are at odds with common sense that non‐cooperation is the dominant strategy, because the presence of tolerant reciprocators and leadership resources provide promising incentive structures for collective action development.

Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism

The current study aims to establish a connection between students' behavioral concerns, namely stress and anxiety, related to the completion of academic tasks, and their integration of technology using the Technology Acceptance Model (TAM) through the utilization of Chat-GPT via ubiquitous learning (UL) procedure. To achieve this objective, data was collected from 156 students studying management science who were engaged in their final year research projects or internship reports from selected universities in Pakistan. The gathered data underwent analysis through Structural Equation Modeling (SEM) using Smart PLS software. The findings reveal a significant relationship: students' stress contributes to the emergence of anxiety, which in turn motivates the adoption of technology-assisted solutions, specifically Chat-GPT, to efficiently complete assigned tasks within deadlines working through any device from anywhere. Consequently, the perceived ease of use and usefulness associated with Chat-GPT's AI-generated text contribute to shaping students' favorable attitudes toward utilizing Chat-GPT and also play a role in reducing their stress levels. Furthermore, the study confirms that the development of a positive attitude in students acts as a driving force, compelling them to engage with Chat-GPT through ubiquitous learning (UL) procedure, ultimately resulting in increased actual usage of Chat-GPT. This pattern, in turn, contributes to stress and anxiety reduction among management science students. The study's outcomes corroborate the TAM model, which aligns with the social exchange process, demonstrating its applicability within the context of the educational setup in management sciences and its potential to enhance the learning experiences of researchers.

Penerapan Metode Team Games Tournament Guna Meningkatkan Minat Belajar Siswa Kelas 5 SD 067090 Jl. Halat Kota Medan

After the passing of the Covid-19 Pandemic, it gave new impacts and challenges in the world of teaching and learning, especially for grade 5 students at SD 067090 Halat who experienced a decrease in their interest in learning. The main factor for this to happen is distance learning which makes students spend more time with smartphones. The impact after the pandemic was over, students became lazy to study at school. This decrease in learning interest affects the final grades of students later. This cannot be allowed to continue because it will not only affect the final grades of students but will also affect the development of students' traits, attitudes and ethics. The problem that exists in grade 5 students at SD Negeri 067090 is the decline in student interest in learning. The monotonous class atmosphere with only material explanations and doing assignments is one of the reasons students feel bored studying in class. Team Games Tournament is a learning procedure that provides opportunities for groups to compete with other groups so that students are passionate about learning. The system applied in this method emphasizes games (games) as learning activities that aim to spur the motivation and spirit of competition in students both physically and mentally with predetermined rules so as to make students enthusiastic during the learning process.

Monte Carlo simulation with confusion matrix paradigm - A sample of internal consistency indices.

Monte Carlo simulation is a common method of providing empirical evidence to verify statistics used in psychological studies. A representative set of conditions should be included in simulation studies. However, several recently published Monte Carlo simulation studies have not included the conditions of the null distribution of the statistic in their evaluations or comparisons of statistics and, therefore, have drawn incorrect conclusions. This present study proposes a design based on a common statistic evaluation procedure in psychology and machine learning, using a confusion matrix with four cells: true positive, true negative, false negative modified, and false positive modified. To illustrate this design, we employ an influential Monte Carlo simulation study by Trizano-Hermosilla and Alvarado (2016), which concluded that the Omega-indexed internal consistency should be preferred over other alternatives. Our results show that Omega can report an acceptable level of internal consistency (i.e., > 0.7) in a population with no relationship between every two items in some conditions, providing novel empirical evidence for comparing internal consistency indices.

Application of Blockchain Technology for Innovation in the Education Sector

The utilization of blockchain technology holds immense potential to profoundly influence the field of education in the forthcoming years. This technology has the potential to enhance the security, transparency, and affordability of education. Blockchain technology can be employed to securely and permanently maintain student education records. Implementing this can enable students to monitor their academic advancement and receive acknowledgement for their accomplishments. Employing blockchain technology can issue educational certificates that possess a high level of security and are impervious to counterfeiting. This might facilitate students in securing employment or pursuing further education at an advanced level. The implementation of blockchain technology can enhance the security and dependability of online learning platforms. One way to achieve this is by using blockchain technology to store educational resources, evaluations, and communication between students and educators. Blockchain technology can facilitate the creation of adaptive learning systems that have the ability to customize learning materials and procedures according to the individual requirements of each learner. Blockchain technology has the potential to establish a learning environment that is decentralized and characterized by increased democratic participation. A collective or network comprising diverse stakeholders, including students, educators, and parents, typically oversees this educational setting.

The Teacher Professional Education Students' Perception on Learning Management System-based Online Learning

This research aimed to investigate the perceptions and impacts of online learning utilizing the SPACE (Electronic religious learning system application) among students enrolled in Teacher Professional Education (TPE) programs at State Islamic Religious Colleges. Employing a quantitative approach, the study utilized linear regression analysis and structural equation modeling through AMOS 23. Effectiveness was gauged using indicators proposed by Robert E. Slavin, encompassing quality of learning, appropriateness of learning level, intensity, and time. The study participants comprised students across three Teacher Professional Education Study Programs. The findings revealed that nearly all participants expressed ease in learning and perceived the modules within SPACE as more effective. They also indicated that the materials/modules in SPACE aligned well with the needs of TPE participants and found the learning procedures within SPACE easy to follow. Notably, incentive variables within implementation indicators had a direct influence on perceived ease of use (user convenience), subsequently impacting the perceived usefulness variable. For future research, integrating direct interview data from students and conducting comparative analyses between offline and online implementations in professional education would provide valuable insights.

Knockout of NMDARs in CA1 and dentate gyrus fails to impair temporal control of conditioned behavior in mice.

The hippocampus has been implicated in temporal learning. Plasticity within the hippocampus requires NMDA receptor-dependent glutamatergic neurotransmission. We tested the prediction that hippocampal NMDA receptors are required for learning about time by testing mice that lack postembryonal NMDARs in the CA1 and dentate gyrus (DG) hippocampal subfields on three different appetitive temporal learning procedures. The conditional knockout mice (Grin1ΔDCA1 ) showed normal sensitivity to cue duration, responding at a higher level to a short duration cue than compared to a long duration cue. Knockout mice also showed normal precision and accuracy of response timing in the peak procedure in which reinforcement occurred after 10 s delay within a 30 s cue presentation. Mice were tested on the matching of response rates to reinforcement rates on instrumental conditioning with two levers reinforced on a concurrent variable interval schedule. Pressing on one lever was reinforced at a higher rate than the other lever. Grin1ΔDGCA1 mice showed normal sensitivity to the relative reinforcement rates of the levers. In contrast to the lack of effect of hippocampal NMDAR deletion on measures of temporal sensitivity, Grin1ΔDGCA1 mice showed increased baseline measures of magazine activity and lever pressing. Furthermore, reversal learning was enhanced when the reward contingencies were switched in the lever pressing task, but this was true only for mice trained with a large difference between relative reinforcement rates between the levers. The results failed to demonstrate a role for NMDARs in excitatory CA1 and DG neurons in learning about temporal information.

Mathematical Formulation of Learning and Its Computational Complexity for Transformers’ Layers

Transformers are the cornerstone of natural language processing and other much more complicated sequential modelling tasks. The training of these models, however, requires an enormous number of computations, with substantial economic and environmental impacts. An accurate estimation of the computational complexity of training would allow us to be aware in advance about the associated latency and energy consumption. Furthermore, with the advent of forward learning workloads, an estimation of the computational complexity of such neural network topologies is required in order to reliably compare backpropagation with these advanced learning procedures. This work describes a mathematical approach, independent from the deployment on a specific target, for estimating the complexity of training a transformer model. Hence, the equations used during backpropagation and forward learning algorithms are derived for each layer and their complexity is expressed in the form of MACCs and FLOPs. By adding all of these together accordingly to their embodiment into a complete topology and the learning rule taken into account, the total complexity of the desired transformer workload can be estimated.

Implementation and Evaluation of a Trainee Orientation Manual in an Intensive Care Unit Rotation.

The intensive care unit (ICU) rotation places trainees in a fast-paced, high-intensity environment that requires complex patient care and multidisciplinary coordination. Trainees seek continuous medical knowledge acquisition while tasked with learning ICU-specific workflows, procedures, and policies. The unfamiliarity with rotation logistics and administrative roles and expectations could hinder the ICU rotation learning experience. A lack of standardization and material for trainee orientation to administrative ICU tasks and workflows could affect the trainee's rotation performance and overall learner satisfaction. We evaluated the implementation of an ICU trainee manual to provide trainees with a source of orientation for rotation logistics and nonclinical ICU tasks. We assessd its impact on content retention, learners' satisfaction with the manual, and overall ICU rotation experience. We designed an observational, prospective cohort study that included all trainees scheduled to rotate in the ICU during the 2020-2021 academic year. The ICU manual was delivered electronically and was available throughout the academic year. Trainees received a survey before their first ICU rotation (pretest) and 6 months after their first ICU rotation (retest) to assess content retention, trainees' perception of the ICU manual, and overall rotation satisfaction. A total of 95 trainees completed the pretest survey, and 61 completed the retest survey. The target cohort response rate for each survey was 100%. Pretest scores were higher than the matched retest scores (41 of 48 [interquartile range, 37-44] vs. 38 of 48 [34-41]; P < 0.001). The median ICU manual satisfaction score was 32 of 40 (26-36.5). We found positive correlations between ICU manual trainee satisfaction score and the retest score (r[59] = 0.320; P = 0.01) and ICU rotation trainee satisfaction level (r[59] = 0.909; P < 0.001). Implementing an ICU manual to orient trainees to their ICU clinical rotation was well received and showed continued retention of orientation content. Higher ICU rotation trainee satisfaction levels were related to a positive perception of the ICU manual.

Implementation of the Assure Model Using Google Site Media in Learning Procedure Texts

Education in Indonesia is of low quality. Learning media is one component that can be used as an effort to improve the quality of education in Indonesia. Teachers as learning facilitators have a role in determining appropriate learning media for student characteristics so that learning outcomes and student interest in learning increase. The lack of varied learning makes students more lazy in studying, especially studying Indonesian. Based on the results of observations at SMP Negeri 21 Semarang, the problem was found that the boredom experienced by students in learning was due to the learning media presented by the teacher needing to be more varied and innovative. Based on the problems found, the research intended to determine the implementation of the ASSURE learning model using Google Site media in learning procedure texts. The research was conducted in class VII A of SMP Negeri 21 Semarang. The number of research subjects was 33 students, 15 men and 18 women. The research was carried out in November in semester 1 of the 2022/2023 academic year. Data collection was carried out by observation or observation and interviews. Data analysis was carried out using three steps: data reduction, data presentation, and drawing conclusions. The research results show that the implementation of the ASSURE model using Google Site media in learning procedural texts went well. Through the use of learning media that is different from usual, students are interested in learning so that learning activities do not feel boring and the learning outcomes are also in accordance with the learning objectives.

Precision Corn Pest Detection: Two-Step Transfer Learning for Beetles (Coleoptera) with MobileNet-SSD

Using neural networks on low-power mobile systems can aid in controlling pests while preserving beneficial species for crops. However, low-power devices require simplified neural networks, which may lead to reduced performance. This study was focused on developing an optimized deep-learning model for mobile devices for detecting corn pests. We propose a two-step transfer learning approach to enhance the accuracy of two versions of the MobileNet SSD network. Five beetle species (Coleoptera), including four harmful to corn crops (belonging to genera Anoxia, Diabrotica, Opatrum and Zabrus), and one beneficial (Coccinella sp.), were selected for preliminary testing. We employed two datasets. One for the first transfer learning procedure comprises 2605 images with general dataset classes ‘Beetle’ and ‘Ladybug’. It was used to recalibrate the networks’ trainable parameters for these two broader classes. Furthermore, the models were retrained on a second dataset of 2648 images of the five selected species. Performance was compared with a baseline model in terms of average accuracy per class and mean average precision (mAP). MobileNet-SSD-v2-Lite achieved an mAP of 0.8923, ranking second but close to the highest mAP (0.908) obtained by MobileNet-SSD-v1 and outperforming the baseline mAP by 6.06%. It demonstrated the highest accuracy for Opatrum (0.9514) and Diabrotica (0.8066). Anoxia it reached a third-place accuracy (0.9851), close to the top value of 0.9912. Zabrus achieved the second position (0.9053), while Coccinella was reliably distinguished from all other species, with an accuracy of 0.8939 and zero false positives; moreover, no pest species were mistakenly identified as Coccinella. Analyzing the errors in the MobileNet-SSD-v2-Lite model revealed good overall accuracy despite the reduced size of the training set, with one misclassification, 33 non-identifications, 7 double identifications and 1 false positive across the 266 images from the test set, yielding an overall relative error rate of 0.1579. The preliminary findings validated the two-step transfer learning procedure and placed the MobileNet-SSD-v2-Lite in the first place, showing high potential for using neural networks on real-time pest control while protecting beneficial species.

Memeplex-based memetic algorithm for the multi-objective optimal design of composite structures

Hybrid construction is proposed to decrease costs in lightweight structures using fiber-reinforced plastics (FRP) composite materials. Minimum weight, minimum strain energy (stiffness), and minimum energy variability of the structural system response are the objectives of the robust design optimization approach applied to composite shell structures with stiffeners. The trade-off depends on given stress, displacement, and buckling constraints imposed on composite structures considering non-linear geometric behavior. The design variables are ply angles and ply thicknesses of shell laminates, the cross-section dimensions of stiffeners, and the variables related to the selection of materials and their distribution at the laminate level and subsequently of the laminates along the structure. A Multi-Objective Memetic Algorithm (MOMA) applies multiple learning procedures exploring the synergy of different cultural transmission rules. An enlarged virtual population with a dual age-dominance nature captures and updates the Pareto curve. The concept of memeplex controls the meme selection and their propagation along the hybrid genetic and cultural evolution path. The success of memetic learning procedures is analyzed by measuring each meme's relative and absolute success events according to different approaches, depending on the reference time used in evolutionary history. Results show that MOMA is promising in multi-objective optimization of composite hybrid structures.

Prediction of Crop Yield by Support Vector Machine Coupled with Deep Learning Algorithm Procedures in Lower Kulfo Watershed of Ethiopia

Sensible and judicious utilization of water for agriculture in conjunction with prediction techniques increases the crop yield. The Ethiopian economy relies on and is exclusively dependent on agricultural-based activities. Different soil compositions (nitrogen, phosphorous, and potassium), crop alternation, soil dampness, and climate conditions play an imperative contribution in cultivation. The primary purpose of this study was to conduct a machine learning approach which can be practiced dynamically for efficient farming at a low cost. The support vector machine (SVM) was applied as a machine learning procedure, whereas long short-term memory (LSTM) and the recurrent neural network (RNN) were considered as deep learning procedures. The research comprised a model that is combined with machine learning procedures (ANN, random forest, and decision tree) to know efficient and appropriate crop types. The planned model is improved through conducting deep learning methods incorporated to the existing practice for different crop condition. Pure data and related evidence are attained concerning the quantities of soil constituents desired through their expenditures distinctly. It delivers well precision as compared to the current model examining the specified documents and assisting the local agronomists in forecasting different types of crop and gain benefits. In RNN, LSTM, and SVM algorithms, the accuracy is determined as 96% which is comparatively preferable as compared to other machine learning procedures under different feature and crop types. The techniques are evaluated in terms of percentage in prediction accuracy. The results generated are important for agrarians, experts, researchers, and local farmers to maximize the crop productivity and help to enhance agriculture and climate change-related decisions, especially in low-to-middle-income countries.

Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (WIP)

Sandflies (Diptera; Psychodidae) are medical and veterinary vectors that transmit diverse parasitic, viral, and bacterial pathogens. Their identification has always been challenging, particularly at the specific and sub-specific levels, because it relies on examining minute and mostly internal structures. Here, to circumvent such limitations, we have evaluated the accuracy and reliability of Wing Interferential Patterns (WIPs) generated on the surface of sandfly wings in conjunction with deep learning (DL) procedures to assign specimens at various taxonomic levels. Our dataset proves that the method can accurately identify sandflies over other dipteran insects at the family, genus, subgenus, and species level with an accuracy higher than 77.0%, regardless of the taxonomic level challenged. This approach does not require inspection of internal organs to address identification, does not rely on identification keys, and can be implemented under field or near-field conditions, showing promise for sandfly pro-active and passive entomological surveys in an era of scarcity in medical entomologists.

A Fused Deep Fuzzy Neural Network Controller and Its Application to Pneumatic Flexible Joint

With multilayered deep neural networks (DNNs) distinguished from the traditional single-layer neural networks (SNNs) by the depth, deep learning is a powerful paradigm possessing the capability of feature learning from big data. However, as a completely deterministic model, deep learning does not perform well in dealing with uncertain information and must be pretrained before being applied, which hinders the broad application of deep learning in real-time control. In this article, we introduce the knowledge-based fuzzy logic system (FLS) into deep learning and propose a fused deep fuzzy neural network (FDFNN) controller. The FDFNN controller derives the control signal from both deep fuzzy neural networks and the knowledge-based FLS. At the beginning of control, the FDFNN controller performs like a traditional fuzzy logic controller (FLC), which eliminates the pretraining procedure of deep learning. With another FLS providing supervised signal, the network weights of DFNN's hidden layers can be updated during the control process, which endows the FDFNN controller with online learning capability. A Lyapunov-based convergence analysis is conducted to guarantee the stability of the learning procedure. Both numerical simulations and real-world experiments validate that the proposed method effectively improves the real-time tracking control performance of a pneumatic flexible joint system.

Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network

Concept drift is the phenomenon where the data distribution in a data stream changes over time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause a jam in a certain period, leading to a distribution change in traffic speed. Most research in the concept drift field focuses on single data stream, however, few of them consider multi-stream environments which are more in line with the application needs. To fill this gap, we propose a multi-stream prediction setting and a multi-stream concept drift self-adaptation framework using graph neural network, named SAGN. In SAGN, we reconsider the learning procedure of GNN-based predictors from an aspect of concept drift adaptation for multi-stream. By this design, the prediction task is converted into online streaming data tasks in sub-graphs. Each sub-graph corresponds to an adaptation target and will be updated over time. In this way, locally we can overcome drift in each sub-graph by a designed adaptation technique, and globally the correlation between different data streams is well-preserved as a graph structure. Therefore, whether drift occurs or not, in one or several streams, SAGNcan provide consistently accurate prediction results. We comprehensively tested SAGNon both synthetic and real-world, drift and non-drift data in the multi-step prediction task. The experiment results show that SAGNis able to achieve state-of-the-art performance in most cases.

A STUDY ON THE EXISTENTIALISM AND ITS RELEVANCE TO THE READING LEARNING FOR ELEMENTARY SCHOOL STUDENTS

This article discusses the description of a study on existentialism in which its relevance to the reading learning is seen based on the teaching and learning procedures applied by the teacher in order to make a more qualified teaching and learning process for students who have rights and obligations. The research method used is a qualitative approach with descriptive method that focused on a philosophical study (Rahman, 2020) with a library research method or literature review by analyzing one book entitled Model-Model Pembelajaran Bahasa Indonesia di SD/MI written by Siti Anisatun Nafiah (2018) and published by Ar-Ruzz Media in Yogyakarta. Humans did not only live throughout their lives in themselves, and they were in the human realm, but they also went out of themselves to pursue a transcendent goal so that humans could make up their existence for themselves. In this relation, every learning subject in elementary school should prioritize the essence for students as Gods Creature who have different abilities in the reading learning. Existentialism view on the reading learning that imparted a number of opinions emphasized that it was no need the more freedom rules in the reading learning. On this occasion, the students and the teachers are the subjects who recognized the time. Past, future and present times are similar for them. Students as individuals have chosen their lives based on their volition. It means that students can find out the real meaning of life if they can relate to their selves to do unlimited actions and be ready to contemplate a life even though they realized they have limited to do it.