Effective Learning Research Articles

Bridging STEM and linguistic gaps: A review of multilingual teaching approaches in science education

This paper examines how multilingual education strategies can be applied within STEM classrooms to bridge linguistic gaps and enhance learning outcomes. It reviews various teaching approaches incorporating multiple languages in conveying scientific concepts, aiming to improve accessibility, and understanding for students from diverse linguistic backgrounds. The analysis includes case studies of schools successfully implementing multilingual STEM programs, highlighting the benefits and challenges of such educational practices. Multilingualism presents challenges and opportunities in science education, particularly in bridging the gap between STEM (Science, Technology, Engineering, and Mathematics) subjects and linguistic diversity. This review provides an overview of the key themes and findings from a review of multilingual teaching approaches in science education. The review highlights multilingual students' challenges in STEM education, including language barriers that may impede their understanding of complex scientific concepts. It also examines the importance of addressing these challenges through innovative teaching approaches that leverage students' linguistic diversity as a strength rather than a limitation. Key findings from the review include the effectiveness of bilingual education models, such as content and language-integrated learning (CLIL), in enhancing students' scientific literacy and language proficiency. These models allow students to develop their STEM knowledge and language skills in tandem, leading to improved academic outcomes. The review also discusses the role of culturally responsive teaching in engaging multilingual students in STEM subjects. By incorporating students' cultural and linguistic backgrounds into the curriculum, educators can create more inclusive and effective learning environments that cater to the diverse needs of learners. Overall, the review highlights the importance of adopting a multilingual approach to teaching STEM subjects, which recognizes and values students' linguistic diversity. By embracing multilingualism in science education, educators can help bridge the gap between STEM and linguistic gaps, ultimately promoting more significant equity and inclusivity in STEM education.

Peer evaluation as an alternative assessment method for case presentations: A cross-sectional study of dental students' perception.

Peer assessment in dental and medical education is a key tool for evaluating attitudes and communication skills, serving as an effective formative learning method. It not only boosts academic performance and encourages lifelong learning and reflective practice but also fosters collaboration, effective communication, and constructive feedback among peers. This study aimed to assess dental students' satisfaction and perceptions regarding implementing peer assessment in case presentations, including contribution to their learning. This cross-sectional study involved third (n=26), fourth (n=54), and sixth (n=71) dental students divided into five groups. Each student received peer assessments (n=8) from the students of the other groups regarding their clinical presentation. At the end of the process, students were invited to complete an anonymous 9-item questionnaire to assess student perceptions of the peer-assessment process. Data were analyzed to evaluate students' perception and satisfaction with the peer assessment task. The bivariate analysis explored associations among questionnaire items. A total of 151 students participated in the current study. Results showed a high level of agreement for most of the students regarding their preparedness for peer and self-assessment. More than 75% of the students at all academic levels found it challenging to evaluate their peers. Student satisfaction scores increased with the level of education, (4.076, 4.214, and 4.246 out of five, respectively) with moderate correlations between peer evaluation characteristics. Peer assessment appears to enhance learning and professional development in dental students. Students' satisfaction with peer assessment grows with education level, reflecting its role in fostering critical evaluation skills and feedback.

Generalized nonlinear hybrid-norm parallel sparse filtering for bearing fault diagnosis under complex interference

Abstract Considerable attention has been garnered by the bearing fault diagnostic method, founded on sparse feature learning, for its exceptional robustness and adaptability to noise. Being an effective sparse learning method, parallel sparse filtering (PSF) has found successful applications in intelligent fault diagnosis. However, influenced by mechanical structural collisions and complex transmission paths, the obtained bearing signals come with abnormal impacts and noise, resulting in a reduced reliability of the PSF. Additionally, PSF constrains features and samples with l_(1⁄2) norm, which is significantly disturbed by noise. To address these issues, generalized nonlinear hybrid-norm parallel sparse filtering (GNHNPSF) is proposed. Firstly, the Sigmoid nonlinear function is examined to suppress the amplitude of the anomalous shock. Subsequently, the hybrid-norm constraints, which involve the application of a l_(1⁄2) norm to the column features of the input signal and a l_(1⁄3) norm to the row features, can be employed to effectively extract distinctive information from the original data. The GNHNPSF is demonstrated to improve the accuracy of bearing fault diagnosis under complex interference, as evidenced by the results of simulation and experiments.

Manajemen pembelajaran guru seni di SMK Negeri 5 Yogyakarta

Purpose: This research aims to explore and analyze learning management carried out by art teachers at Vocational High School (SMK) 5 Yogyakarta. The main focus of this research is to identify effective learning management strategies, which consist of planning and implementing learning carried out by art teachers at the school. Method: This research is qualitative. Subjects of this study were art teachers of ceramics crafts, wooden crafts, and metal crafts at SMK Negeri 5 Yogyakarta. Determination of the subject is done by random sampling, which consists of 3 teachers and the object of research is the effectiveness of art teachers learning process. Data was obtained using observation, interviews, and documentation, as descriptive analysis. Findings: The results of descriptive research reveal that art teacher learning at SMK Negeri 5 Yogyakarta is viewed from the aspects of learning planning and learning implementation. Learning planning activities carried out by teachers have been realized in the form of a syllabus and lesson plans. Teacher learning activities at SMK Negeri 5 Yogyakarta go through three stages, namely preliminary activities, core activities and closing activities. The learning carried out by the teacher was declared successful as proven by the students' work with good work in terms of creativity, beauty, form and completion of the final result. Evaluation of learning by teachers is realized by scoring in the form of numbers based on minimum completeness criteria.

Augmented Reality in Education: A Review of Learning Outcomes and Pedagogical Implications

Purpose: The use of technology in education can either engage students actively, fostering motivation and effective learning, or result in passive learning if it fails to encourage critical thinking, meaning making, and metacognition. Materials and Methods: This concept paper conducts a comprehensive review of research on the potential of augmented reality (AR) in education. It systematically examines AR applications in various academic fields, comparing them to traditional educational technologies and teaching methods. Findings: Augmented reality (AR) exhibits significant potential to revolutionize education by transforming the learning process into a more active, effective, and meaningful experience. AR accomplishes this by providing advanced technology that allows users to interact with virtual and real-time applications, thereby immersing them in realistic educational scenarios. Across a spectrum of subjects, including Medicine, Chemistry, Mathematics, Physics, Geography, Biology, Astronomy, and History, AR offers advantages over conventional technology and traditional teaching methods. Implications to Theory, Practice and Policy: The research underscores the positive potential of AR in education and highlights its capacity to bridge the gap between technology and active, engaged learning. To fully harness these benefits, educators and researchers should address the identified limitations of AR and explore its further integration into educational practices. This paper emphasizes the need for ongoing research and innovation to optimize AR's role in education and enhance learning outcomes. Future research endeavors should concentrate on refining the implementation of AR for optimal educational impact.

Implementation of a "Health Equity Rounds" Curriculum in a Military Internal Medicine Residency Program: A Pilot Study.

Health disparities in the Military Health System (MHS) have been consistently documented despite the system ensuring equal access to care for its beneficiaries. Research has shown that social, economic, and political factors (i.e., Social Determinants of Health) and health care-specific factors like provider bias and systemic discrimination are key drivers of health disparities in the general population. Medical education focused on introducing these concepts using case-based learning has led to effective learning of health equity terminology. However, a significant gap exists in identifying optimal teaching approaches to develop skills to recognize these factors in actual clinical cases. This begs the million-dollar question: can case-based learning help trainees acquire the skills needed to identify the main factors contributing to health disparities in the MHS? A longitudinal case-based curriculum was developed in which clinical cases from the Internal Medicine Wards, Medical Intensive Care Unit, or General Internal Medicine Clinic at the National Capital Consortium were solicited from trainees and analyzed for evidence of health care provider bias and systemic forms of discrimination using small groups. The National Capital Consortium Internal Medicine Residency Program implemented this pilot study in November 2021. A retrospective pretest-posttest survey assessing trainee reactions to the curriculum and changes in self-reported confidence in skills was used for curriculum assessment. Survey data were analyzed using a paired samples t-test. The survey was administered during the last session of the 2022-2023 academic year, with 14 of the 23 available trainees completing it: a 60.8% response rate. Overall, 93% reported that the cases selected that academic year were engaging; the skills they were taught were practice-changing, and the educational value of the curriculum was good, very good, or excellent. Confidence ratings, assessed via a 5-point Likert Scale, demonstrated a statistically significant increase in self-reported confidence in the following skill domains with large effect sizes: identification of bias and systemic discrimination in clinical cases-change in mean: 1.07 (Pre: 3.29, Post: 4.36), P < .001, g = 1.38; recognizing and mitigating personal biases-change in mean: 0.71 (Pre: 3.50, Post: 4.21), P <.001, g = 1.10; participating in a discussion about health care provider bias and systemic discrimination-change in mean: 0.79 (Pre: 3.57, Post: 4.36), P = .001, g = 1.06; and leading a discussion about bias and systemic discrimination-change in mean: 1.00 (Pre: 2.93, Post: 3.93), P = .002, g = 0.98. As the need to address health disparities in the United States becomes more pressing, so does the need for military physicians to recognize the drivers of these disparities within the MHS. Results from this pilot study of Health Equity Rounds suggest that case-based learning may be an optimal teaching approach to improve the skills of military Internal Medicine trainees in identifying and recognizing the impact of health care provider bias and systemic discrimination on clinical cases from the MHS.

Designing, piloting and evaluating (through a matched pre- and post-implementation survey) a targeted e-learning resource on antimicrobial resistance for public health professionals.

Antimicrobial resistance (AMR) is a significant global public health threat and key priority for the public health, especially health protection, workforce to lead by example. There is a paucity of learning resources on this for public health professionals (PHPs) in the UK. This project aimed to develop and disseminate a tailored interactive learning resource and evaluate impact on self-reported intention to change behaviour. Learning objectives were agreed, content developed by the multi-disciplinary team and piloted by PHPs in 2022 alongside a matched pre- and post-implementation evaluation survey. Questions were mapped to the capability-opportunity-motivation-behaviour change model. Before and after responses were calculated to compare change in self-reported knowledge, understanding and behaviour. Significance of change in binary responses was estimated. The resource was delivered using an interactive, user-friendly and cost-free internal platform. Thirty-one PHPs completed the pilot e-learning and survey. Perceived and actual knowledge increased in parallel. Actual knowledge on AMR burden increased from 6.45 to 35.48% (P = 0.004). Self-reported confidence to explain AMR to others improved by 0.71 (95% CI; 0.38-1.04: P = 0.0001) Likert points on a five-point scale. Motivation to advocate for antimicrobial stewardship (AMS) in day-to-day work, improved by 0.71 (95% CI; 0.34-1.08: P < 0.00001) Likert points. Case scenarios were well-received as an effective way to apply theory to practice. Ensuring a well-informed and confident public health workforce is vital for reducing the AMR threat and advocating for AMS with the public and partner organizations. This targeted e-learning module is an effective additional learning medium in contributing to PHPs knowledge, understanding and self-reported intention to change behaviours.

Modeling Course for Open Distance Learning (ODL) in Artificial Intelligence e-Learning for Leadership and Business Success

Open distance learning is often associated with characteristics such as assertiveness, dominance, and competitiveness. This study investigates how interactive open distance learning can benefit students by helping them build stronger relationships with education providers, foster a more inclusive workplace culture, and improve overall team performance, thus helping education business survive. The results of this study could have important implications for open distance learning development programs and training, as well as for promoting diversity and inclusivity in education provider positions and sustain education business in the future. By understanding the relation between leadership style, gender, and authenticity, organizations could develop more effective open distance learning strategies and be viewed as more authentic and effective by their team members and public. ODL planning helps education providers work more efficiently and effectively, thus growing the organization. Keywords: open distance learning, leadership, business planning

Promoting emergent literacy skills among children at risk for specific learning disorder: general vs. Specific vs. Combined metacognitive interventions

ABSTRACT First graders with Developmental Language Disorder are considered at risk for exhibiting Specific Learning Disorder during school years. They also have deficiencies in their metacognitive skills, which leads to less effective learning processes. The current study examined, for the first time, the added value of various types of metacognitive interventions prior to an activity with an e-book for promoting language and literacy skills. Three experimental groups, each comprised of 30 first graders at risk for Specific Learning Disorder, were provided metacognitive intervention (specific, general, or combined) prior to an individual learning process with an e-book. The metacognitive interventions were designed according to the Triple A Model. Two control groups were also assigned: one control group engaged with an e-book without receiving any prior metacognitive intervention, and another followed its regular school activities. All groups were measured pre- and post-individual learning processes for language (vocabulary) and literacy (story comprehension) achievements. Results demonstrated an advantage to prior specific metacognitive intervention for promoting vocabulary as well as story comprehension measures. Although children at risk for Specific Learning Disorder tend to exhibit difficulties with generalisation processes, the combined metacognitive intervention proved to be as effective as the specific one.

Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning

Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications.

An Overview of the Utilization of TikTok in Improving Young Saudi Learners’ Speaking Skills

The present study sought to examine the effectiveness of social media applications, particularly the TikTok platform, in the improvement of English-speaking skills among young Saudi learners from the perspectives of both learners and teachers. The study attempted to answer three questions: How far is the use of social media, especially TikTok, via mobile devices, effective in improving young Saudi learners’ speaking skills outside the classroom? What are young Saudi English language learners’ perspectives on the use of TikTok via mobile devices as a tool for improving their speaking skills? To what extent is learning to speak English through TikTok more effective than traditional class methods? The researcher used a mixed-methods approach to collect data in the study. The sample consisted of 120 secondary school and university students who participated in the study. To reach valid conclusions regarding their perceptions on the use of TikTok as a language development tool, the participants responded to an online 5-point Likert scale questionnaire. Following that step, three experienced English teachers provided their perceptions of the impact of TikTok as an enhancement tool in English-speaking learning. The findings of the study indicated that TikTok is an effective tool for learning English, particularly in relation to speaking skills, since both most young Saudi learners and teachers agreed that they perceive TikTok as an effective learning tool that English learners can utilize to motivate them and enhance their speaking skills.

Under the Vision of Situated Learning: Analysing the Influence of Educational Video Games on English Vocabulary Retention in Chinese Childrens Second Language Acquisition

This study will analyze the influence of educational video games on English vocabulary retention in Chinese childrens second language acquisition. This study base on the situated learning theory and the ABC reading which is both mobile and web-based is adopted as educational game in this research. This experiment takes the Chinese primary school students with an average age of 9-10 years old as experimental subjects, and set three groups of these Chinese students including the control group. By comparing the amounts of words they can remember at different times after learning, researchers can judge whether the educational video games have any influence on students' language learning. Through the experiment, We hypothesis that the video games will have a positive effect on childrens second language learning acquisition. On this basis, video games can be used as tools for language learning to provide more effective learning methods during the process of studying.

Multi-Task Visual Perception for Object Detection and Semantic Segmentation in Intelligent Driving

With the rapid development of intelligent driving vehicles, multi-task visual perception based on deep learning emerges as a key technological pathway toward safe vehicle navigation in real traffic scenarios. However, due to the high-precision and high-efficiency requirements of intelligent driving vehicles in practical driving environments, multi-task visual perception remains a challenging task. Existing methods typically adopt effective multi-task learning networks to concurrently handle multiple tasks. Despite the fact that they obtain remarkable achievements, better performance can be achieved through tackling existing problems like underutilized high-resolution features and underexploited non-local contextual dependencies. In this work, we propose YOLOPv3, an efficient anchor-based multi-task visual perception network capable of handling traffic object detection, drivable area segmentation, and lane detection simultaneously. Compared to prior works, we make essential improvements. On the one hand, we propose architecture enhancements that can utilize multi-scale high-resolution features and non-local contextual dependencies for improving network performance. On the other hand, we propose optimization improvements aiming at enhancing network training, enabling our YOLOPv3 to achieve optimal performance via straightforward end-to-end training. The experimental results on the BDD100K dataset demonstrate that YOLOPv3 sets a new state of the art (SOTA): 96.9% recall and 84.3% mAP50 in traffic object detection, 93.2% mIoU in drivable area segmentation, and 88.3% accuracy and 28.0% IoU in lane detection. In addition, YOLOPv3 maintains competitive inference speed against the lightweight YOLOP. Thus, YOLOPv3 stands as a robust solution for handling multi-task visual perception problems. The code and trained models have been released on GitHub.

The potential and pitfalls of large language models in molecular biosciences

Large language models (LLMs) have revolutionized information processing and are now being applied to complex problems in molecular biosciences. LLMs excel at learning patterns in biological sequences, enabling applications such as predicting the function of DNA regulatory elements, analysing chromatin modifications and predicting the effects of genetic variants. In drug discovery, LLMs are being used to optimize drug properties, design novel molecules and even automate compound synthesis. However, realizing the full potential of LLMs in the molecular biosciences requires addressing challenges such as designing effective learning objectives, generating high-quality, domain-specific datasets and building trust in AI-assisted decision-making. As these challenges are addressed, LLMs hold immense potential for advancing research and driving ground-breaking discoveries in molecular biosciences.

Development of Maths Booklet Media for Primary School Students

This research focuses on the development, validity, and practicality of booklet-based teaching materials for mathematics subjects, specifically on equivalent fractions. Following the Research and Development (R&amp;D) model, which incorporates the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) design stages, this research aims to create effective learning aids. The assessment of the developed Booklet showed high validity and practicality. The material expert assessment rated it "very valid" at 93.33%, while the media design and language experts rated it high at 98.57% and 95%, respectively. In addition, both teachers and students considered the Booklet practical, with response percentages of 87% and 94%, respectively. These findings underscore booklet-based learning materials' effectiveness and ease of use in mathematics education. The Booklet is a valuable tool for teachers to deliver curriculum content effectively and for students to improve their understanding of mathematical concepts. Learning outcomes can be significantly enhanced through such media, facilitating understanding and practical application of mathematical principles.

Development and Acceptability of Web-based Module in Cookery

This study delves into the development and acceptance of a web-based cookery module, aiming to enhance culinary education, particularly in the face of challenges such as the pandemic. These modules offer promising avenues for effective learning experiences and the promotion of self-regulated learning among students. Employing a mixed-method approach utilizing the Input-Process-Output design model, the study involves the validation of the developed module by eight expert practitioners in cookery. Subsequently, its acceptability will be assessed among Senior High School (SHS) Technical-Vocational-Livelihood (TVL) students enrolled in cookery subjects during the academic year 2023-2024. Evaluation of the module by teacher experts utilizes a four-point rating scale, drawing from the Guidelines and Processes for Learning Resource Management and Development System (LRMDS) Assessment &amp; Evaluation – Evaluation Rating Sheet for Non-Print Materials (DepEd, 2009), focusing on content quality, instructional quality, technical quality, and other pertinent factors. Similarly, student feedback is gathered through a five-point scale adapted from Venkatesh et al. (2003) and modified by Namoco, S. (2021) to align with the Unified Theory of Acceptance and Use of Technology (UTAUT) model, offering both rating scale responses and descriptive interpretations. Both teacher experts and student users are purposefully selected for their insights. Initial findings from the expert evaluation indicate a high recommendation for implementing the web-based module in cookery education. However, ongoing data collection and analysis are underway to gauge student-user acceptability comprehensively. This study underscores the significance of well-designed interactive learning modules in fostering self-regulated learning and enhancing student performance in cookery subjects, notwithstanding the challenges posed by the pandemic and economic constraints.

The Effect of Dopamine Stimulation Activities on English Language Learners: An Empirical Research

This study explores the perceptions of dopamine-stimulating activities among students and the challenges faced by teachers in implementing these activities in language education at Dai Nam University. A mixed-methods approach was employed, involving surveys and semi-structured interviews with 220 non-English major students and language teachers. The surveys assessed students' perceptions of various dopamine-stimulating activities, including gamified vocabulary acquisition courses, interactive language games, reward-based speaking activities, physical activity breaks, and project-based learning with rewards. Results indicate varying perceptions across academic years, with first-year students expressing the highest perceptions of effectiveness, followed by a decreasing trend in perceptions among higher-year students. Teachers reported challenges such as balancing novelty with meeting specific learning objectives, resource constraints, and classroom management difficulties. Qualitative data from semi-structured interviews with language teachers provided additional insights into the challenges and strategies encountered in implementing dopamine-stimulating activities. Themes emerged around the excitement and perceived effectiveness of these activities, specific activities utilized by teachers (such as interactive games, multimedia presentations, and real-world simulations), challenges faced, positive outcomes observed (including enhanced student engagement, participation, and language proficiency), and the need for flexibility and creativity in overcoming challenges. The study highlights the importance of considering students' perceptions and challenges faced by teachers in optimizing the effectiveness of dopamine-stimulating activities in language education. Tailored approaches to address varying perceptions and challenges are necessary to ensure meaningful engagement and promote effective language learning experiences. The findings contribute to the ongoing discourse on innovative pedagogical approaches in language education and offer practical implications for educators seeking to enhance student engagement and learning outcomes.

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN

In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior. Most proposals in the literature are based on hardware-independent algorithms that require the network to store the spiking history to be able to update the weights accordingly. In this work, we developed a new learning rule, the Bi-Sigmoid STDP (B2STDP), which originates from the physical properties of MTJs. This rule enables immediate synaptic plasticity based on neuronal activity, leveraging in-memory computing. Finally, the integration of this learning approach within an SNN framework leads to a 91.71% accuracy in unsupervised image classification, demonstrating the potential of MTJ-based synapses for effective online learning in hardware-implemented SNNs.

Achievement of Physical Education Learning Results Based on Gender Review and Learning Motivation on High School Students in the Yogyakarta Region, Indonesia

This research aims to examine differences in Physical Education (PE) learning outcomes for high school students based on gender and learning motivation. The sample in this research was 84 high school students in Yogyakarta and was taken using a simple random sampling system. Data analysis used descriptive statistics and inferential factorial two-way anova. The mean score for male students in the low learning motivation category was 86.29 and female students in the low learning motivation category was 83.71. The mean score for male students in the moderate learning motivation category was 83.21 and female students in the moderate learning motivation category was 84.14. The mean score for male students in the high learning motivation category is 85.64 and female students in the high learning motivation category is 82.64. The results of the Anova test on the learning motivation factor variable value Sig. 0.42 &lt; 0.05 means that there is a significant difference or influence between students' learning motivation (low, medium and high) on PE learning outcomes. In the gender factor variable, the Sig. value 0.001 &lt; 0.05 means that there is a significant difference or influence between gender (male and female) on PE learning achievement. Next, on the variables of learning motivation and gender, the Sig. value 0.000 &lt; 0.05 means that there is an influence or interaction between learning motivation factors and gender on students' PE learning outcomes. The conclusion is that PE design needs to take into account the specifics of gender and type of sport. This is so that male and female students have the right to exercise according to their portion in order to benefit from the PE program at school. Apart from that, it is also to provide maximum enjoyment and achievement for students in various games and sports activities in PE without any pressure. The conclusion is that motivational factors and gender significantly interact and influence students' PE learning outcomes. Therefore, it is important for teachers to consider these two factors in designing and implementing effective PE learning, paying special attention to the specific needs and conditions of each student based on their respective learning motivation and gender. Keywords: Achievement, Learning Results, Physical Education, Students, Gender, Learning Motivation

Mitigating Malware Threats on Emerging Technology: A Machine Learning Approach

Malicious programs and malware threats lead to a substantial vulnerability and pose a fundamental problem. Nowadays, smart devices are becoming more common, and consequently, the risk of malware intrusion is highly observed. This paper presents a comprehensive exploration from the initial to the final phase of an effective strategy and the deployment of a model to detect malware efficiently. The proposed Mitigating Malware Threats on Emerging Technology framework “MMTET” will help mitigate the risk of intrusion. This study explores the complexity of handling datasets. Random Forests and Decision Trees serve as machine learning algorithms for training and testing. Starting with a data collection method to obtain relevant parameters, this paper highlights the importance of well-curated datasets in training using effective machine learning models. Data analysis follows a statistical approach, and the visualization tools are used for identifying inherent biases, imbalances, and trends in datasets. For boosting the dataset’s quality, feature engineering and selection take a central stage to balance the data with new methodologies and detect relevant features with correlation analysis. Experimental result shows that Random Forest has the best performance compared to other methods obtained from different algorithms, with accuracy 98.30%.