Traditional Learning Methodology Research Articles

Improving Cardiopulmonary Resuscitation skills for layperson in cases of heart attack: a scoping review

The proficiency of trained individuals in effectively administering Cardiopulmonary Resuscitation (CPR) is paramount in mitigating the impact of out-of-hospital cardiac arrest incidents. Inadequate CPR skills among laypersons can result in missed opportunities to save lives. Therefore, it is imperative to prioritize unified efforts to enhance CPR competencies within the general populace. This scoping review aims to consolidate literature discussing the enhancement of laypeople's CPR skills using various methodologies.This scoping review employed the PRISMA methodology and encompassed an extensive search across four critical databases (Science Direct, Proquest, Pubmed, and Google Scholar) for literature published between 2013 and 2023. The search utilized the keywords "CPR Teaching," "Skill," and "Layperson." Out of 487 collected articles, 20 were deemed relevant. The findings of all relevant articles consistently indicated an improvement in laypeople's CPR skills following training.Incorporating concise CPR instructional videos and smartphone applications has shown potential for enhancing CPR knowledge and improving emergency responsiveness. Nonetheless, integrating these modern technological approaches with traditional simulation methods has demonstrated greater efficacy in CPR training, resulting in enhanced compression quality and depth.In conclusion, Integrating traditional and technology-based learning methodologies improves the capability of individuals without specialized medical training to administer CPR effectively.

Utilization of artificial intelligence in Outcome-Based Curriculum Evaluation and Development

The integration of artificial intelligence (AI) in education has revolutionized traditional teaching and learning methodologies, offering significant improvements in curriculum evaluation and development. This study explores the utilization of AI in the evaluation and development of an Outcome-Based Curriculum (OBC). AI technologies, including machine learning algorithms and natural language processing, are employed to analyze vast amounts of educational data, providing insights into student performance and curriculum effectiveness. The research highlights how AI can identify learning gaps, predict student outcomes, and recommend personalized learning paths, thereby enhancing the overall educational experience. By leveraging AI, educators can design more adaptive and responsive curricula that meet the dynamic needs of students and industry standards. The findings suggest that AI-driven tools not only streamline the evaluation process but also facilitate continuous curriculum improvement, ensuring that educational programs remain relevant and outcome-focused. This paper underscores the potential of AI to transform curriculum development practices, promoting a more efficient, data-driven approach to education that aligns with contemporary educational goals and outcomes.

Adapting to new challenges in medical education: a three-step digitization approach for blended learning

This research work focuses on evaluating the development of our three-step digitization approach, designed to transform traditional lectures into engaging digital learning experiences. Aimed at offering an easy-to-use solution for educators, this approach addresses the pressing challenges of modern educational environments by enhancing accessibility, engagement, and effectiveness of learning through digital means.Quantitative analysis demonstrated a notable increase in knowledge gain for students engaged with the digital format (t(230) = -2.795, p = 0.006) over traditional methods. Additionally, it was observed that the online setting significantly improved concentration levels (t(230) = -5.801, p < .001) and reduced distractions (t(230) = 2.848, p = 0.005). Emotional assessments, based on the Achievement Emotions Questionnaire (AEQ), indicated an elevation in enjoyment (t(230) = -4.717, p < .001) and a reduction in anxiety (t(230) = 9.446, p < .001) within the digital learning environment. A substantial preference for the digital course format was expressed by 61.0% of participants, with 71.4% acknowledging its superior efficiency compared to 14.3% for traditional lectures.Qualitative feedback underscored the digital format's flexibility, comprehensive learning experience, and immediate feedback as key benefits. Nevertheless, nuances such as a preferred understanding in face-to-face interactions and the absence of a social component in digital settings were noted.To conclude, the findings from this study illuminate the significant advantages of the three-step digitization approach in meeting contemporary educational challenges. By facilitating an enhanced knowledge acquisition and fostering a supportive emotional climate, this approach signifies a promising direction for the future of medical education and beyond, fusing the convenience of digital solutions with the depth and engagement of traditional learning methodologies.

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.

Traditional Teaching and Learning Methodologies: Innovations Needed.

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Systematic review using a spiral approach with machine learning

With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text screening is done intermittently with title/abstract screening, which we examine in three datasets by simulation under 360 conditions comprised of different algorithmic classifiers, feature extractions, prioritization rules, data types, and information provided (e.g., title/abstract, full-text included). Overwhelmingly, the results favored a spiral processing approach with logistic regression, TF-IDF for vectorization, and maximum probability for prioritization. Results demonstrate up to a 90% improvement over traditional machine learning methodologies, especially for databases with fewer eligible articles. With these advancements, the screening component of most systematic reviews should remain functionally achievable for another one to two decades.

Streaming machine learning algorithms with streaming big data systems

As the era of big data unfolds, the need for real-time analytics and decision-making becomes increasingly crucial. Streaming big data systems, designed to process and analyse data in motion, have emerged as a pivotal solution for handling vast streams of continuously arriving information. This research delves into the synergy between streaming big data systems and machine learning algorithms, aiming to harness the power of real-time insights. We explore the challenges and opportunities presented by the dynamic nature of streaming data, emphasizing the importance of adapting traditional machine learning methodologies to suit the evolving requirements of streaming environments.The research begins with an overview of streaming big data systems, laying the foundation for understanding the unique characteristics of data in motion. We then delve into the selection and adaptation of machine learning algorithms that are well-suited for continuous learning and updating. Key aspects of the research include the preprocessing and feature extraction techniques tailored for real-time data streams, ensuring the effective utilization of streaming machine learning algorithms. The paper provides insights into the challenges of model training and updating in a dynamic environment, emphasizing the importance of accuracy and efficiency.

Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images

Hyperspectral remote sensing images, with their amalgamation of spectral richness and geometric precision, encapsulate intricate, non-linear information that poses significant challenges to traditional machine learning methodologies. Deep learning techniques, recognised for their superior representation learning capabilities, exhibit enhanced proficiency in managing such intricate data. In this study, we introduce a novel approach in hyperspectral image analysis focusing on multi-label, patch-level classification, as opposed to applications in the literature concentrating predominantly on single-label, pixel-level classification for hyperspectral remote sensing images. The proposed model comprises a two-component deep learning network and employs patches of hyperspectral remote sensing scenes with reduced spatial dimensions yet with a complete spectral depth derived from the original scene. Additionally, this work explores three distinct training schemes for our network: Iterative, Joint, and Cascade. Empirical evidence suggests the Joint approach as the optimal strategy, but it requires an extensive search to ascertain the optimal weight combination of the loss constituents. The Iterative scheme facilitates feature sharing between the network components from the early phases of training and demonstrates superior performance with complex, multi-labelled data. Subsequent analysis reveals that models with varying architectures, when trained on patches derived and annotated per our proposed single-label sampling procedure, exhibit commendable performance.

Leveraging VR/AR/MR/XR Technologies to Improve Cybersecurity Education, Training, and Operations

The United States faces persistent threats conducting malicious cyber campaigns that threaten critical infrastructure, companies and their intellectual property, and the privacy of its citizens. Additionally, there are millions of unfilled cybersecurity positions, and the cybersecurity skills gap continues to widen. Most companies believe that this problem has not improved and nearly 44% believe it has gotten worse over the past 10 years. Threat actors are continuing to evolve their tactics, techniques, and procedures for conducting attacks on public and private targets. Education institutions and companies must adopt emerging technologies to develop security professionals and to increase cybersecurity awareness holistically. Leveraging Virtual/ Augmented/Mixed/Extended Reality technologies for education, training, and awareness can augment traditional learning methodologies and improve the nation’s cybersecurity posture. This paper reviews previous research to identify how distance and remote education are conducted generally, and how Virtual/Augmented/Extended/Mixed reality technologies are used to conduct cybersecurity awareness training, cybersecurity training, and conduct operations. Finally, barriers to adopting these technologies will be discussed. Understanding how these technologies can be developed and implemented provides one potential way of overcoming the cybersecurity workforce gap and increasing the competencies and capabilities of cybersecurity professionals.

Artificial intelligence integration in personalised learning for employee growth: a game-changing strategy

PurposeThis viewpoint paper aims to explore the adoption of artificial intelligence (AI) in personalized learning for employees and its potential, challenges and ethical consideration to transform the landscape of employee’s performance improvement.Design/methodology/approachA review of relevant research papers, articles and case studies is done to highlight developments in research and practice.FindingsPersonalised learning powered by AI will transform employee education and training as AI evolves, helping individuals and organisations succeed in an ever-changing, knowledge-driven world. Concerns like data privacy, algorithmic bias and human engagement must be addressed to achieve ethical and fair implementation.Originality/valueThis paper discusses the overall influence of AI adoption on personalised learning for employees. It examines how AI technologies have the ability to disrupt traditional learning methodologies, modify organisational learning cultures and encourage an environment that fosters ongoing learning and innovation.

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method’s potential and performance in network intrusion detection.

The role of asynchronous and synchronous activities in university academic performance: A comparative study of traditional and inverted class methodologies

Purpose : The objective of this work is to investigate the effect of synchronous and asynchronous activities on the academic performance of university students. For this purpose, the academic results obtained and the involvement and motivation of the student and the teacher in the different learning methodologies involved are analyzed. Students’ perception of the effectiveness of the flipped class with the face-to-face classroom and online methodologies is compared to that of the traditional learning methodology. Design/methodology/approach : Data were collected from undergraduate students in three marketing courses and were analyzed through a quantitative descriptive, quasi-experimental and cross-sectional study. Academic performance with the different learning methodologies and the effect of synchronous and asynchronous activities on academic performance were compared using logistic regression on each course and methodology. Findings : The results showed that academic performance in synchronous and asynchronous activities was better in the flipped class, except for synchronous activities in the online flipped class. In addition, the best academic performance was obtained in the face-to-face inverted classes. The explanatory effect of asynchronous activities on academic performance was also identified. The greatest predictive capacity and the best prognosis were obtained in the online inverted classes. Students expect to obtain satisfactory results with the inverted class methodology and perceive it as effective, although they prefer a mixture of inverted classes and the traditional methodology. Originality/value : We contribute to theoretical and practical research with this new model to study the influence of the flipped learning methodology and asynchronous and synchronous activities on academic performance. We believe that these results, despite the specific and limited scope of the study, will be of great interest to the teaching community and contribute to improving the motivation and performance of students, which constitutes a main challenge in the higher education system.

Biological informed graph neural network for tumor mutation burden prediction and immunotherapy-related pathway analysis in gastric cancer

Tumor mutation burden (TMB) has emerged as an essential biomarker for assessing the efficacy of cancer immunotherapy. However, due to the inherent complexity of tumors, TMB is not always correlated with the responsiveness of immune checkpoint inhibitors (ICIs). Thus, refining the interpretation and contextualization of TMB is a requisite for enhancing clinical outcomes. In this study, we conducted a comprehensive investigation of the relationship between TMB and multi-omics data across 33 human cancer types. Our analysis revealed distinct biological changes associated with varying TMB statuses in STAD, COAD, and UCEC. While multi-omics data offer an opportunity to dissect the intricacies of tumors, extracting meaningful biological insights from such massive information remains a formidable challenge. To address this, we developed and implemented the PGLCN, a biologically informed graph neural network based on pathway interaction information. This model facilitates the stratification of patients into subgroups with distinct TMB statuses and enables the evaluation of driver biological processes through enhanced interpretability. By integrating multi-omics data for TMB prediction, our PGLCN model outperformed previous traditional machine learning methodologies, demonstrating superior TMB status prediction accuracy (STAD AUC: 0.976 ± 0.007; COAD AUC: 0.994 ± 0.007; UCEC AUC: 0.947 ± 0.023) and enhanced interpretability (BA-House: 1.0; BA-Community: 0.999; BA-Grid: 0.994; Tree-Cycles: 0.917; Tree-Grids: 0.867). Furthermore, the biological interpretability inherent to PGLCN identified the Toll-like receptor family and DNA repair pathways as potential combined biomarkers in conjunction with TMB status in gastric cancer. This finding suggests a potential synergistic targeting strategy with immunotherapy for gastric cancer, thus advancing the field of precision oncology.

Digital transformation of Industrial Automation Course in virtual learning environment during COVID-19 Pandemic

A learning environment is a diverse platform where users are engaged and interacted to learn new skills by any means of learning methodology. Nowadays the millennial students are fast learners and expecting a new learning environment and methodology to enhance their skills in learning and acquiring knowledge. Though they possess many good skills they are under various pressurized social and family environments due to COVID-19 pandemic which lead to get depressed quickly. The traditional teaching learning methodology is also increasing their stress. This stress will affect the cognitive functioning among the students and the mentors and further leads the students to hate their subjects and slowly reduce their involvement and interaction with subject instructors. To overcome the above problem, the instructors should have the responsibility to incorporate an arsenal of strategies that would inoculate learners against the negative attitude by providing enough positive experiences during this post COVID situation. The proposed work gives a brief description about the online and off line learning environments and their impact on the education with societal aspects. It collaborates the research from different traditions and gives description about the connections that can be drawn between the teacher-student interactions and with the peer interaction through case studies. Keywords— Virtual Learning Environment, Discord, Software learning, Post COVID

Transfer learning as an AI-based solution to address limited datasets in space medicine

The advent of artificial intelligence (AI) has a promising role in the future long-duration spaceflight missions. Traditional AI algorithms rely on training and testing data from the same domain. However, astronaut medical data is naturally limited to a small sample size and often difficult to collect, leading to extremely limited datasets. This significantly limits the ability of traditional machine learning methodologies. Transfer learning is a potential solution to overcome this dataset size limitation and can help improve training time and performance of a neural networks. We discuss the unique challenges of space medicine in producing datasets and transfer learning as an emerging technique to address these issues.

Perceptions of students about the educational environment in Gomal Medical College Dera Ismail Khan

Introduction:Student satisfaction, academic performance, and learning effectiveness have all been related to the educational environmental perceptions in medical institutes. Students' opinions of their educational environment might help medical schools discover impediments and opportunities to improve student learning.&#x0D; Objective:To evaluate student perception related to current traditional and integrated learning methodologies in the Gomal Medical College Dera Ismail Khan.&#x0D; Methods: The Dundee Ready Education Environment Measure (DREEM) questionnaire was provided to students of Gomal Medical College Dera Ismail Khan after approval from the institute. One hundred and sixty-one students out of 190 returned the questionnaire. The data was analyzed with SPSS 27.0. All questions were evaluated on a 5-point Likert scale.&#x0D; Results:The mean age of the students was 21.34±1.73 years. There were 89 (55.34%) males and 72 (42.66%) female students. The mean DREEM score among the students was 116.15±3.73. The highest score among the students was 153 and the lowest score was 42.The scores were higher among males than females.&#x0D; Conclusion: The students were more positive and self-assured. Students had a more positive attitude toward teachers, the learning environment, and the atmosphere. However, they were lacking in social perceptionrequiring attention.&#x0D; KEYWORDS: Dundee Ready Educational Environment Measure, educational environment, medical institutes, perception

The Impact of Online Learning In The Internal Medicine Rotation Among Medical Clerks During The Covid 19 Pandemic and Lockdown

Background: The impact of online learning during internal medicine rotation among graduating medical students was evaluated during COVID 19 pandemic. Objectives: The study aimed to gauge acceptability of a self-directed learning, recognize frustrations, identify coping mechanisms, assess rate of learning, evaluate study habits and appraise level of readiness in handling actual patients later in practice among graduating medical clerks. Methodology: An ambispective cohort design study involving both retrospective and prospective follow-up of respondents. The retrospective component involved blended learners with clinical experience (Group 1). The prospective component included online learner with clinical experience (Group 2) and online learner without clinical experience. Results: Group 1 showed that they favor pure traditional learning over pure online learning in contrast to the other 2 groups which rated acceptability of online learning high. All groups had high levels of frustration with their inability to experience actual patient exposure, had moderate to high level of coping mechanisms, and rated high the traditional learning and blended learning methodologies. In contrary, all groups rated low to moderate contributions of online learning to their learning. Group 3 had a significantly higher mean overall readiness score compared to the two other groups. Conclusion: The study showed the different levels of impact of online learning to the different population of medical clerks in terms of its acceptability as an alternative way of learning. Actual patient exposure is of main concern while different coping mechanisms maximize knowledge acquisition. Key words: Online learning, self directed, COVID 19 pandemic, Medical clerks

Extracting the Relationships Among Students Based on Accessing Pattern of Digital Learning Attributes

A paradigm shift can be expected in the education sector, especially after the COVID-19 pandemic. E-learning systems are being adopted by all the stakeholders as physical meetings are not feasible. Different online learning attributes, such as video conferencing tools, coding platforms, online learning frameworks, digital books, and online videos, are available, which are enhancing the traditional learning methodology. Now, the main challenge for the educationists is to identify how these attributes are utilized by the learners. In this article, we have represented any online class as a social network where students are connected through learning platforms. We have mined the network based on several network measuring parameters to recognize the maneuvering pattern of these digital resources or attributes by the students. We have also proposed a community detection method that would form different groups among the students based on their comfortable learning patterns. As a case study, we have scrutinized the accessing patterns of different digital learning resources by some particular students. The experimental results show the significant relationship between the digital resource accessing patterns of the students with their immediate performance in the test. The satisfactory inquisitive results of our approach would definitely inspire the researchers of interdisciplinary areas to probe further in this domain.

Narrative-based learning using mobile devices

PurposeThe aim of this paper is to explore whether the use of an active learning methodology implemented through a mobile phone can help future teachers to develop more effective reading promotion activities than those based on traditional learning methodologies.Design/methodology/approachA study was conducted based on the comparison of perceptions of two groups of teacher training students. The experimental group was trained in an active methodology to promote reading on mobile phones, whilst the control group was trained in a classical methodology also using the same devices. Variables were observed using a self-administered questionnaire, and the scores obtained were analysed from their descriptive statistics of the comparison of means of Kruskal–Wallis H test.FindingsThe results showed that students perceived significant improvements associated with active learning methodology. The variables with the most remarkable results were those related to better use of the class, participation and satisfaction. However, the ubiquitous variable obtained the fewest differences, maybe because both learning methodologies were applied using mobile devices.Originality/valueThe conclusions of this study clearly suggest that combining active learning methodologies and the use of mobile phones to promote reading could lead to better results than applying traditional learning methodologies. The value of this study paves the way for future research to move forward in the discovery of effective teaching strategies based on active methods and mobile devices.

The pivotal role of students’ absorptive capacity in management learning

Within a research context dominated by an increasing interest in innovative learning methodologies in management education, an individual's capacity to establish links between existing and new knowledge, that is, absorptive capacity (AC), has been surprisingly neglected in management (higher) education inquiry. This study helps to close this gap by investigating the role of management students' AC on their academic performance. The study also examines the moderating effect on this relationship of using traditional learning methodologies (such as lectures), innovative learning methodologies (such as interacting with digital platforms), and having a cooperative climate in the classroom. Secondary and primary data are combined from samples of students enrolled in a strategic management course. Results show that students with higher AC achieve higher levels of academic performance. This relationship is moderated by the intensive use of traditional learning methodologies and by cooperative climates. Students who have higher AC benefit more from traditional learning methodologies and from a classroom with a cooperative climate than those with lower AC. Results will be useful to management education instructors, higher education administrators, and policymakers, when revising current educational programmes.