Effective Learning Research Articles

PENGEMBANGAN MODEL PEMBELAJARAN TEMATIK INTEGRATIF BERBASIS KEARIFAN LOKAL UNTUK MEMBENTUK KARAKTER ANAK (PENELITIAN PENGEMBANGAN DI RA KHALIFAH INSANI METRO

This research was motivated by problems discovered during initial observations in Group A RA. Khalifah Insani Metro City, namely some students do not understand their respective characters because their learning activities are still conventional so they rarely link local culture with learning themes. The aim of this research is to develop a prototype of an integrative thematic learning model based on local wisdom for early childhood and determine the effectiveness of an integrative thematic learning model based on local wisdom in forming character. The research approach used in this research is Research and Development (R&D) with J. Moonen's developmental instructional model. The sample used was 1 teacher and 20 group A students at RA Khalifah Insani Metro City. The results of the research show that the development of an integrative thematic learning model based on local wisdom to shape children's character regarding product use is able to facilitate and instill good manners in students and can create appropriate and effective learning.

IMPLEMENTASI SISTEM PEMBELAJARAN EFEKTIF SEBAGAI STRATEGI PENGUATAN PROFESIONALISME GURU DALAM ERA DIGITAL

This research aims to analyze teacher professionalism strengthening through the implementation of effective learning systems. Using a mixed-method approach, the study was conducted on 100 teachers selected through purposive sampling. Data collection included questionnaires, in-depth interviews with 20 teachers, learning observations, and focus group discussions. The results showed significant improvements in teacher competencies: pedagogical (25%), professional (28%), learning technology utilization (35%), and evaluation skills (30%). The implementation of effective learning systems positively impacted student learning motivation (75%) and learning outcomes (68%). Key supporting factors included policy support and availability of continuous professional development programs, while inhibiting factors included administrative burden and limited technology access. The teacher professionalism strengthening program proved effective in improving learning quality, although it still requires strengthening of the mentoring system and development of digital learning platforms..

Advancements in technology-enhanced assessment in tertiary education

The field of assessment in tertiary education is currently experiencing a significant shift mostly due to the technological advancements. Technology is, in fact, both threatening assessment practices and empowering them when integrated in the assessment process. This special issue discusses the evolution of technology-enhanced assessment in tertiary education focusing on the use of digital resources such as gamification and artificial intelligence to improve learning and evaluation methods. Research findings showcased in this issue delve on the ability of technology-enhanced assessments to promote effective personalised learning experiences while also addressing the ethical dilemmas these innovations bring. The blend of conversations about assessment with AI driven evaluations, immersive simulations and gamified platforms explores the benefits and challenges for teachers and learners alike, not forgetting the importance of sound methodological approaches for both summative and formative assessment. This editorial summarises contributors’ perspectives on how these innovations are changing assessment methods and emphasises the importance of integrating them thoughtfully. At the same time, questions are raised about the evolving role of educators in a technology-driven educational landscape.

Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

Comparative Analysis of Machine Learning Models for Predicting Rice Yield: Insights from Agricultural Inputs and Practices in Rwanda

Food security is a global challenge, especially in developing countries like Rwanda. With a growing population and limited agricultural land, Rwanda struggles to meet increasing food demands. Rice is a staple food crop in Rwanda, playing a crucial role in the country’s food security. However, factors like climate variability, soil nutrient management, and limited access to high-quality inputs hinder rice yield optimization. This paper investigates the most effective machine learning model for predicting rice crop yield in Rwanda, using agricultural inputs and practices. The study used secondary datasets from the National Institute of Statistics of Rwanda (NISR) for rice yield prediction. Eight supervised machine learning algorithms were used, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, eXtreme Gradient Boosting Tree, and AdaBoost. The models were evaluated based on their accuracy in predicting rice yields, with RMSE, MAE, and Relative Error as primary metrics. The feature importance analysis was also conducted to identify significant factors influencing yield predictions. The study’s findings revealed that the Adaptive Boosting Tree model outperformed the other machine learning models in predicting rice yield. This model achieved RMSE, MAE and Relative Error of 0.69, 0.46 and 12.4%, respectively, indicating a high level of predictive accuracy. The feature importance analysis further highlighted the key factors that contributed to rice yield predictions, with the quantity of inorganic fertilizer, degree of erosion, season, and seed type emerging as the most influential variables. The study demonstrates the effectiveness of machine learning models, particularly the Adaptive Boosting Tree, in improving rice yield predictions and highlighting the crucial role of agricultural inputs like fertilizer and seed type, in influencing crop yields. The output from this study will help the farmers and stakeholders to make data-driven decisions about resource use and crop management.

Development of Learning Models to Enhance Students’ Creative Thinking: A Systematic Literature Review

Creative thinking is an essential component in education that drives innovation, problem-solving, and the development of students’ critical thinking skills. This study aims to review various learning models designed to enhance students’ creative thinking abilities through a systematic literature review (SLR). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this research identifies, screens, and evaluates relevant articles from the Scopus database, resulting in eight studies that meet the inclusion criteria. The results indicate that, following innovative learning models such as play-based activities, digital-based virtual classrooms (VCLE), basic movement through play approaches, and project-based mathematics learning are effective in improving creativity, problem-solving, collaboration, and student learning motivation. The integration of technology, STEAM approaches, and creative game design also show positive effects in enhancing students’ creative thinking skills. The higher distribution of research at the tertiary education level highlights the need for greater focus on other educational levels, particularly secondary education, to ensure the benefits of learning innovation are evenly distributed. These findings provide practical implications for educators and policymakers, emphasizing the importance of using innovative teaching methods, integrated technology, and the role of teachers as facilitators to develop a conducive learning environment for student creativity. The study also identifies several limitations, including small trial scales and short research durations, indicating the need for further research with larger scales and longer durations. By continually exploring and developing effective learning models, it is expected that students can achieve their maximum potential in dynamic and diverse learning environments.

Motivation, Learning Styles and Strategies in English Language among First-Year College Students in Public HEI’s in Sulu

English, as the most frequently spoken second language, and sometimes referred to as the global lingua franca, acts as a bridge for communication across cultures and nations. Learning English is a strong tool that allows people to fully participate in the global society by encouraging understanding, collaboration, and personal growth. This descriptive-comparative paper analyzed the motivation, learning styles, and learning strategies of first year college students from public higher education institutions (HEIs) in Sulu, Philippines. Participants were purposively sampled (n=200) to participate in the study. Findings indicated that first year college students in the province were integratively motivated to learn the English language. They particularly engaged in psychological and sociological learning styles through speaking to fluent English speakers, understanding emotional cues, reflection, and self-assessment. They engage in metacognitive (planning, organizing thoughts) and cognitive (assessment, analysis) to learn and understand English. Educators should adopt a holistic approach to teaching English, one that motivates students, respects their individual learning styles, and equips them with effective learning strategies. Such an approach could likely lead to more effective and meaningful language learning experiences, which could improve students' proficiency and confidence in using English.

Navigating pedagogical dilemmas in interdisciplinary education: a reflective practice perspective

Interdisciplinary education is increasingly recognized as essential in higher education for addressing complex real-world issues. Although this paradigm shift began in the 20th century, challenges in interdisciplinary pedagogy persist, including classroom preparation, delivery, assessment, and feedback. One significant challenge is disciplinary distance, the disparities between disciplines that hinder effective interdisciplinary teaching and learning. Additionally, the varying degrees of disciplinarity—from intradisciplinary to transdisciplinary—complicate this landscape. Despite interdisciplinary pedagogy's potential to enhance critical thinking and problem-solving, it presents numerous dilemmas. As a graduate teaching assistant, I have faced several such dilemmas. This reflection discusses these interdisciplinary dilemmas and explores epistemological and pedagogical practices to navigate them. Leveraging my experience as a PhD researcher and educator, I examine how teachers with multidisciplinary backgrounds can navigate the complexities of interdisciplinary education.

Amp-Holo-Net enables speckle-free and high-quality coaxial amplitude-only holographic display

At present, amplitude-only holograms (AOH) are widely applied because of the high response speed and small pitch of amplitude spatial light modulators. However, AOHs are generally generated by traditional diffraction theories, and there are no effective deep learning algorithms to generate high-quality coaxial AOHs, which makes it difficult to achieve speckle-free, high-quality holographic displays using amplitude spatial light modulators. Thereby, we propose two cascaded deep neural networks, embedding physical models of the coaxial AOH to generate speckle-free and high-quality coaxial AOHs within an acceptable time, named Amp-Holo-Net, which is the first work that introduces deep learning to generate coaxial AOHs. We demonstrate the superiority of the proposed method through simulation, achieving an 81% reduction in speckle contrast of two-dimensional (2D) monochrome reconstruction and a 60% improvement in peak signal-to-noise ratio of 2D color reconstruction compared with point source method, and optical experiments are conducted to verify the validity of the proposed method. We believe our method fills the blank of AOH algorithms and paves the way for holographic displays based on amplitude spatial light modulators.

Quantifying the Relationship Between Self-Efficacy and Mathematical Creativity: A Meta-Analysis

This study is a meta-analysis to examine the relationship between self-efficacy and mathematical creativity. This meta-analysis included 21 studies with a total sample size of 11,621 participants and 33 effect sizes across the studies. Data analysis using a random effects model using the “meta” package (version 7.0-0) in R software version 4.3.0. The results showed a positive and significant relationship between self-efficacy and mathematical creativity (z=3.51;95% CI [0.09, 0.32], p<0.001). The influence of self-efficacy on mathematical creativity is included in the low category (re=0.21). These findings had no publication bias issues with Egger’s test (t=−0.03; p=0.978) and were stable against the impact of unpublished studies (Fail−safe N=5101; p<0.001). Meta-regression revealed two variables that showed significant results: measurement method (Q=11.17;df=2; p=0.0038) and study location (Q=372.41, df=9; p<0.0001). This study provides valuable information about the relationship between self-efficacy and mathematical creativity, but more research is needed to develop effective and efficient learning strategies.

Navigating pedagogical dilemmas in interdisciplinary education: a reflective practice perspective

Interdisciplinary education is increasingly recognized as essential in higher education for addressing complex real-world issues. Although this paradigm shift began in the 20th century, challenges in interdisciplinary pedagogy persist, including classroom preparation, delivery, assessment, and feedback. One significant challenge is disciplinary distance, the disparities between disciplines that hinder effective interdisciplinary teaching and learning. Additionally, the varying degrees of disciplinarity—from intradisciplinary to transdisciplinary—complicate this landscape. Despite interdisciplinary pedagogy's potential to enhance critical thinking and problem-solving, it presents numerous dilemmas. As a graduate teaching assistant, I have faced several such dilemmas. This reflection discusses these interdisciplinary dilemmas and explores epistemological and pedagogical practices to navigate them. Leveraging my experience as a PhD researcher and educator, I examine how teachers with multidisciplinary backgrounds can navigate the complexities of interdisciplinary education.

Computer-aided diagnosis for China-Japan Friendship Hospital classification of necrotic femurs using statistical shape and appearance model based on CT scans.

The purpose of this study is to quantify the three-dimensional (3D) structural morphology, bone mineral density (BMD) distribution, and mechanical properties of different China-Japan Friendship Hospital (CJFH) classification types and assist clinicians in classifying necrotic femurs accurately. In this study, 41 cases were classified as types L2 and L3 based on CT images. Then, 3D Statistical Shape and Appearance Models (SSM and SAM) were established, and 80 principal component (PC) modes were extracted from the SSM and SAM as the candidate features. The bone strength of each case was also calculated as the candidate feature using finite element analysis (FEA). Support vector machine (SVM) and Extreme Gradient Boosting (XGBoost) were used to establish 10 machine learning models. Feature selection methods were used to screen the candidate features. The performance of each model was evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. This resulted in a SVM model for CJFH classification with the performance: accuracy of 87.5%, sensitivity of 85.0%, specificity of 76.0%, and AUC of 94.2%. This study provided effective machine learning models for assisting in diagnosing CJFH types, increasing the objectivity of the diagnosis. They may have great potential for application in clinical assessments of CJFH classification.

Analysis of the Effectiveness of Project-Based Seamless Learning Model on Concept Understanding

Aims: The aim of this study was to identify the effectiveness of project-based seamless learning model on concept understanding. Study Design: The research method used quantitative experimental design. Methodology: The research method used quasi experiment with research design nonequivalent pretest posttest group design, where there is an experimental group and a control group, the experimental group is given treatment and there is no treatment on the control group. The research subjects were 160 people, 80 people in the experimental group and 80 people in the control group. Data collection using tests conducted pretest and posttest. Data analysis used normality test, homogeneity test and independent sample t test. Results: The results of research and discussion, it can be concluded that there is an effective project-based seamless learning model on concept understanding. Based on the results of hypothesis testing on the posttest, the value on the posttest of the experimental group and the control group obtained a significance value of 0.002 <0.050. And also the difference in the average posttest score of the experimental group is 84.06, greater than the lower control group of 80.56. This proves that there are differences caused by the application of the learning model in the two groups. The experimental group used a project-based seamless learning model while the control group used a conventional model. Conclusion: there is the effectiveness of project-based seamless learning model on concept understanding. The hope is that the project-based seamless learning model can be applied at various levels of educational institutions or in various subject matters so that the quality of learning will be even better and continue to improve rapidly.

Analysis of the Effectiveness of the Teaching at the Right Level (TaRL) Approach in Improving Students' Literacy Skills through the 15-Minute Literacy Program at Primary School

This research aims to analyze the effectiveness of the Teaching at the Right Level (TaRL) approach in improving students' literacy skills through a 15-minute literacy program. This approach is based on grouping students according to their ability levels, allowing teaching strategies to be tailored to students' needs. The method used in this study is descriptive with a qualitative approach, where data is collected through observation, interviews, and documentation. Students are grouped based on their reading abilities: beginner, letters, words, paragraphs, and stories. The instruments used in this study are initial diagnostic and developmental tests conducted orally at the beginning and during the research process. These tests measure the overall development of students' reading skills over time. Data analysis is conducted to evaluate students' engagement and their responses to short but intensive literacy activities. The results indicate a significant improvement in students' literacy skills at all reading ability levels, both in comprehension and reading fluency. This improvement suggests that the TaRL approach can effectively enhance elementary school literacy skills. This research is expected to significantly contribute to developing more effective literacy strategies in elementary schools to optimize short yet effective learning time.

The impact of surgical simulation and training technologies on general surgery education.

The landscape of general surgery education has undergone a significant transformation over the past few years, driven in large part by the advent of surgical simulation and training technologies. These innovative tools have revolutionized the way surgeons are trained, allowing for a more immersive, interactive, and effective learning experience. In this review, we will explore the impact of surgical simulation and training technologies on general surgery education, highlighting their benefits, challenges, and future directions. Enhancing the technical proficiency of surgical residents is one of the main benefits of surgical simulation and training technologies. By providing a realistic and controlled environment, With the use of simulations, residents may hone their surgical skills without compromising patient safety. Research has consistently demonstrated that training with simulations enhances surgical skills., reduces errors, and enhances overall performance. Furthermore, simulators can be programmed to mimic a wide range of surgical scenarios, enabling residents to cultivate the essential critical thinking and decision-making abilities required to manage intricate surgical cases. Another area of development is incorporating simulation-based training into the wider surgical curriculum. As simulation technologies become more widespread, they will need to be incorporated into the fabric of surgical education, rather than simply serving as an adjunct to traditional training methods. This will require a fundamental shift in the way surgical education is delivered, with a greater emphasis on simulation-based training and assessment.

The Impact of Artificial Intelligence on Today’s Student Life

AI has profoundly impacted the lives of students by transforming various aspects of their education and daily activities. AI can be a very useful learning tool in a variety of contexts. Students can access learning materials tailored to their needs and learning styles, resulting in more effective learning. AI also speeds up evaluation and scoring. However, there are also issues to contend with, such as the dangers of relying on technology, problems with data privacy, and the possibility of a decline in social interaction. This article discusses the impact of AI on college students and its implications for the future of higher education. The rapid development of technology has made the emergence of the millennial era where technology has become the need of every individual, especially youth and women in the millennial era who are faced with technological advancements, so it is required to always sort out every information obtained, in order to prevent the occurrence of bad impacts because access between the worlds is easy to obtain so that it can be a weapon for irresponsible people to attack an area from the next generation. The purpose of this study is to find out the various impacts of technological developments.

The Impact of Clinical Practice Stress on Nursing Professional Competence among Undergraduate Nursing Students: A Cross-Sectional Study.

This study aims to explore the impact of undergraduate nursing students' clinical practice stress on nursing professional competence. The research was performed as a cross-sectional study. From January 2023 to February 2023, a total of 114 nursing students in their first clinical practice at a university in Taiwan were included in the survey. Data collection was conducted using the Clinical Practice Stress Scale and the Nurse Professional Competence Scale. Pearson's correlation analysis was used to analyze the relationship between clinical practice stress and nursing professional competence, and multiple regression analysis was used to explore predictors of nursing professional competence. The clinical practice stress of nursing students was at a medium level (37.75 ± 12.24), and the nurse professional competence was at a high level (79.34 ± 10.12). Clinical practice stress was negatively correlated with nurse professional competence (r=-.311, p=.001). Clinical practice stress could predict nursing professional competence (F=3.041, p=.013), which explained 12.3% of the total variance (adjusted R2 = .083, p < .001), among which the stress of taking care of patients in clinical practice had the highest β value (β=-.336, p=< .001). Clinical practice stress can predict nursing professional competence. The stress of taking care of patients in clinical practice is a major predictor of nursing professional competence. Nursing students who experience the stress of clinical practice tend to develop lower nursing professional competence, highlighting the need for effective coping strategies and supportive learning environments for nursing students.

Improving Academic Standard of Low-Performing Adolescents: A Delphi Study

The Indian educational landscape, marked by infrastructure limitations and disparities in educator quality and school types, presents an opportunity for equitable education enhancement. This study employs the Delphi method to identify effective learning strategies for teacher training and policy considerations. Initially, 57 experts provided opinions on strategies for academically low-performing adolescents. Subsequently, 25 experts ranked these strategies’ importance using a Likert scale. In an independent round, 50 new experts with teaching experience shared insights into strategies implemented for low-performing school adolescents. Of 38 proposed strategies short-listed in the first round, experts strongly recommended 21, yet educators implemented only nine. Notably, strategies supported by existing literature and strongly endorsed by experts, such as experiential learning, reading aloud, and independent thinking, remain unimplemented. This absence could impede adolescents’ knowledge and hinder academic success. Integrating evidence-based strategies into the curriculum could enhance low-performing adolescents’ academic performance, career prospects, and life satisfaction.

Comparing the effectiveness of multiple text reading and rereading on knowledge retention and metacognitive accuracy

AbstractAlthough learning approaches are designed to enhance individuals' ability to store and retrieve information, not all of them are considered effective. The goal of the present study was to experimentally compare the test performance as well as the accuracy of metacognitive judgements of a multiple text reading group, rereading group, and single reading group in a one-day vs. one-week delayed test (3 × 2 between subject design). A total of 186 psychology students (Mage = 20.76) participated in the experiment focused on reading comprehension, accuracy of metacognitive judgments and knowledge retention. Results of the knowledge test indicate that in the one-day delayed test, multiple text reading and rereading yield similar results: both deliver slightly better results than single reading. In the one-week delayed test, though, multiple text reading yields better results than rereading, and both these reading approaches outperform single reading. Moreover, multiple text reading results in fairly robust knowledge retention with only a slight decrease in scores between the one-day delayed and one-week delayed test. Regarding metacognitive monitoring, judgements of learning in the multiple text reading group remained relatively stable after each reading and participants were underconfident about their knowledge. In the rereading group, judgements of learning increased after each reading and participants were overconfident about their knowledge, especially on the one-day delayed test. These findings have implications for educational practices aimed at enhancing learning outcomes and promoting effective learning.

E-Healthcare System for Stroke Management Using IoT and Machine Learning

Machine learning is one of the hottest topics in e-healthcare and has a big social impact. It is challenging to build and develop an efficient and successful e-healthcare system without machine learning. Machine learning's primary function is to analyse healthcare data, which is gathered from various sources and can be both homogeneous and heterogeneous. Therefore, creating an effective machine learning algorithm that functions with both homogeneous and heterogeneous data is a difficult task. On the other hand, it is noted that a sizable number of health-related products, such as fitness bands, smart watches, and sensors, are being developed, and the majority of people use these items to track their own health. Each person's health data is also gathered by these devices, and a machine learning algorithm is integrated into the data to identify any strange patterns or behaviours. These gadgets capture any aberrant behaviour or pattern, and an alarm message is sent to the individual. Thus, machine learning (ML) presents a promising technique for cutting healthcare device costs and describing doctor-patient relationships at a higher level.