Integration Of Learning Research Articles

The Development of Learning Achievement in Military Theory Course Through Integrated Learning Activities of the Sophomore at Guang Zhon Huashang Vocational College

Background and Aims: In the current educational environment, military theory classes have always been regarded as a special learning experience. Guangzhou Huashang Vocational College offers military theory courses for sophomore students, aiming to improve students' military accomplishment and national defense awareness through systematic theoretical study and practical operation. However, there are several issues concerning the setting and implementation of this curriculum that are worth exploring in depth. For example, does military theory have an impact on the academic success of sophomore students? Is the effect positive or negative? How satisfied are students with the course? Based on the concern of these problems, this study takes the sophomore students of Guangzhou Huashang Vocational College as the research object, analyzes the teaching practice of military theory courses, and discusses the influence of military theory courses on academic achievement. The objectives of this research were 1) To compare academic achievement in Development of Learning Military Theory Course subjects before and after the blended learning. 2) Study on students' satisfaction towards integrated learning activities. 3) Study students' interest in interactive games in integrated learning activities. Methodology: The research tools include 1) A blended by traditional learning management plans, a total of 6 plans. 2) Academic achievement with multiple choice test before class - after class total of 30 items.3) A questionnaire on students' satisfaction towards integrated learning activities totals 10 items.4）A questionnaire on students' interest in interactive games in integrated learning activities totals 5 items. Results: The results of the research found that 1) Academic achievement after the blended learning arrangement was higher than before with statistical significance at the.05 level.2) Students were satisfied with the integrated teaching and learning arrangement. at a high level 3) Students' interest in interactive games in integrated learning activities at a high. Conclusion: The study discovered that the use of blended learning greatly increased academic achievement and that students expressed great satisfaction with both the integrated teaching strategy and the interactive games incorporated into the learning activities.

IMPLEMENTASI MODEL PEMBELAJARAN TEMATIK BERBASIS COOPERATIVE LEARNING KELAS II DI MI NU KESESI

Thematic learning is a learning activity that integrates material from several subjects in one theme/topic of discussion. Thematic learning will provide integrated learning opportunities that place more emphasis on student participation/involvement. A teacher who wants the teaching and learning process to be successful must be able to choose and determine the thematic learning model that will be used in the teaching and learning process, so that it can stimulate the enthusiasm of each student to be actively involved in their learning experience. This research aims to describe how the class II thematic learning model is implemented at MI NU Kesesi. This research method uses a descriptive qualitative approach with a field research type where data collection techniques use observation, interviews and documentation. The collected data was analyzed through 3 stages, namely data reduction, display and data verification. The results of this research show that the implementation of thematic learning carried out at MI NU Kesesi using the cooperative learning model is considered to be very appropriate to the conditions of the students and the students' abilities, which can be seen from the great enthusiasm of the students in participating in learning when using the cooperative learning model. . And the teacher attracts students' activity by asking questions about the material and relating it to everyday life.

Computer-Modeling System of Agronomist’s Labor Functions

The goal of the paper is to develop the computer-modeling system of an agronomist’s labor functions as the most optimal solution for teaching preschoolers and primary schoolchildren about trends and technologies in farming, aimed at increasing the attractiveness of professions in the field of agriculture. Among the main objectives of the research is the study of the essence of computer modeling as a teaching method in the works of domestic scientists, as well as the construction of models and their implementation using high-level programming languages in order to obtain an information system for the labor functions of an agronomist.Materials and methods. In the course of the analysis, a study was carried out of scientific and technical literature on the use of computer-modeling systems in the educational field. The analysis of the market for computer-modeling systems in the agricultural sector of crop production was carried out. When visualizing and describing system models, UML modeling notation was used. A diagram of use cases for this notation made it possible to describe the functional requirements and interactions between the system and its user. A UML notation sequence diagram was used to model the interaction of the main menu, game scene, and game character over time. The class diagram made it possible to define the structure of the system and the relationships between its objects, as well as an overview of the attributes and operations of the identified classes. The designed system models were implemented in the C# programming language in the Visual Studio development environment.Results. The study revealed the need to introduce new forms, methods and means of teaching into the content of the modern educational process, the implementation of which, largely, is carried out using information technology. The most optimal solution to the issue of teaching children about trends and technologies in agriculture, which has become relevant in the light of the rapid development of agriculture and its digital transformation, was the introduction of computer- modeling systems. The analysis of literature on the research problem has shown that in the works of L. Zhuk, M. Larionova, N. Rozova, etc. great attention is paid to the use of computer modeling as a teaching tool. According to the author, the use of this technology in the educational process makes it possible to implement such teaching principles as the principle of integrative learning, the principle of a practice-oriented approach, the principle of differentiation of learning and the principle of dialogical learning. There are no domestic developments in the subject area under consideration on the information systems market. The author of the study designed and implemented, using the object-oriented programming language C#, the computer-modeling system of agronomist’s labor functions as a technology for teaching professional tasks of an agronomist at various stages of agrarian evolution. The proposed solution allows you to be acquainted with four significant agrarian revolutions that played a decisive role in the transformation of agriculture: the Neolithic revolution, the Renaissance agricultural revolution, the green revolution and the digital revolution in agriculture. Conclusion. It is important to note that the computer-modeling system of the labor functions of agronomist is of interest for preschool and primary education institutions. This development can also be used in the system of additional education for children. Using a computer game in the educational process will expand children’s knowledge about agriculture: it will familiarize them with different agricultural processes, such as planting, growing and harvesting crops, as well as with various types of crop cultivation technologies. This will contribute to the development of interest in the study of agriculture and, as a result, the growth of students’ motivation to connect their professional activities with agriculture.

‘People-in-environments’ taking the classroom to the community: evaluation of a collaborative problem-based community learning model for social work students

ABSTRACT This paper reports on an innovative community-based ‘People-in-Environments’ learning model designed to support social work students in their development of ‘Readiness for Practice’ in an English urban metropolis. This experiential model challenges students to think differently about the meaning of communities, community resources, and their own assumptions, whilst enhancing critical thinking, self-awareness, reflexivity, and the integration of anti-oppressive values. A central element involves engaging students in embodied, problem-based, confluent learning by critically responding to scenarios using real-life circumstances and visiting diverse community locations. Drawing on qualitative questionnaires, focus groups, and anecdotal data from selected student cohorts 2018–2023, six learning themes are presented: Enhanced Knowledge, Deeper Learning; Examining the Familiar Unfamiliar; What Does Community Mean; Person-In-Environment, Self-In-Environment; A Different Kind of Empathy, and Group Work Challenges. Students’ narratives emphasize the significance of embodied, emplaced learning in unfamiliar settings. Reflexively analyzing thoughts, emotions, and materiality enhanced understanding and skill development. This model facilitates nuanced appreciation of what community means, both the intersections of geographical locales with identities and attachments, and the importance of understanding community attributes, strengths, risks, and resources. The paper concludes with suggestions for creative expansion through community mapping in placements and integrative learning in social work curricula.

The role of biomechanics in enhancing cognitive function and learning outcomes in English language teaching

Language learning, traditionally taught through memory and repetition, often overlooks the role of physical movement in enhancing cognitive and linguistic abilities. Theories like embodied cognition and motor learning suggest that cognitive functions are closely linked to physical actions. Research suggests that posture, gestures, and movement can improve language processing and comprehension. However, the practical integration of biomechanical principles into structured language teaching remains underexplored. Previous studies on gesture-based learning and movement-enhanced vocabulary acquisition indicate potential benefits but lack a comprehensive approach. This study introduces a series of biomechanical interventions in English language teaching, including posture training, movement-based vocabulary learning, sensory-motor integration, and kinesthetic learning techniques. A quasi-experimental design with 115 12–16-year-olds was used for 8 weeks. The experimental group received biomechanical interventions, and the control group received traditional teaching. Cognitive function and language outcomes were assessed pre-and post-assessment. The experimental group showed significant cognitive function improvements, with scores rising from 78.5 ± 5.6 to 89.3 ± 4.8, while the control group showed only a minor change (77.9 ± 5.8 to 80.7 ± 5.5, t = 4.56, p = 0.002, d = 1.78 In the experimental group, vocabulary scores increased from 85.6 ± 7.4 to 92.3 ± 6.2 (t = 3.22, p = 0.008, d = 1.03). Memory improvement correlated strongly with vocabulary acquisition (r = 0.68, p = 0.003). Also, engagement frequency increased (p = 0.004).

Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning model for the morphologic measurement of unruptured intracranial aneurysms (UIAs) based on CT angiography (CTA) data and validate its performance using a multicenter dataset. Materials and Methods In this retrospective study, patients with CTA examinations, including those with and without UIAs, in a tertiary referral hospital from February 2018 to February 2021 were included as the training dataset. Patients with UIAs who underwent CTA at multiple centers between April 2021 to December 2022 were included as the multicenter external testing set. An integrated deep-learning (IDL) model was developed for UIA detection, segmentation and morphologic measurement using an nnU-net algorithm. Model performance was evaluated using the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC), with measurements by senior radiologists serving as the reference standard. The ability of the IDL model to improve performance of junior radiologists in measuring morphologic UIA features was assessed. Results The study included 1182 patients with UIAs and 578 controls without UIAs as the training dataset (55 years [IQR, 47-62], 1,012 [57.5%] females) and 535 patients with UIAs as the multicenter external testing set (57 years [IQR, 50-63], 353 [66.0%] females). The IDL model achieved 97% accuracy in detecting UIAs and achieved a DSC of 0.90 (95%CI, 0.88-0.92) for UIA segmentation. Model-based morphologic measurements showed good agreement with reference standard measurements (all ICCs > 0.85). Within the multicenter external testing set, the IDL model also showed agreement with reference standard measurements (all ICCs > 0.80). Junior radiologists assisted by the IDL model showed significantly improved performance in measuring UIA size (ICC improved from 0.88 [0.80-0.92] to 0.96 [0.92-0.97], P < .001). Conclusion The developed integrated deep learning model using CTA data showed good performance in UIA detection, segmentation and morphologic measurement and may be used to assist less experienced radiologists in morphologic analysis of UIAs. ©RSNA, 2024.

Integrative Learning Strategies for Enhancing Critical Thinking in Islamic Religious Education

This research employs a qualitative approach to explore integrative learning strategies aimed at enhancing critical thinking skills in Islamic Religious Education at the secondary school level in Padang, Indonesia. The study involved four teachers and 120 students from various classes to provide a comprehensive perspective. Given the increasing importance of critical thinking in education, especially in subjects that shape students' moral and ethical foundations, this research emphasizes the crucial role of developing these skills within Islamic Religious Education. Data were collected through direct classroom observations, in-depth interviews with teachers and students to capture their experiences and insights on the strategies used, and a detailed analysis of relevant teaching materials. The findings reveal that group discussions create a dynamic environment where students can exchange ideas, broaden perspectives, and develop critical arguments. Case study analyses enable students to connect religious concepts to real-life situations, reinforcing the practical significance of religious teachings. Problem-solving activities promote logical, systematic, and reflective thinking skills, especially when addressing complex moral and ethical dilemmas. The study also highlights that these strategies not only enhance students' critical thinking but also increase their active engagement in learning, deepen their understanding of religious teachings, and foster a reflective approach toward moral values. This research offers an in-depth understanding of the effectiveness of integrative learning strategies in Islamic Religious Education and underscores their relevance in preparing students to meet the demands of modern education, which prioritizes critical thinking skills.

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

The cascade integration model based on machine learning to predict gestational diabetes

Abstract Machine learning has significant superiority in disease prediction and auxiliary clinical decisions due to its powerful data analysis and exploration capabilities. Making accurate predictions about gestational diabetes mellitus (GDM) with clinical data is not always an easy task. In previous work, GDM was predicted using machine learning models and integrated learning models such as Stacking. This work improves on previous work by developing a new integrated learning model building approach for better exploiting the benefits of machine learning models. Initially, the clinical data set is normalized. Then, according to the principle of removing the redundant features of each machine learning model, the first nine high-importance features of the five single models are filtered respectively. Finally, the GDM Cascade integration prediction model is constructed and compared with the Blending model and Stacking model, it is obvious that the proposed model construction method has superior performance and the AUC value reaches 0.9536.&amp;#xD;

Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population

As the population ages, the increase in the number of middle-aged and older adults with diabetes poses new challenges to the allocation of resources in the healthcare system. Developing accurate diabetes prediction models is a critical public health strategy to improve the efficient use of healthcare resources and ensure timely and effective treatment. In order to improve the identification of diabetes in middle-aged and older patients, a Bagging-RF model is proposed. In the study, two diabetes datasets on Kaggle were first preprocessed, including unique heat coding, outlier removal, and age screening, after which the data were categorized into three age groups, 50–60, 60–70, and 70–80, and balanced using the SMOTE technique. Then, the machine learning classifiers were trained using the Bagging-RF integrated model with eight other machine learning classifiers. Finally, the model’s performance was evaluated by accuracy, F1 score, and other metrics. The results showed that the Bagging-RF model outperformed the other eight machine learning classifiers, exhibiting 97.35%, 95.55%, 95.14% accuracy and 97.35%, 97.35%, 95.14% F1 Score at the Diabetes Prediction Dataset for diabetes prediction for the three age groups of 50–60, 60–70, and 70–80; and 97.03%, 94.90%, 93.70% accuracy and 97.03%, 94.90%, 93.70% F1 Score at the Diabetes Prediction Dataset. 95.55%, 95.13% F1 Score; and 97.03%, 94.90%, 93.70% accuracy; and 97.03%, 94.89%, 93.70% F1 Score at Diabetes Prediction Dataset. In addition, while other integrated learning models, such as ET, RF, Adaboost, and XGB, fail to outperform Bagging-RF, they also show excellent performance.

Fog Computing Enabled Adaptive Prognosis of Cutting Tool Remaining Life through Multi-source Data

Abstract Cutting tool remaining life during machining processes is essential for achieving sustainable production. Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilising multi-source data to address these challenges effectively. The key innovations include: (1) The proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardised data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life, best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite Element Analysis (FEA) is incorporated to validate the model's predictions to validate reliability of deep learning model. (4) The fog computing optimisation mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64% and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in enhancing the operational efficiency and reliability of manufacturing systems is validated through advanced data integration and deep learning techniques.

Comprehensive Approach to Training English Communicative Competence in Chemistry

Abstract The development of foreign language professional communicative competence (FLPCC) is crucial for professionals, considering communicative skills as an integral part of their competence. In Kazakhstan, acquiring a foreign language is a prerequisite for advancing chemistry education, enabling students to access global scientific advancements and exchange knowledge. Content and language-integrated learning (CLIL) stands out as an effective approach for cultivating FLPCC in future chemistry teachers. This study addresses the scarcity of theoretical and practical tools in content-language integrated learning in pedagogical universities. It aims to enhance the training quality for pedagogy bachelor’s degree students studying chemistry in a foreign language. By identifying active teaching forms, methodologies, and approaches in integrated learning, students’ foreign language competence can be successfully developed within the professional domain. A meticulously designed training model and exercises were developed to achieve these goals. The proposed modeling approach was evaluated using the Quasi research method, involving questionnaire data analysis. The results confirm that implementing this approach significantly improves future chemistry professionals’ training quality, enabling confident communication in English within their professional domains and demonstrating well-developed speaking skills. The practical value of this research extends to university educators implementing CLIL practices and researchers studying students’ FLPCC formation.

9.X.2. Activating the ASPHER-WHO Public Health Core Curriculum: Interprofessional Skills

Abstract The updated ASPHER-WHO Public Health Core Curriculum builds on ASPHER’s previous work on Competencies for Public Health Professionals (2006-2020), reflects the changing needs of our population and planet and public health professionals. The key objectives of this workshop are to: - Share the updated ASPHER core curriculum for public health; - Illustrate examples of public health challenges that young professionals have championed for inclusion; - Share the Faculty of Public Health (UK)’s work on Fair training as a case study on operationalising public health leadership on equality, diversity and inclusion and demonstrating the core competencies in action. A curricular content survey of member schools and programmes yielded responses from 60 schools, with &amp;gt; 500 submissions of detailed. subject curricula. These have been compiled into 36 Subject Areas in four Domains, namely: (1) Core Subject Areas in Public Health: (2) Subject-specific Areas in Public Health;(3) Core Cross-curricular Subject Areas; and (4) Core Interdisciplinary Professional Skills in PH. The development process was guided and grounded by the expertise and passion of young public health professionals. They are helping us make the core curriculum fit for purpose for the near future and provide them with the skills required to be a competent member of a supportive public health team in an ever changing world. This Workshop will profile selected Subject Areas from each Domain using real life challenges facing WHO European Region to show how the core curriculum integrates research, theory and practice to help build those essential interdisciplinary skills. The examples will focus on Economics in Public Health (Domain 1); Public Health and Criminal Justice; (Domain 2); Health Literacy and Infodemiology, Mental Health and Wellbeing (Domain 3); and Integrative Learning in Action; Communication Skills; Advocacy, Negotiation; Collaborations and Partnerships; and Leadership, Management and Implementation Science (Domain 4). Discussions for each of the Subject Areas will be led by an expert in the area. We will also illustrate an example of these competencies in action, how they are helping shine a light on the challenges we face in creating that supportive team and environment and how the potential for collaboration helps us to capitalise on the expertise available in some schools and programmes for the benefit of all. Key messages • Learning interdisciplinary skills are essential for effective public health analysis, advocacy and action. • Collaboration across countries, schools, professions and generations can create transformational change. Speakers/Panelists Mary Codd University College Dublin, Dublin, Ireland Karl F Conyard University College Dublin, Dublin, Ireland Jenny Houston University of Edinburgh, Edinburgh, UK Samia Latif Faculty of Public Health, London, UK Olalekan Popoola ASPHER, UCD, Dublin, Ireland

Integrative machine learning model for predicting patient-oriented composite endpoints in acute myocardial infarction: a neuroendocrine biomarker approach

Abstract Background Acute Myocardial Infarction (AMI) represents a significant clinical challenge with diverse outcomes. Predicting patient-oriented composite endpoints (POCE) (all-cause death, any stroke, any AMI, or any revascularization) can significantly enhance post-AMI care. Incorporating clinical data with neuroendocrine biomarkers may offer improved prognostic capabilities. Objective To develop and validate a machine learning model to predict POCE in AMI patients, utilizing clinical parameters and neuroendocrine biomarkers. Methods There was prospective, observational, single-center study. Three approaches have been used to obtain the predictions for POCE event. First, as the dataset was imbalanced, an adaptive synthetic (ADASYN) sampling approach was used to generate synthetic instances, particularly focusing on those that are difficult-to-learn by using a weighted distribution. Then, random forest classifier (RF) was used to build a machine learning model to predict POCE. The model was tuned via a grid-search algorithm for optimal hyperparameters and validated using a 10-fold stratified cross-validation. Finally, the feature importance was determined by Shapley Additives, which measure the average marginal contribution of a feature value across all potential feature combinations. For the comparative purpose, the Gini index, which also shows the feature importance but in terms of mean decrease in impurity, was calculated. Results The study incorporated data from 315 patients, examining 47 variables and identifying 72 instances of POCE. After applying the ADASYN algorithm, the class distribution within the dataset was effectively equalized, facilitating the training of a robust RF model. Upon training with 252 instances, the model distinguished POCE with an accuracy of 83.8%, demonstrating a sensitivity of 80% and a specificity of 86%. During 10-fold cross-validation, the model's accuracy slightly dipped to 75%, with a sensitivity of 56% and specificity of 82%, indicating a balance between generalizability and overfitting. Testing mirrored these results, underscoring the model’s consistent performance. Notably, SHAP value analysis highlighted C-Reactive Protein (CRP) and alanine transaminase (ALT) as the most influential features, underscoring their significance in the context of AMI (Figure 1-2). The predictive power of each feature was further demonstrated by the mean decrease in Gini index (Figure 1-2). Conclusion Our study highlights the feasibility of employing machine learning to predict POCE post-AMI using an integrative dataset of clinical and neuroendocrine markers. The predictive model has the potential to revolutionize post-AMI care by allowing clinicians to identify high-risk patients early, tailor interventions, and allocate resources efficiently. Future research could explore the integration of this model into clinical workflows and its impact on patient outcomes.

Development of RME-based Teaching Modules to Improve Problem-Solving Skills and Mastery of IPAS Concepts for Junior High School Students

This research aims to develop a teaching module based on RME that is valid, practical, and effective for class VII SMP, with a focus on improving the ability to solve problems with mathematical students. We developed the modules by considering the local context and culture, adhering to RME measures, and emphasizing the use of relevant and conceivable situations for students. We utilized the Plomp development model, which encompasses the stages of investigation, creation, and evaluation. We tested the prototype and assessment of this teaching module on participants enrolled in class VII at SMP N 1 Ranah Batahan. The research results demonstrate that the developed teaching modules have high validity (89.33%), practicality (89.60%), and proven effectiveness, as evidenced by an average score of 85.50% among the test study students. In addition to enhancing the mathematical skills of students, the RME approach also enhances the study of IPAS (Natural and Social Sciences). The development of problem-solving and critical thinking skills through this module enhances students' understanding of natural and social phenomena. Therefore, this teaching module not only promotes mathematical achievement but also fosters integrative and holistic learning within the framework of IPAS.

The effects of hands-on content-language integrated learning on fourth graders’ acquisition of target vocabulary and procedural knowledge in Taiwan

Despite the widespread use of multimodal pedagogies in Content and Language Integrated Learning (CLIL) contexts, empirical studies specifically focusing on the impact of hands-on learning on target vocabulary and procedural knowledge acquisition in CLIL Social Studies, particularly in Taiwan, are relatively uncommon. Grounded in embodied learning theories, this study investigated the comparative impact of hands-on learning versus worksheet-based instruction on fourth graders’ target vocabulary acquisition and procedural knowledge within a CLIL Social Studies setting in Taiwan. The study also explored students’ perceptions of their learning experiences with these instructional methods. This quasi-experimental study involved four intact fourth-grade CLIL Social Studies classes from three elementary schools in Taiwan, comprising 40 students in the Experimental Group (EG), who engaged in hands-on learning, and 34 students in the Control Group (CG), who engaged in worksheet-based learning. Activities included Chinese dumpling making, bird’s nest building, and designing a pot of succulents. Data were collected through pre- and post-tests to assess target vocabulary acquisition and procedural knowledge, followed by learner experience surveys to evaluate students’ perceptions of their learning experiences. The analysis included paired-samples t-tests, independent-samples t-tests, and thematic analysis of open-ended survey responses. Results revealed that the EG significantly outperformed the CG in both target vocabulary acquisition and procedural knowledge across all three activities, with large effect sizes (d = 1.05–1.51 for target vocabulary and 0.95–2.89 for procedural knowledge), demonstrating the effectiveness of hands-on learning. However, while significant differences were observed in memory retention of target vocabulary and procedural knowledge from the survey results, no significant differences were found between the EG and CG in terms of perceived ease of learning. These findings suggest that hands-on learning offers substantial benefits over traditional worksheet-based instruction in CLIL settings, though the transition to hands-on methods may require additional support. Practical implications include the strategic selection of relevant vocabulary, the implementation of structured hands-on approaches, and the emphasis on behavioral reinforcement to enhance learning outcomes.

An Investigation into the Susceptibility to Landslides Using Integrated Learning and Bayesian Optimization: A Case Study of Xichang City

In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy.

Teacher education in Poland (1970–2024): policy development, legislation, and period-specific challenges

ABSTRACT This paper aims to provide a comprehensive analysis of the evolution of teacher education in Poland from 1970 to 2024, detailing socio-political and economic changes that have shaped educational policies. The study uses a historical and policy analysis approach, examining legislative documents, policy developments, and educational outcomes across three distinct periods – Socialist Era (1970–1989), Transition and Integration Era (1990–2004), and Modernisation and Reform Era (2005–2024). During the Socialist Era, centralised control and ideological rigidity resulted in a uniform system. Policy changes aimed to align education with socialist principles, often at the expense of flexibility and creativity. The Transition and Integration Era saw significant reforms driven by Poland’s move towards a market economy and European Union integration. This period focused on decentralising the education system, improving teacher qualifications, and aligning with European standards, though it faced challenges such as resource disparities and resistance to change. In the Modernisation and Reform Era, efforts have focused on improving teacher education quality through modernisation and adopting international best practices. There has been an emphasis on innovation, technology integration, and lifelong learning. Despite progress, issues like equitable resource distribution and the relevance of training programmes persist. The paper concludes with recommendations for further decentralisation, better resource allocation, continuous professional development, and integrating innovative teaching methods. These recommendations aim to guide future policy and enhance the effectiveness of teacher education in Poland. Implications for research include exploring the impact of these reforms on teacher effectiveness, policy adaptation, and practice improvement in education.

Medical students’ perceptions of live versus recorded pathology lectures at a South African medical school during the Covid-19 pandemic: unique lessons for undergraduate pathology education in a diverse student population

Abstract Introduction/Objective The morphological depiction and interactive learning vital to the core discipline of anatomical pathology posed specific challenges during emergency remote teaching (ERT) in the COVID 19 pandemic. Using voice-over PowerPoint presentations (VoPPPs) in anatomical pathology at Wits University in Johannesburg, South Africa during this time provided a unique opportunity to determine students’ perceptions of this teaching and learning modality. Objective To determine a South African medical students’ perceptions of live lectures vs online learning with VoPPPs during the Covid-19 pandemic. Methods/Case Report A descriptive, cross-sectional, online survey was sent to 326 4th year medical students in 2020. The questionnaire consisted of closed- and open-ended questions. Descriptive and inferential statistical tests were conducted on the quantitative data. Content analysis was performed on the open-ended responses. Results (if a Case Study enter NA) The survey response rate was 56.75%. Respondents preferred the traditional lecture format to the exclusive use of VoPPPs (48.6% to 34.6%) for pathology learning. Respondents appreciated the flexibility of learning from VoPPPs, resulting in blended learning being the preferred approach (45.9% vs 44.3 for live lectures only and 9.7% for VoPPPs only, respectively). Respondents indicated a need for more effective use of VoPPPs regarding the depiction of pathology images, more opportunities for feedback, and more online learning modalities in addition to VoPPPs. Some respondents from rural areas stated that their home study environments were not conducive to online learning modalities. Conclusion Blended learning combines the interactivity possible in contact sessions with the time benefits of online learning for undergraduate pathology education. However, educators should use various online methods rather than relying solely on VoPPPs. This is especially important when considering the visual nature of pathology lectures. Where VoPPPs are used, they should be used effectively, mainly using good quality, labelled images to depict morphology. The preference for blended learning offers exciting possibilities for developing integrated learning platforms in the future, however, these must be tailored to suit the needs of students from economically and ethnically diverse backgrounds.

Soil Salinity Inversion Based on a Stacking Integrated Learning Algorithm

Soil salinization is an essential risk factor for agricultural development and food security, and obtaining regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of soil salinity inversion, this study focuses on the Manas River Basin oasis area, the largest oasis farming area in Xinjiang, as the study area and proposes a new soil salinity inversion model based on stacked integrated learning algorithms. Firstly, we selected four machine learning regression models, namely, random forest (RF), back propagation neural network, support vector regression, and convolutional neural network, for performance evaluation. Based on the model performance, we selected the more effective RF and BPNN as the basic regression models and further constructed a stacking integrated learning model. This stacking integration learning model improved the prediction accuracy by training a secondary model to fuse the prediction results of these two basic models as new features. We compared and analyzed the stacking integrated learning model with four single machine learning regression models. Findings indicated that the stacking integrated learning regression model fitted better and had good stability; on the test set, the stacking integrated learning regression model showed a relative increase of 8.2% in R2, a relative decrease of 14.0% in RMSE, and a relative increase of 6.5% in RPD when compared to the RF model, which was the single most effective machine learning regression model, and the stacking model was able to achieve soil salinity inversion more accurately. The soil salinity in the oasis areas of the Manas River Basin tended to decrease from north to south from 2016 to 2020 from a spatial point of view, and it was reduced in April from a temporal point of view. The percentage of pixels with a high soil salinity content of 2.75–2.80 g kg−1 in the study area had decreased by 19.6% in April 2020 compared to April 2016. The innovatively constructed stacking integrated learning regression model improved the accuracy of soil salinity estimation on the basis of the superior results obtained in the training of the single optimal machine learning regression model. As a consequence, this model can provide technological backup for fast monitoring and inversion of soil salinity as well as prevention and containment of salinization.