Research on Compatible Mode of Self-directed Learning and Cooperative Learning in Blended Teaching of Basic English Course

In the information age,blended teaching,no matter online or offline,has become the mainstream of college teaching reform.In this teaching model,self-directed learning and cooperative learning are the two main learning approaches.On the online teaching platform,students mainly learn knowledge-based content by self-directed learning,while practising their language skills by cooperative learning in flipped classroom activities.On one hand,it advocates student-centered strategy so as to improve students autonomous learning ability;on the other hand,teachers serve as a guide to organize the classroom activities;meanwhile,they give timely feedback to students in order to promote students’learning ability.In blended teaching model,this mutually compatible and reinforcing model of self-directed learning and cooperative learning is undoubtedly helpful to improve the teaching efficiency.

Relationship between self-directed learning readiness, learning attitude, and self-efficacy of nursing undergraduates

Objective: The purposes of this study were to analyze the influencing factors of self-directed learning readiness(SDLR) of nursing undergraduates and explore the impacts of learning attitude and self-efficacy on nursing undergraduates.Methods: A total of 500 nursing undergraduates were investigated in Tianjin, with the Chinese version of SDLR scale, learning attitude questionnaire of nursing college students, academic self-efficacy scale, and the general information questionnaire.Result: The score of SDLR was 149.99±15.73. Multiple stepwise regressions indicated that academic self-efficacy, learning attitude, attitudes to major of nursing, and level of learning difficulties were major influential factors and explained 48.1% of the variance in SDLR of nursing interns.Conclusions: The score of SDLR of nursing undergraduates is not promising. It is imperative to correct students' learning attitude, improve self-efficacy, and adopt appropriate teaching model to improve SDLR.

Effectiveness of the flipped classroom on the development of self-directed learning in nursing education:a meta-analysis

Objectives: To examine the best practice evidence of the effectiveness of the flipped classroom（FC） as a burgeoning teaching model on the development of self-directed learning in nursing education.Data sources: The relevant randomized controlled trial（RCT） and non-RCT comparative studies were searched from multiple electronic databases including PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature（CINAHL）, Cochrane Central Register of Controlled Trials（CENTRAL）, Wanfang Data, China National Knowledge Infrastructure（CNKI）, and Chinese Science and Technology Periodical Database（VIP） from inception to June 2017.Review methods: The data were independently assessed and extracted for eligibility by two reviewers. The quality of included studies was assessed by another two reviewers using a standardized form and evaluated by using the Cochrane Collaboration’s risk of bias tool. The self-directed learning scores（continuous outcomes） were analyzed by using the 95% confidence intervals（Cls） with the standard deviation average（SMD） or weighted mean difference（WMD）. The heterogeneity was assessed using Cochran’s I;statistic.Results: A total of 12 studies, which encompassed 1440 nursing students（intervention group = 685, control group = 755）, were eligible for inclusion in this review. Of 12 included studies, the quality level of one included study was A and of the others was B. The pooled effect size showed that compared with traditional teaching models, the FC could improve nursing students’ selfdirected learning skill, as measured by the Self-Directed Learning Readiness Scale（SDLRS）, Self-Directed Learning Readiness Scale for Nursing Education（SDLRSNE）, Self-Regulated Learning Scale（SRL）, Autonomous Learning Competencies scale（ALC）, and Competencies of Autonomous Learning of Nursing Students（CALNS）. Overall scores and subgroup analyses with the SRL were all in favor of the FC.Conclusions: The result of this meta-analysis indicated that FCs could improve the effect of self-directed learning in nursing education.Future studies with more RCTs using the same measurement tools are needed to draw more authoritative conclusions.

Self-directed learning readiness and nursing competency among undergraduate nursing students in Fujian province of China

Aims:We examined the relationship between self-directed learning readiness(SDLR)and nursing competency among undergraduate nursing students.Background:There is little evidence-based data related to the relationship between selfdirected learning(SDL)and nursing competency.Methods:A descriptive correlational design was used.We conducted convenience sampling of 519 undergraduate nursing students from three universities during their final period of clinical practice.We investigated SDL according to the SDLR scale for nursing education(Chinese translation version),and used the Competency Inventory for Registered Nurses to evaluate nursing competency.Results:The mean SDLR score of the students was 148.55(standard deviation[SD]18.46),indicating intermediate and higher SDLR.The mean score for nursing competency was 142.31(SD30.39),indicating intermediate nursing competence.SDLR had a significant positive and strong relationship with nursing competency.Conclusion:SDLR is a predictor of nursing competency.

Role of academic self-efficacy in the relationship between self-directed learning readiness and problem-solving ability among nursing students

Objective: Problem-solving should be a fundamental component of nursing education because It is a core ability for professional nurses. For more effective learning, nursing students must understand the relationship between self-directed learning readiness and problem-solving ability. The aim of this study was to investigate the relationships among self-directed learning readiness, problemsolving ability, and academic self-efficacy among undergraduate nursing students.Methods: From November to December 2016, research was conducted among 500 nursing undergraduate students in Tianjin, China,using a self-directed learning readiness scale, an academic self-efficacy scale, a questionnaire related to problem-solving, and selfdesigned demographics. The response rate was 85.8%.Results: For Chinese nursing students, self-directed learning readiness and academic self-efficacy reached a medium-to-high level,while problem-solving abilities were at a low level. There were significant positive correlations among the students' self-directed learning readiness, academic self-efficacy, and problem-solving ability. Furthermore, academic self-efficacy demonstrated a mediating effect on the relationship between the students' self-directed learning readiness and problem-solving ability.Conclusions: To enhance students' problem-solving ability, nursing educators should pay more attention to the positive impact of self-directed learning readiness and self-efficacy in nursing students' education.

Self-directed learning readiness and social problem solving of nursing students in Macao

Objective The aims of this study were to describe nursing students′self-directed learning readiness and social problem solving and test their correlations in Macao.Methods This descriptive cross-sectional study was conducted on 140baccalaureate nursing students.A stratified random sampling was performed.The Self-directed Learning Readiness(SDLR)Scale and Chinese Social Problem-Solving Inventory-Revised(C-SPSI-R)were used.Results The response rate was 79.3%.Students possessed readiness for self-directed learning(mean 149.09±12.53,51.4%at high level,48.6%at low level).Regarding to social problem solving,the mean scores of each subscale were 9.35±3.25(Rational Problem Solving,RPS),10.26±3.23(Positive Problem Orientation,PPO),8.14±4.06(Negative Problem Orientation,NPO),5.67±4.44(Avoidance Style,AS),and 4.84±3.03(Impulsivity/Carelessness Style,ICS).SDLR was positively related to RPS and PPO,but was negatively related to AS.Conclusion Half of students possessed stronger readiness for self-directed learning.Students had a belief in the ability to solve problems,and adopted relevant strategies in solving problems.However,students still had negative and dysfunctional orientation and defective attempts in solving problems.Self-directed learning was positively related to positive and constructive orientation,but was negatively related to defective problem-solving pattern.Nurse educators should create educational climates for promoting student confidence and mutual responsibility for learning and their thinking process for problem solving.

The effect of self-observation on the self-directed learning ability of nursing students: An experimental study

Background: Patients expect nurses to be both technically competent and psychosocially skilled. Enhancing the quality of patient care and patient safety in healthcare settings has increased, resulting in limited opportunities for students to practice clinical skills in healthcare settings. Achieving competence in these skills is viewed as an essential task to be completed during the school curriculum. Objective: The purpose of this study was to evaluate the use of self-observation through cellular recordings as an adjunct to the clinical skills teaching of a blood sugar test to undergraduate nursing. Design: The research design consisted of pre- and post-test consecutive experimental design through a control group. Settings: This study targeted nursing students enrolled in baccalaureate programs running in Korea. Participant: The participants were 64 students including 34 for the experimental group and 30 for the control group. Methods: Those in the control group received standard teaching methods using lectures and skills classes and facilitated the use of self-study methods. Those in the experimental group received standard teaching using lectures and skills classes and facilitated use of cell phone recorded self-observation. The self-confidence of practicing a blood sugar test, satisfaction with the learning method, self-study participation, level of interest in nursing practice, and self-directed learning ability were measured using questionnaires. Results: Significant between-groups differences were detected in self-confidence of practicing a blood sugar test (t = 2.067, p = 0.043), satisfaction with the learning method (t = 2.818, p = 0.044), self-study participation (χ2 = 7.635, p = 0.022), and average self-directed learning ability (t = 3.202, p = 0.002). Conclusions: Self-observation through cellular phone recordings is an effective learning method as an adjunct to teach clinical skills.

Impact of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students

Objective: To explore the effects of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students. Methods: A convenience sampling of 460 undergraduate nursing students was surveyed in Tianjin, China. Students who participated in the study completed a questionnaire that included social demographic questionnaire, Self-directed Learning Readiness Scale, Attitude to Learning Scale, and Social Problem-Solving Inventory. Pearson’s correlation analysis was performed to test the correlations among problem-solving ability, self-directed learning readiness, and learning attitude. Hierarchical linear regression analyses were performed to explore the mediating role of learning attitude. Results: The results showed that learning attitude (r=0.338, P<0.01) and self-directed learning readiness (r=0.493, P<0.01) were positively correlated with problem-solving ability. Learning attitude played a partial intermediary role between self-directed learning readiness and problem-solving ability (F=74.227, P<0.01). Conclusions: It is concluded that nursing educators should pay attention on students’ individual differences and take proper actions to inspire students’ self-directed learning readiness and learning attitude.

Nursing Students’ Readiness for Self-Directed Learning and Its Effect on Learning Outcome in South-West Nigeria

Self-directed learning (SDL) uses diverse learning resources to solve identified problems in learning. Nursing is a lifelong learning profession and SDL is a valuable skill to remain relevant and productive professionals. Nursing students are expected to embrace SDL and develop these skills. However, there has been no evidence of this innovative process in South-West Nigeria. This study seeks to evaluate nursing students’ readiness for SDL and its effect on learning outcome. This quasi-experimental study purposively utilized 229 nursing students as participants. Baseline (P1) data was collected using Gugliemino’s SDL readiness scale (SDLRS) and a validated-structured questionnaire. Participants had a pre-test to assess knowledge at P1 followed by 6 weeks interaction using SDL on selected topics in Medical-surgical nursing and the same test at post-intervention (P2). Using a 50-point scale, knowledge was categorized as good ≥ 25 and poor < 25 and SDLRS on a 290-point scale was categorized as below average 5 - 201, average 202 - 226 and above average 227 - 290. Descriptive statistics, Chi-square test, t-test and linear regression analysis were used for analysis at p = 0.05. Nursing students’ SDLRS was average;mean = 203 ± 23.0. A significant difference exists between nursing students with good knowledge at P1 and P2. At P1, 39.2% had good knowledge, mean = 22.2 ± 6.3, and 90.1% at P2, mean = 30.6 ± 5.4, p < 0.05 also a significant relationship exist between SDLR and learning outcome at P2;p < 0.05. With the nursing students’ average SDL readiness level having a significant effect on learning outcome. Nursing training institutions should provide necessary resources to embrace SDL as a main-line teaching method to ensure competent life-long professionals.

Psychological Capital and Self-directed Learning Abilities of Graduate School Candidates

In China,about 4.74 million Chinese have signed up for the 2023 national exam for postgraduate enrollment.More and more students will pursue a graduate school education.It’s important to note that the self-directed learning abilities of the students is crucial in the postgraduate entrance exam.Therefore,the study seeks to identify the level of the self-directed learning abilities and psychological capital of the postgraduate school candidates to identify whether there is a significant correlation between the candidates’self-directed learning abilities and psychological capital.

Psychological Capital and Self-directed Learning Abilities of Graduate School Candidates

In China,about 4.74 million Chinese have signed up for the 2023 national exam for postgraduate enrollment.More and more students will pursue a graduate school education.It’s important to note that the self-directed learning abilities of the students is crucial in the postgraduate entrance exam.Therefore,the study seeks to identify the level of the self-directed learning abilities and psychological capital of the postgraduate school candidates to identify whether there is a significant correlation between the candidates’self-directed learning abilities and psychological capital.

Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

SensFL:Privacy-Preserving Vertical Federated Learning with Sensitive Regularization

In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.

Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.