Achieve Intended Learning Outcomes and Improving Digital Literacy Skills for Practical-Based Subjects Using Online Teaching via Propagation of OER Materials

As professors are subjected to teaching their classes online due to the recent COVID-19, our local Hong Kong students find it difficult to consult their teachers, and ultimately would fail to achieve the intended learning outcomes, especially for practical-based subjects. In this research, students having online classes of a practical-based fabric design subject were encouraged to self-study from Open Educational Resource (OER) materials for a further and better understanding of their subject. Additionally, online materials were developed to improve students’ understanding via skill of digital literacy. Their learning progress was evaluated and compared to the face-to-face version. The majority of students found online classes combined with self-studying OER materials, potentially be a substitute for face-to-face classes. Most of the students further opined different OER videos assisted them without any face-to-face instructions in practical works, to develop new fabric samples from the inspiration. Analysis of test results, and comparison of students’ final grades with different learning modes, supported these phenomena.

A Meta-analysis of the Effect of the Sandwich Teaching Model on the Learning Outcomes of Nursing Students

Objective:To evaluate the effect of the sandwich teaching model on the learning effect of nursing students.Methods:The Chinese and English databases of CNKI,WanFang,Vip,superstar,and PubMed were searched by computer,and the data were analyzed by Rev Man 5.3 software after literature quality evaluation.Results:Meta-analysis showed that the theoretical and operational performance of the nursing students in the sandwich teaching method was better than that of the traditional teaching group.Conclusion:The sandwich approach was superior to the traditional teaching method in the learning outcomes of nursing students.

Do Learners' Preferred Learning Styles Affect Learning Outcomes and Satisfaction in PLE: A Pilot Study of the Supervised-PLE-IELTS Platform

Personal Learning Environment(PLE)enables a knowledge-based,learner-centered lifelong learning which could be successfully integrated with formal education when taking educators’supervision into consideration.But do learners’preferred Learning Styles matter in PLE?To investigate the relationship among learners’Learning Styles,learning outcomes and satisfaction towards the PLE platform,the study constructed and applied a supervised-PLE-IELTS platform.57 sophomores majored in Business in Wenzhou University took part in a 16-week project.Data were collected by Honey and Mumford’s Learning Styles questionnaire for the Learning Styles,post-test of IELTS reading,listening and vocabulary for the cognitive learning outcomes,and Distance Education Learning Environments Survey(DELES)for the satisfaction towards the PLE platform.The results showed:(1)Learning Styles have positive relationship with the cognitive learning achievements in PLE;(2)Learning Styles had no direct effect on satisfaction,and learners of all Learning Styles enjoyed PLE-IELTS platform;and(3)learners who spent more time on PLE platform achieved better cognitive learning outcomes.The paper shed light on the future construction of supervised-PLEs.

Beyond Tech:The Impact of Hybrid Classroom Climate on the Learning Outcomes of University Students

In the context of the highly demanding trend of equitable and quality education,digital technology has outlined a blueprint for sharing resources across spatial and temporal boundaries and learner differences.The hybrid classroom implemented on university campuses during the pandemic demonstrates the tremendous shaping power of digital technology on teaching and learning across different groups,time,and space.This study investigates two types of learners,Clone Classroom and Global Hybrid Classroom at Tsinghua University,and finds that intrapersonal,interpersonal,and institutional dimensions of classroom climate all significantly affect learners’learning outcomes.However,the influences at the three dimensions differ in degree,with interpersonal factors outweighing institutional factors and institutional factors outweighing individual factors.Furthermore,in individual factors,information literacy and tech-assisted support in institutional factors have the weakest impact on learning outcomes;institutional factors mediate individual and interpersonal factors influencing higher-order cognition development.To avoid the pitfalls of techno-centrism,this study suggests promoting an insight of technology for humanity and embedding technology into teaching to better empower teacher development and student learning experience.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification

There is a drastic increase experienced in the production of vehicles in recent years across the globe.In this scenario,vehicle classification system plays a vital part in designing Intelligent Transportation Systems(ITS)for automatic highway toll collection,autonomous driving,and traffic management.Recently,computer vision and pattern recognition models are useful in designing effective vehicle classification systems.But these models are trained using a small number of hand-engineered features derived fromsmall datasets.So,such models cannot be applied for real-time road traffic conditions.Recent developments in Deep Learning(DL)-enabled vehicle classification models are highly helpful in resolving the issues that exist in traditional models.In this background,the current study develops a Lightning Search Algorithm with Deep Transfer Learning-based Vehicle Classification Model for ITS,named LSADTL-VCITS model.The key objective of the presented LSADTL-VCITS model is to automatically detect and classify the types of vehicles.To accomplish this,the presented LSADTL-VCITS model initially employs You Only Look Once(YOLO)-v5 object detector with Capsule Network(CapsNet)as baseline model.In addition,the proposed LSADTL-VCITS model applies LSA with Multilayer Perceptron(MLP)for detection and classification of the vehicles.The performance of the proposed LSADTL-VCITS model was experimentally validated using benchmark dataset and the outcomes were examined under several measures.The experimental outcomes established the superiority of the proposed LSADTL-VCITS model compared to existing approaches.

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal system.The understandings obtained from the data can assist municipal authorities handle assets and services effectually.At the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic.Besides,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability.Few of the commonly available wastes are paper,paper boxes,food,glass,etc.In order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and trash.Due to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of wastes.In this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart cities.The goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL techniques.The IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object classification.In addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a classifier.Moreover,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet model.In order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques with maximal accuracy of 0.993.

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

Object Detection is the task of localization and classification of objects in a video or image.In recent times,because of its widespread applications,it has obtained more importance.In the modern world,waste pollution is one significant environmental problem.The prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher efficiency.Waste object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or images.This technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental pollution.Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data andmetrics.Therefore,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)algorithm.The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects.In the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object classification.For object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone network.In addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are used.Finally,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste objects.The performance validation of the EFFDL-WODC approach was validated on the benchmark database.The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

Learning outcomes of nursing curriculum in Turkey:a cross-sectional study

Objective:To assess the nursing curriculum and point out learning outcomes in Turkey.Methods:A cross-sectional design was used in this study.This study was conducted between May and June 2017 from 23 undergraduate nursing schools'education programs for one education and academic year's curriculum.The public information of the universities collected from their web sites and learning outcomes of the schools were classified as cognitive,psychomotor,and affective domains.Results:It appears that half of the basic nursing courses are in the psychomotor domain,and the majority of basic medical sciences courses are in the cognitive domain.Learning outcomes about the affective domain mostly take place in basic nursing courses.Conclusions:The findings of the results of this study can provide insight into current nursing education and guide new curricula to be developed.

Effective Face-to-faceTutorials of Online English Learning Lie in Interaction of Affective Domain and Higher Levels of Cognitive Domain of Bloom＇s Taxonomy

Online English learning as an outcome of the rapid development of the Internet has got a wider and wider market in China. However, problems of varieties have also occurred along its way. People never stop thinking of better strategies either in designing online course wares or tutorials to help smooth the learning process. My experience as a tutor is that interaction of affective domain and higher levels of cognitive domain of Bloom＇s Taxonomy plays an important role in face-to-face tutorials of online English learning.

A Review of Researches on Deep Learning in Remote Sensing Application

In recent years, deep learning has been widely used in the field of image understanding and made breakthroughs research progress in image understanding. Because remote sensing application and image understanding are inseparable, researchers have carried out a lot of research on the application of deep learning in remote sensing field, and extended the deep learning method to various application fields of remote sensing. This paper summarizes the basic principles of deep learning and its research progress and typical applications in remote sensing, introduces the current main deep learning model and its development history, focuses on the analysis and elaboration of the research status of deep learning in remote sensing image classification, object detection and change detection, and on this basis, summarizes the typical applications and their application effects. Finally, according to the current application of deep learning in remote sensing, the main problems and future development directions are summarized.

Fruits and Vegetables Freshness Categorization Using Deep Learning

The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only.We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories:purefresh,medium-fresh,and rotten.We gathered a dataset comprising of 60K images of 11 fruits and vegetables,each is further divided into three categories of freshness,using hand-held cameras.The recognition and categorization of fruits and vegetables are performed through two deep learning models:Visual Geometry Group(VGG-16)and You Only Look Once(YOLO),and their results are compared.VGG-16 classifies fruits and vegetables and categorizes their freshness,while YOLO also localizes them within the image.Furthermore,we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree.A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset.The dataset is publicly available for further evaluation by the research community.

Classifying 3 Moss Species by Deep Learning, Using the “Chopped Picture” Method

Especially in recent years, deep learning has become a very effective tool for object identification. However, in general, the automatic object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. The technique called chopped picture method, where teacher images are systematically dissected into numerous small squares. As a result, the model correctly classified 3 moss species and “non-moss” objects in test images with accuracy more than 90%. Using this approach will help progress in computer vision studies for various ambiguous objects.

基于e-Learning的测绘专业教学训练系统的设计与实现

介绍了e-Learning的概念及体系结构,结合测绘专业教育的实际需求,在e-Learning三层体系结构的基础上,给出e-Learning环境下测绘专业教学训练的系统设计及实现方案。

The Role of Teacher Professional Development in Improving Teaching Quality and Student Learning Outcomes

This study investigates the impact of Teacher Professional Development(TPD)on teaching quality and student learning outcomes.Utilizing a mixed-methods approach,the research draws on quantitative data from standardized test scores and qualitative insights from teacher interviews and classroom observations.The findings indicate a strong positive correlation between TPD,as measured by self-assessment and peer evaluation,and improvements in teaching practices.These enhancements in teaching quality are further shown to have a significant impact on student learning outcomes,including academic achievement,critical thinking,and engagement.The study also presents case studies that highlight the transformative potential of TPD in various educational contexts.Limitations include the generalizability of the sample and the correlational nature of the study design.The research concludes with policy implications,emphasizing the need for increased investment in TPD,customization of TPD programs,and the promotion of a collaborative culture within schools.Future research directions are suggested to further explore the long-term effects of TPD and the most effective models for professional development.

Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

Using the Technology Acceptance Model to Analyze the Learning Outcome of Open Education Resources

Along with the development of information and communications technology,open educational resources were widely applied in training usage.The use of these resources facilitates the access to knowledge by enabling learners to transcend time and space.In this way,learners are able to obtain new knowledge more actively and efficiently than before.Using Technology Acceptance Model(TAM)as the theoretical foundation,this study aims to explore the learning outcome of using open educational resources with the perceived convenience as the external variable.In this study,the open educational resources were defined as online courses on the Open Course Ware(OCW)and Massive Open Online Courses(MOOCs),on which the learners choose courses themselves and study without the impact from people,matters,time,space,and things with the help of the Internet.To achieve the objectives of the study,the researchers conducted a survey with the participants who had already used the open educational resources.In total,124 valid samples were collected.The Partial Least Squares(PLS)statistical method was used to carry out the analysis.Overall,the model of this study has good prediction and explanatory power.After the data analysis,the study found that the perceived convenience exerts a positive impact on the use of the open educational resources.In addition,among the four TAM variables,the perceived usefulness does not exert a significant impact on the behavioral intention to use,but the other three TAM variables all have a significant impact on the behavioral intention.

Diagnosing Student Learning Problems in Object Oriented Programming

Students often face difficulties while taking basic programming courses due to several factors. In response, research has presented subjective assessments for diagnosing learning problems to improve the teaching of programming in higher education. In this paper, the authors propose an Object Oriented conceptual map model and organize this approach into three levels： constructing a Concept Effect Propagation Table, constructing Test Item-Concept Relationships and diagnosing Student Learning Problems with Matrix Composition. The authors＇ work is a modification of the approaches of Chert and Bai as well as Chu et al., as the authors use statistical methods, rather than fuzzy sets, for the authors＇ analysis. This paper includes a statistical summary, which has been tested on a small sample of students in King Abdulaziz University, Jeddah, Saudi Arabia, illustrating the learning problems in an Object Oriented course. The experimental results have demonstrated that this approach might aid learning and teaching in an effective way.