Making the case for the evolving role of social media in health professions education and a literature review on the application of some in ophthalmology

Background and Objective:Social media(SoMe)has emerged as a tool in health professions education(HPE),particularly amidst the challenges posed by the coronavirus disease 2019(COVID-19)pandemic.Despite the academia’s initial skepticism SoMe has been gaining traction in supporting learning communities,and offering opportunities for innovation in HPE.Our study aims to explore the integration of SoMe in HPE.Four key components were outlined as necessary for a successful integration,and include designing learning experiences,defining educator roles,selecting appropriate platforms,and establishing educational objectives.Methods:This article stemmed from the online Teaching Skills Series module on SoMe in education from the Ophthalmology Foundation,and drew upon evidence supporting learning theories relevant to SoMe integration and models of education.Additionally,we conducted a literature review considering Englishlanguage articles on the application of SoMe in ophthalmology from PubMed over the past decade.Key Content and Findings:Early adopters of SoMe platforms in HPE have leveraged these tools to enhance learning experiences through interaction,dialogue,content sharing,and active learning strategies.By integrating SoMe into educational programs,both online and in-person,educators can overcome time and geographical constraints,fostering more diverse and inclusive learning communities.Careful consideration is,however,necessary to address potential limitations within HPE.Conclusions:This article lays groundwork for expanding SoMe integration in HPE design,emphasizing the supportive scaffold of various learning theories,and the need of furthering robust research on examining its advantages over traditional educational formats.Our literature review underscores an ongoing multifaceted,random application of SoMe platforms in ophthalmology education.We advocate for an effective incorporation of SoMe in HPE education,with the need to comply with good educational practice.

A Review of Artificial Intelligence in K-12 Education

Artificial intelligence (AI) has garnered significant interest within the educational domain over the past decade, promising to revolutionise teaching and learning. This paper provides a comprehensive overview of systematic reviews conducted from 2010 to 2023 on the implementation of AI in K-12 education. By synthesising findings from ten selected systematic reviews, this study explores the multifaceted opportunities and challenges posed by AI in education. The analysis reveals several key findings: AI’s potential to personalise learning, enhance student motivation, and improve teaching efficiency are highlighted as major strengths. However, the study also identifies critical concerns, including teacher resistance, high implementation costs, ethical considerations, and the need for extensive teacher training. These findings represent the most significant insights from the analysis, while additional findings further underscore the complexity and scope of AI integration in educational settings. The study employs a SWOT analysis to summarise these insights, identifying key areas for future research and policy development. This review aims to guide educators, policymakers, and researchers in effectively leveraging AI to enhance educational outcomes while addressing its inherent challenges.

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

An Improved Spatially Aware Routing Algorithm for Mobile Ad Hoc Network in Inter-Vehicle Communication

A new algorithm called spatially aware routing algorithm with enhanced learning (SAREL) is proposed to guarantee the rationality of route selecting in inter-vehicle communication scenario. Firstly, the traffic model is discussed and set up by using Poisson distribution. Then we analyze the process of traffic evaluation with enhanced learning, and exploit movement estimation to assist state memorization. The improvement of algorithm is provided at last compared with our previous work. Simulation results show that SAREL algorithm could achieve better performance in packet delivery ratio, especially when network connection ratio is average. Key words mobile ad hoc network - spatially aware routing - enhanced learning CLC number TP 316 Foundation item: Supported by Open Laboratory Foundation by China Ministry of Education (TKLJ9903), Project CarTALK 2000 by the European Commission (IST-2000-28185) and Project FleetNet-Internet on the Road by the German Ministry of Education and Research (01AK025)Biography: HAN Lu (1974-), male, Ph. D candidate, research direction; distributed artificial intelligence.

科技辅助第二语言教学研究趋势及其对国际中文教育的启示

In recent years,the enabling role of technology has been widely recognized in second language teaching and learning.Using a co-word analysis approach,this study aims to present the state-ofthe-art current literature in technology enhanced language learning.The analysis results show that most popular research themes from 2010 to 2020 are CMC,L2 writing,teacher education,L2 motivation,students’attitudes,SLA,L2 vocabulary learning,L2 reading,EFL,and L2 learners.Compared with these key themes,publications on technology enhanced teaching Chinese as a foreign language focus more on the investigation of the effectiveness of the use of latest technologies such as smart phones,virtual reality,intelligent tools,and gamification.Based on the findings of these investigations,the latest developments in these areas are synthesized and presented accordingly.In addition,newly-emerged key themes about the co-word analysis are also reported and their contributions to future investigations is briefly discussed.The implications for future research in technology enhanced teaching Chinese as a foreign language is also discussed.

Single channel speech enhancement via time-frequency dictionary learning

A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.