Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning w...Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.展开更多
Artificial intelligence is increasingly entering everyday healthcare.Large language model(LLM)systems such as Chat Generative Pre-trained Transformer(ChatGPT)have become potentially accessible to everyone,including pa...Artificial intelligence is increasingly entering everyday healthcare.Large language model(LLM)systems such as Chat Generative Pre-trained Transformer(ChatGPT)have become potentially accessible to everyone,including patients with inflammatory bowel diseases(IBD).However,significant ethical issues and pitfalls exist in innovative LLM tools.The hype generated by such systems may lead to unweighted patient trust in these systems.Therefore,it is necessary to understand whether LLMs(trendy ones,such as ChatGPT)can produce plausible medical information(MI)for patients.This review examined ChatGPT’s potential to provide MI regarding questions commonly addressed by patients with IBD to their gastroenterologists.From the review of the outputs provided by ChatGPT,this tool showed some attractive potential while having significant limitations in updating and detailing information and providing inaccurate information in some cases.Further studies and refinement of the ChatGPT,possibly aligning the outputs with the leading medical evidence provided by reliable databases,are needed.展开更多
随着Chat GPT的发布,大型语言模型(large language model,LLM)已经在全球迅速崭露头角并在各行各业广泛应用。与此同时,以中文语言为基础的大型语言模型研究逐渐展开,其在教育领域的应用与效果也有待研究。为此,文章以10年高考题目数据...随着Chat GPT的发布,大型语言模型(large language model,LLM)已经在全球迅速崭露头角并在各行各业广泛应用。与此同时,以中文语言为基础的大型语言模型研究逐渐展开,其在教育领域的应用与效果也有待研究。为此,文章以10年高考题目数据集“GAOKAO-Bench”为测试数据,通过统计和分析11个不同来源(包括大型企业、学术机构和新兴公司)的开源中文大型语言模型在9个不同学科(语文、数学、英语、物理、化学、生物、历史、政治、地理)中的表现,来评估不同的中文大型语言模型在教育教学自动评估中的效果。随后,文章根据评估结果,从多学科、多维度出发,对模型在各个科目上的推理表现进行分析研究。最后,文章对中文大型语言模型在教育教学自动评估中可能遇到的挑战与问题进行探讨,并提出可供优化的思路与方法,以期推动中文大型语言模型在未来教育教学中的发展与传播。展开更多
文摘Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.
文摘Artificial intelligence is increasingly entering everyday healthcare.Large language model(LLM)systems such as Chat Generative Pre-trained Transformer(ChatGPT)have become potentially accessible to everyone,including patients with inflammatory bowel diseases(IBD).However,significant ethical issues and pitfalls exist in innovative LLM tools.The hype generated by such systems may lead to unweighted patient trust in these systems.Therefore,it is necessary to understand whether LLMs(trendy ones,such as ChatGPT)can produce plausible medical information(MI)for patients.This review examined ChatGPT’s potential to provide MI regarding questions commonly addressed by patients with IBD to their gastroenterologists.From the review of the outputs provided by ChatGPT,this tool showed some attractive potential while having significant limitations in updating and detailing information and providing inaccurate information in some cases.Further studies and refinement of the ChatGPT,possibly aligning the outputs with the leading medical evidence provided by reliable databases,are needed.
文摘随着Chat GPT的发布,大型语言模型(large language model,LLM)已经在全球迅速崭露头角并在各行各业广泛应用。与此同时,以中文语言为基础的大型语言模型研究逐渐展开,其在教育领域的应用与效果也有待研究。为此,文章以10年高考题目数据集“GAOKAO-Bench”为测试数据,通过统计和分析11个不同来源(包括大型企业、学术机构和新兴公司)的开源中文大型语言模型在9个不同学科(语文、数学、英语、物理、化学、生物、历史、政治、地理)中的表现,来评估不同的中文大型语言模型在教育教学自动评估中的效果。随后,文章根据评估结果,从多学科、多维度出发,对模型在各个科目上的推理表现进行分析研究。最后,文章对中文大型语言模型在教育教学自动评估中可能遇到的挑战与问题进行探讨,并提出可供优化的思路与方法,以期推动中文大型语言模型在未来教育教学中的发展与传播。
