The purposes of this research were to 1) develop of an e-learning benchmarking model for higher education institutions;2) analyze and synthesize e-learning indicators for e-learning benchmarking model. The research wa...The purposes of this research were to 1) develop of an e-learning benchmarking model for higher education institutions;2) analyze and synthesize e-learning indicators for e-learning benchmarking model. The research was conducted using the research and development methods. The result shows that there are eight elements of e-learning benchmarking model: 1) team/staffs 2) benchmarking’s title 3) comparative companies 4) benchmarking indicators 5) data collection method 6) analysis data and results 7) report of results and 8) action plan development. Moreover, four steps of benchmarking model will be used in this research. “Plan” is the step of setting team for benchmarking title and choosing the company to collect the benchmarking while “Do” is a field study in order to analyze and collect each indicator. The step “Check” presents the data to stakeholders and set the purposes of action plan. Finally, “Act” which is the development of action plan leads to the practice or implementation which related to auditing and evaluating.展开更多
Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medic...Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.展开更多
The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decis...The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.展开更多
Background:Large language models(LLMs)have shown promise in educational applications,but their performance on high-stakes admissions tests,such as the Dental Admission Test(DAT),remains unclear.Understanding the capab...Background:Large language models(LLMs)have shown promise in educational applications,but their performance on high-stakes admissions tests,such as the Dental Admission Test(DAT),remains unclear.Understanding the capabilities and limitations of these models is critical for determining their suitability in test preparation.Methods:This study evaluated the ability of 16 LLMs,including general-purpose models(e.g.,GPT-3.5,GPT-4,GPT-4o,GPT-o1,Google’s Bard,mistral-large,and Claude),domain-specific finetuned models(e.g.,DentalGPT,MedGPT,and BioGPT),and open-source models(e.g.,Llama2-7B,Llama2-13B,Llama2-70B,Llama3-8B,and Llama3-70B),to answer questions from a sample DAT.Quantitative analysis was performed to assess model accuracy in different sections,and qualitative thematic analysis by subject matter experts examined specific challenges encountered by the models.Results:GPT-4o and GPT-o1 outperformed others in text-based questions assessing knowledge and comprehension,with GPT-o1 achieving perfect scores in the natural sciences(NS)and reading comprehension(RC)sections.Open-source models such as Llama3-70B also performed competitively in RC tasks.However,all models,including GPT-4o,struggled substantially with perceptual ability(PA)items,highlighting a persistent limitation in handling image-based tasks requiring visual-spatial reasoning.Fine-tuned medical models(e.g.,DentalGPT,MedGPT,and BioGPT)demonstrated moderate success in text-based tasks but underperformed in areas requiring critical thinking and reasoning.Thematic analysis identified key challenges,including difficulties with stepwise problem-solving,transferring knowledge,comprehending intricate questions,and hallucinations,particularly on advanced items.Conclusions:While LLMs show potential for reinforcing factual knowledge and supporting learners,their limitations in handling higherorder cognitive tasks and image-based reasoning underscore the need for judicious integration with instructor-led guidance and targeted practice.This study provides valuable insights into the capabilities and limitations of current LLMs in preparing prospective dental students and highlights pathways for future innovations to improve performance across all cognitive skills assessed by the DAT.展开更多
Advancing the integration of artificial intelligence and polymer science requires high-quality,open-source,and large-scale datasets.However,existing polymer databases often suffer from data sparsity,lack of polymer-pr...Advancing the integration of artificial intelligence and polymer science requires high-quality,open-source,and large-scale datasets.However,existing polymer databases often suffer from data sparsity,lack of polymer-property labels,and limited accessibility,hindering system-atic modeling across property prediction tasks.Here,we present OpenPoly,a curated experimental polymer database derived from extensive lit-erature mining and manual validation,comprising 3985 unique polymer-property data points spanning 26 key properties.We further develop a multi-task benchmarking framework that evaluates property prediction using four encoding methods and eight representative models.Our re-sults highlight that the optimized degree-of-polymerization encoding coupled with Morgan fingerprints achieves an optimal trade-off between computational cost and accuracy.In data-scarce condition,XGBoost outperforms deep learning models on key properties such as dielectric con-stant,glass transition temperature,melting point,and mechanical strength,achieving R2 scores of 0.65-0.87.To further showcase the practical utility of the database,we propose potential polymers for two energy-relevant applications:high temperature polymer dielectrics and fuel cell membranes.By offering a consistent and accessible benchmark and database,OpenPoly paves the way for more accurate polymer-property modeling and fosters data-driven advances in polymer genome engineering.展开更多
With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent lea...With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.展开更多
A semi-active strategy for model predictive control （MPC）, in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A mult...A semi-active strategy for model predictive control （MPC）, in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A multi-step predictive model is developed to estimate the seismic performance of high-rise buildings, taking into account of the effects of nonlinearity, time-variability, model mismatching, and disturbances and uncertainty of controlled system parameters by the predicted error feedback in the multi-step predictive model. Based on the predictive model, a Kalman-Bucy observer suitable for semi-active strategy is proposed to estimate the state vector from the acceleration and semi-active control force feedback. The main advantage of the proposed strategy is its inherent stability, simplicity, on-line real-time operation, and the ability to handle nonlinearity, uncertainty, and time-variability properties of structures. Numerical simulation of the nonlinear seismic responses of a controlled 20-story benchmark building is carried out, and the simulation results are compared to those of other control systems. The results show that the developed semi-active strategy can efficiently reduce the nonlinear seismic response of high-rise buildings.展开更多
Despite the efforts by Ministry of Education to promote Information and Communication Technology （ICT） in education in Malaysia, the Islamic education syllabus is far behind the intended plan in ICT usage in learnin...Despite the efforts by Ministry of Education to promote Information and Communication Technology （ICT） in education in Malaysia, the Islamic education syllabus is far behind the intended plan in ICT usage in learning and teaching. Concern was raised that Islamic Studies faced the risk of being misunderstood if the lessons were taught through self-accessing method with minimal intervention from teachers. Using the Dick and Carey instructional model as a framework, an e-learning version was devised for the national Form 4 Islamic Studies syllabus, ＂The steps and procedures of Hajj and Umrah＂. The Islamic Studies textbook for national secondary schools in Malaysia was reviewed using a systematic approach, from identifying the instructional goal through to formative and summative evaluation processes. Interview sessions with students were conducted to assess the developed e-learning Islamic Studies content. A subsequent survey with students was conducted. Results from the study indicated the e-learning Islamic Studies content had the potential to help students, being easy to use, and attracting and retaining students＇ attention.展开更多
The rapid changes and increased complexity in today’s world present new challenges and put new demands on the education system. There has been generally a growing awareness of the necessity?to change and improve the ...The rapid changes and increased complexity in today’s world present new challenges and put new demands on the education system. There has been generally a growing awareness of the necessity?to change and improve the existing system towards online learning. Jordan is one of the distinguished countries in the Middle East with rapid progress in education and with advanced teaching and learning technologies. The University of Jordan is trying to exploit Information and Communication Technology (ICT) in education and moving forward by introducing the latest E-learning management systems (LMSs) to keep pace of technological revolution in the higher education. It is?important to find out the impact of E-learning management system in the University of Jordan,?examine the students’ acceptance for this new system and address the challenges facing the students while using the E-learning management system and these are what this paper is trying to do.展开更多
As the information on telecommunication products updates rapidly, using E-learning in staff training becomes an edge for company operation. However, previous studies showed that staff＇s attitude toward E-learning sig...As the information on telecommunication products updates rapidly, using E-learning in staff training becomes an edge for company operation. However, previous studies showed that staff＇s attitude toward E-learning significantly affected the outcomes of training. The purpose of this study is to investigate the acceptance of E-learning in a telecommunication company. The researchers adopted the technology acceptance model （TAM） and diffusion of innovation theory to evaluate the perceived usefulness and perceived ease of use on E-learning, in addition to employees＇ self-directed learning motivation, attitude toward computers, and organizational influence. We randomly chose 571 employees of the telecommunication entrepreneur at the Taichung office in Taiwan to participate in this survey. The result showed that employees＇ background factors such as age, job position, marital status, education level and the scale of job unit had the significant impact on behavioral intention to use E-learning. Employees＇ self-directed learning, attitude toward computers, and organizational influence respectively also had positive effects on perceived usefulness of E-learning and perceived ease of use of E-learning. Furthermore, employees＇ perceived usefulness of E-learning and perceived ease of use of E-learning also had a positive effect on behavioral intention to use E-learning systems.展开更多
Improving learning outcome has always been an important motivating factor in educational inquiry. In a blended learning environment where e-learning and traditional face to face class tutoring are combined, there are ...Improving learning outcome has always been an important motivating factor in educational inquiry. In a blended learning environment where e-learning and traditional face to face class tutoring are combined, there are opportunities to explore the role of technology in improving student’s grades. A student’s performance is impacted by many factors such as engagement, self-regulation, peer interaction, tutor’s experience and tutors’ time involvement with students. Furthermore, e-course design factors such as providing personalized learning are an urgent requirement for improved learning process. In this paper, an artificial neural network model is introduced as a type of supervised learning, meaning that the network is provided with example input parameters of learning and the desired optimized and correct output for that input. We also describe, by utilizing e-learning interactions and social analytics how to use artificial neural network to produce a converging mathematical model. Then students’ performance can be efficiently predicted and so the danger of failing in an enrolled e-course should be reduced.展开更多
The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by sub...The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.展开更多
This work illustrates the innovative results obtained by applying the recently developed the 2<sup>nd</sup>-order predictive modeling methodology called “2<sup>nd</sup>- BERRU-PM”, where the ...This work illustrates the innovative results obtained by applying the recently developed the 2<sup>nd</sup>-order predictive modeling methodology called “2<sup>nd</sup>- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2<sup>nd</sup>-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2<sup>nd</sup>-order response sensitivities must always be included to quantify the need for including (or not) the 3<sup>rd</sup>- and/or 4<sup>th</sup>-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1<sup>st</sup>- and 2<sup>nd</sup>-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3<sup>rd</sup>- and even 4<sup>th</sup>-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1<sup>st</sup>-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2<sup>nd</sup>-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).展开更多
Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-spe...Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.展开更多
The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of L...The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of LMS is essential to ensure successful implementation.The Technology Acceptance Model(TAM)has been widely used to check user acceptance of various technologies,including LMS.This study conducted a systematic literature review(SLR)to analyze existing research on the application of TAM in the context of LMS.A comprehensive search of the academic database was conducted to identify relevant studies published in 2010-2025.The review synthesizes findings related to the core constructs of TAM—Perceived Usability,Perceived Ease of Use,Behavioral Intent,and Actual Use—as well as extended factors such as system quality,self-efficacy,and social influence.The results reveal circumstantial evidence supporting the predictive power of TAM in LMS adoption,while also highlighting emerging trends and gaps in the literature.This review contributes to a deeper understanding of user acceptance in a digital learning environment and provides recommendations for future research and practical LMS implementation strategies.展开更多
文摘The purposes of this research were to 1) develop of an e-learning benchmarking model for higher education institutions;2) analyze and synthesize e-learning indicators for e-learning benchmarking model. The research was conducted using the research and development methods. The result shows that there are eight elements of e-learning benchmarking model: 1) team/staffs 2) benchmarking’s title 3) comparative companies 4) benchmarking indicators 5) data collection method 6) analysis data and results 7) report of results and 8) action plan development. Moreover, four steps of benchmarking model will be used in this research. “Plan” is the step of setting team for benchmarking title and choosing the company to collect the benchmarking while “Do” is a field study in order to analyze and collect each indicator. The step “Check” presents the data to stakeholders and set the purposes of action plan. Finally, “Act” which is the development of action plan leads to the practice or implementation which related to auditing and evaluating.
基金Guangzhou Science and Technology Program,Grant/Award Numbers:2025B03J0110,2024A03J1074,2024A03J0927。
文摘Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.
文摘The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.
基金partially supported by the National Institutes of Health’s National Center for Complementary and Integrative Health under grant number R01AT009457National Institute on Aging under grant number R01AG078154National Cancer Institute under grant number R01CA287413.
文摘Background:Large language models(LLMs)have shown promise in educational applications,but their performance on high-stakes admissions tests,such as the Dental Admission Test(DAT),remains unclear.Understanding the capabilities and limitations of these models is critical for determining their suitability in test preparation.Methods:This study evaluated the ability of 16 LLMs,including general-purpose models(e.g.,GPT-3.5,GPT-4,GPT-4o,GPT-o1,Google’s Bard,mistral-large,and Claude),domain-specific finetuned models(e.g.,DentalGPT,MedGPT,and BioGPT),and open-source models(e.g.,Llama2-7B,Llama2-13B,Llama2-70B,Llama3-8B,and Llama3-70B),to answer questions from a sample DAT.Quantitative analysis was performed to assess model accuracy in different sections,and qualitative thematic analysis by subject matter experts examined specific challenges encountered by the models.Results:GPT-4o and GPT-o1 outperformed others in text-based questions assessing knowledge and comprehension,with GPT-o1 achieving perfect scores in the natural sciences(NS)and reading comprehension(RC)sections.Open-source models such as Llama3-70B also performed competitively in RC tasks.However,all models,including GPT-4o,struggled substantially with perceptual ability(PA)items,highlighting a persistent limitation in handling image-based tasks requiring visual-spatial reasoning.Fine-tuned medical models(e.g.,DentalGPT,MedGPT,and BioGPT)demonstrated moderate success in text-based tasks but underperformed in areas requiring critical thinking and reasoning.Thematic analysis identified key challenges,including difficulties with stepwise problem-solving,transferring knowledge,comprehending intricate questions,and hallucinations,particularly on advanced items.Conclusions:While LLMs show potential for reinforcing factual knowledge and supporting learners,their limitations in handling higherorder cognitive tasks and image-based reasoning underscore the need for judicious integration with instructor-led guidance and targeted practice.This study provides valuable insights into the capabilities and limitations of current LLMs in preparing prospective dental students and highlights pathways for future innovations to improve performance across all cognitive skills assessed by the DAT.
基金financially supported by the National Natural Science Foundation of China (Nos. 92372126,52373203)the Excellent Young Scientists Fund Program
文摘Advancing the integration of artificial intelligence and polymer science requires high-quality,open-source,and large-scale datasets.However,existing polymer databases often suffer from data sparsity,lack of polymer-property labels,and limited accessibility,hindering system-atic modeling across property prediction tasks.Here,we present OpenPoly,a curated experimental polymer database derived from extensive lit-erature mining and manual validation,comprising 3985 unique polymer-property data points spanning 26 key properties.We further develop a multi-task benchmarking framework that evaluates property prediction using four encoding methods and eight representative models.Our re-sults highlight that the optimized degree-of-polymerization encoding coupled with Morgan fingerprints achieves an optimal trade-off between computational cost and accuracy.In data-scarce condition,XGBoost outperforms deep learning models on key properties such as dielectric con-stant,glass transition temperature,melting point,and mechanical strength,achieving R2 scores of 0.65-0.87.To further showcase the practical utility of the database,we propose potential polymers for two energy-relevant applications:high temperature polymer dielectrics and fuel cell membranes.By offering a consistent and accessible benchmark and database,OpenPoly paves the way for more accurate polymer-property modeling and fosters data-driven advances in polymer genome engineering.
文摘With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.
基金Fujian Province Youth Foundation for InnovativResearch Under Grant No. 2006F3008Fujian ProvincEducational Special Foundation Under Grant No. JA06027
文摘A semi-active strategy for model predictive control （MPC）, in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A multi-step predictive model is developed to estimate the seismic performance of high-rise buildings, taking into account of the effects of nonlinearity, time-variability, model mismatching, and disturbances and uncertainty of controlled system parameters by the predicted error feedback in the multi-step predictive model. Based on the predictive model, a Kalman-Bucy observer suitable for semi-active strategy is proposed to estimate the state vector from the acceleration and semi-active control force feedback. The main advantage of the proposed strategy is its inherent stability, simplicity, on-line real-time operation, and the ability to handle nonlinearity, uncertainty, and time-variability properties of structures. Numerical simulation of the nonlinear seismic responses of a controlled 20-story benchmark building is carried out, and the simulation results are compared to those of other control systems. The results show that the developed semi-active strategy can efficiently reduce the nonlinear seismic response of high-rise buildings.
文摘Despite the efforts by Ministry of Education to promote Information and Communication Technology （ICT） in education in Malaysia, the Islamic education syllabus is far behind the intended plan in ICT usage in learning and teaching. Concern was raised that Islamic Studies faced the risk of being misunderstood if the lessons were taught through self-accessing method with minimal intervention from teachers. Using the Dick and Carey instructional model as a framework, an e-learning version was devised for the national Form 4 Islamic Studies syllabus, ＂The steps and procedures of Hajj and Umrah＂. The Islamic Studies textbook for national secondary schools in Malaysia was reviewed using a systematic approach, from identifying the instructional goal through to formative and summative evaluation processes. Interview sessions with students were conducted to assess the developed e-learning Islamic Studies content. A subsequent survey with students was conducted. Results from the study indicated the e-learning Islamic Studies content had the potential to help students, being easy to use, and attracting and retaining students＇ attention.
文摘The rapid changes and increased complexity in today’s world present new challenges and put new demands on the education system. There has been generally a growing awareness of the necessity?to change and improve the existing system towards online learning. Jordan is one of the distinguished countries in the Middle East with rapid progress in education and with advanced teaching and learning technologies. The University of Jordan is trying to exploit Information and Communication Technology (ICT) in education and moving forward by introducing the latest E-learning management systems (LMSs) to keep pace of technological revolution in the higher education. It is?important to find out the impact of E-learning management system in the University of Jordan,?examine the students’ acceptance for this new system and address the challenges facing the students while using the E-learning management system and these are what this paper is trying to do.
文摘As the information on telecommunication products updates rapidly, using E-learning in staff training becomes an edge for company operation. However, previous studies showed that staff＇s attitude toward E-learning significantly affected the outcomes of training. The purpose of this study is to investigate the acceptance of E-learning in a telecommunication company. The researchers adopted the technology acceptance model （TAM） and diffusion of innovation theory to evaluate the perceived usefulness and perceived ease of use on E-learning, in addition to employees＇ self-directed learning motivation, attitude toward computers, and organizational influence. We randomly chose 571 employees of the telecommunication entrepreneur at the Taichung office in Taiwan to participate in this survey. The result showed that employees＇ background factors such as age, job position, marital status, education level and the scale of job unit had the significant impact on behavioral intention to use E-learning. Employees＇ self-directed learning, attitude toward computers, and organizational influence respectively also had positive effects on perceived usefulness of E-learning and perceived ease of use of E-learning. Furthermore, employees＇ perceived usefulness of E-learning and perceived ease of use of E-learning also had a positive effect on behavioral intention to use E-learning systems.
文摘Improving learning outcome has always been an important motivating factor in educational inquiry. In a blended learning environment where e-learning and traditional face to face class tutoring are combined, there are opportunities to explore the role of technology in improving student’s grades. A student’s performance is impacted by many factors such as engagement, self-regulation, peer interaction, tutor’s experience and tutors’ time involvement with students. Furthermore, e-course design factors such as providing personalized learning are an urgent requirement for improved learning process. In this paper, an artificial neural network model is introduced as a type of supervised learning, meaning that the network is provided with example input parameters of learning and the desired optimized and correct output for that input. We also describe, by utilizing e-learning interactions and social analytics how to use artificial neural network to produce a converging mathematical model. Then students’ performance can be efficiently predicted and so the danger of failing in an enrolled e-course should be reduced.
文摘The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.
文摘This work illustrates the innovative results obtained by applying the recently developed the 2<sup>nd</sup>-order predictive modeling methodology called “2<sup>nd</sup>- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2<sup>nd</sup>-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2<sup>nd</sup>-order response sensitivities must always be included to quantify the need for including (or not) the 3<sup>rd</sup>- and/or 4<sup>th</sup>-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1<sup>st</sup>- and 2<sup>nd</sup>-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3<sup>rd</sup>- and even 4<sup>th</sup>-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1<sup>st</sup>-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2<sup>nd</sup>-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).
基金Funded by the Spanish Government and FEDER funds(AEI/FEDER,UE)under grant PID2021-124502OB-C42(PRESECREL)the predoctoral program“Concepción Arenal del Programa de Personal Investigador en formación Predoctoral”funded by Universidad de Cantabria and Cantabria’s Government(BOC 18-10-2021).
文摘Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.
文摘The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of LMS is essential to ensure successful implementation.The Technology Acceptance Model(TAM)has been widely used to check user acceptance of various technologies,including LMS.This study conducted a systematic literature review(SLR)to analyze existing research on the application of TAM in the context of LMS.A comprehensive search of the academic database was conducted to identify relevant studies published in 2010-2025.The review synthesizes findings related to the core constructs of TAM—Perceived Usability,Perceived Ease of Use,Behavioral Intent,and Actual Use—as well as extended factors such as system quality,self-efficacy,and social influence.The results reveal circumstantial evidence supporting the predictive power of TAM in LMS adoption,while also highlighting emerging trends and gaps in the literature.This review contributes to a deeper understanding of user acceptance in a digital learning environment and provides recommendations for future research and practical LMS implementation strategies.
