Elements and Steps of E-Learning Benchmarking Model for Higher Education Institutions

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.

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

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.

Semi-active model predictive control for 3rd generation benchmark problem using smart dampers

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.

E-Learning Islamic Studies for Form Four Students

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.

The University of Jordan E-Learning Platform: State, Students’ Acceptance and Challenges

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.

An Investigation on Telecommunication Staff＇s Acceptance of E-Learning Technology

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.

E-Learning Optimization Using Supervised Artificial Neural-Network

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.

Second-Order MaxEnt Predictive Modelling Methodology. III: Illustrative Application to a Reactor Physics Benchmark

This work illustrates the innovative results obtained by applying the recently developed the 2nd-order predictive modeling methodology called “2nd- 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 2nd-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 2nd-order response sensitivities must always be included to quantify the need for including (or not) the 3rd- and/or 4th-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 1st- and 2nd-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 3rd- and even 4th-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1st-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2nd-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).

超高层建筑结构benchmark模型及其地震反应分析

参照上海中心,根据设定的性能目标设计了一个超高层建筑结构的benchmark模型用于超高层建筑结构抗震研究。该结构总高度为606.1 m,抗震设防烈度为7度,场地类别为IV类,设计分组为第一组。该结构采用巨型框架-核心筒-伸臂桁架钢-混凝土混合结构体系,8道环带桁架将结构分为9个区,环带桁架与型钢混凝土巨柱共同构成了巨型框架结构体系,并通过6道伸臂桁架与核心筒相连,共同承受水平荷载。利用PERFORM-3D软件建立了结构的非线性数值计算模型,对结构进行了弹塑性地震反应分析,验证了结构的抗震性能。计算结果表明,满足现行设计规范的该超高层结构在大震作用下具有较大的安全余量。

基于ANSYS软件benchmark模型耗能减振控制分析

本文系统介绍了当前国际结构振动控制的公共平台———Benchm ark模型,首次给出使用通用有限元软件ANSYS对第3阶段20层非线性钢框架抗震结构的有限元建模,结果表明,模态、无控地震反应分析结果和ASCE给出的MATLAB结果相同。利用粘滞阻尼器对此模型的耗能减振数值仿真分析的结果表明:粘滞阻尼器是一种性能很好的阻尼器,使用本文给出ANSYS的Benchm ark有限元模型来进行耗能减振控制分析是方便的、可行的。

One to One营销优化算法的BenchMark验证方法

该文针对OnetoOne营销优化问题,提出了一种BenchMark验证方法。基于这一BenchMark验证方法,对原始Simplex法、DFS法和LIPSOL法进行了基于理论的BenchMark验证和基于MATLAB的BenchMark验证,给出了仿真结论,证明了提出的BenchMark验证方法的有效性。

环境激励下Benchmark结构损伤识别的试验研究

针对结构健康监测中如何利用在线监测数据进行健康诊断的问题,基于时间序列分析提出了一种新的损伤识别方法。首先,获得结构健康状态的监测数据作为参考状态样本,对各数据样本建立ARMA模型并计算模型残差的方差。然后,将未知状态的监测数据作为待检状态样本,代入已建立的参考状态ARMA模型计算新的残差方差。计算发现,损伤前后两状态模型残差方差存在差异。因而,提出以残差方差之比作为损伤敏感特征,并建立基于F分布的假设检验来辨识结构的状态并预警损伤。最后,以Benchmark结构在环境激励下的试验为例,运用本文方法进行了损伤识别研究。结果表明,基于ARMA模型残差方差的损伤敏感特征可准确地区别结构的健康状态和损伤状态,具备结构在线实时损伤识别的应用价值。

斜拉桥地震响应振动控制Benchmark问题研究

文章建立了斜拉桥结构的有限元模型和振动控制Benchmark模型,采用LQG控制算法和最优控制理论,基于MATLAB环境的Simulink程序,对斜拉桥结构振动的主动控制以及Benchmark问题进行研究,给出了独塔双面索斜拉桥在随机干扰作用下振动控制的仿真结果。

中国古建筑木结构健康监测Benchmark平台

结构健康监测方法已被引入中国古建筑木结构领域,用以实现古建木构的“预防性”保护,但上述过程仍处于起步阶段,亟需一个能够为不同健康监测方法的验证、比较和评估提供条件的平台。该文建立了包含实验室物理模型、有限元模型、平台试验和配套网站4个模块的中国古建筑木结构健康监测Benchmark平台。平台首先综合其他领域健康监测Benchmark平台优势和古建木构自身特征,以既有古建为原型进行简化得到实验室物理模型,并给出构件尺寸、用材材性、监测设备选取和损伤设计等信息。其次,使用有限元分析软件ABAQUS分别建立能够提取静力和动力信号的有限元模型,以作为实验室物理模型的补充。进而,对成型平台进行动态监测试验,通过试验结果和有限元结果对比证明本平台能够为不同的健康监测方法的分析、比较和评估提供条件,并缩小理论研究和实际应用之间的差距。同时,证明目前其他领域常用的模态分析流程同样适用于古建木构领域。最后,建立包含8个模块的网站,对上述信息进行公开,以方便后续研究人员调用。

基于Benchmark技术的汽车正面碰撞仿真建模

介绍Benchmark的工作流程;根据某车型Benchmark数据,采用有限元分析方法,建立包括车身结构、发动机和底盘结构的用于正面碰撞的整车有限元模型;根据实验结果验证模型的有效性。

地震激励下斜拉桥振动控制Benchmark模型研究

为了在地震波激励下对大跨桥梁结构建立一套振动控制系统评价体系,比较不同控制策略的控制效果,将艾默生纪念桥(Emerson Memorial Bridge)作为斜拉桥振动控制的Benchmark模型,用以研究地震激励下斜拉桥振动控制所采取的各种控制算法和装置的有效性。系统总结十余年来各国学者在该研究中涉及到的各种传统控制算法以及智能算法,阐述了该桥被动控制、主动控制、半主动控制、混合控制的应用情况,对未来的研究趋势进行展望。以艾默生纪念桥为例,选取新型主动EMD阻尼器作为控制装置,实施基于模糊推理的智能控制。将EMD装置的速度作为模糊控制器输入,制定模糊规则,快速、有效地确定EMD装置输出电压,从而对该桥在线实时控制。数值仿真结果表明:18项控制指标接近于样本控制,尤其对基础处剪力、倾覆力矩以及桥塔处拉索的减振控制效果明显。

Perspectives on benchmarking foundation models for network biology

Transfer learning has revolutionized fields including natural language understanding and computer vision by leveraging large-scale general datasets to pretrain models with foundational knowledge that can then be transferred to improve predictions in a vast range of downstream tasks.More recently,there has been a growth in the adoption of transfer learning approaches in biological fields,where models have been pretrained on massive amounts of biological data and employed to make predictions in a broad range of biological applications.However,unlike in natural language where humans are best suited to evaluate models given a clear understanding of the ground truth,biology presents the unique challenge of being in a setting where there are a plethora of unknowns while at the same time needing to abide by real-world physical constraints.This perspective provides a discussion of some key points we should consider as a field in designing benchmarks for foundation models in network biology.

基于智能压电摩擦阻尼器的Benchmark模型风振响应半主动控制

在改进的Pall摩擦耗能器的基础上设计了一种智能压电摩擦阻尼器,并分析了其阻尼力模型。利用设计出的智能摩擦阻尼器,采用基于经典线性最优控制理论的半主动控制策略对Benchm ark模型的风振响应控制进行了研究。结果表明:智能压电摩擦阻尼器对标准风振控制模型的半主动控制效果较为明显,相对于无控结构来说能有效降低结构的风振响应。此外,本文对基于限界Hrovat最优控制算法的半主动控制系统的参数优化进行了分析,说明了该半主动控制系统对Benchm ark模型阻尼的不确定性具有很好的鲁棒性,而对刚度的不确定性则表现出一定的敏感性。

生命线网络系统抗震拓扑优化的Benchmark模型

生命线系统拓扑优化问题的Benchmark模型是评测新型算法正确性和适用性的重要手段。基于此,首先以生命线网络系统抗震拓扑优化分析模型为背景,建立了该优化问题的三个Benchmark模型,并在Visual Compaq Fortran开发环境下,通过穷举法统计出解空间的所有网络,进而甄选出不同节点可靠度约束下的最优网络和若干次优网络,最后利用上述Benchmark模型对生命线网络系统抗震拓扑优化中的蚁群算法进行测试。结果表明,当网络规模较小时,蚁群算法能精确地搜索到最优解;当网络规模增大后,蚁群算法也能以较大概率搜索到最优解或次优解。

基于非线性随机最优控制的大跨度斜拉桥地震响应Benchmark问题研究

大跨度斜拉桥Benchmark问题的振动控制研究是当前国际结构控制研讨会的重要议题之一。以美国Bill Emerson Memorial斜拉桥第二阶段Benchmark模型为研究对象,在非线性随机动力学与控制的拟哈密顿理论体系框架下,运用基于随机平均法和随机动态规划原理的非线性随机最优(NSO)控制策略,对地震作用下的Benchmark模型进行MATLAB仿真分析。将最优控制力和性能评价指标与线性二次型Gauss(LQG)控制的计算结果进行对比,得出非线性随机最优控制策略能够更加有效地抑制斜拉桥的地震响应,提高结构的动力稳定性和抗震能力,具有更好的控制效果,对实际桥梁工程的振动控制具有较强的指导意义和适用价值。