Depending on the profile and responsibilities of each management team member, they will have access to the e-learning platform, in or- der to obtain a personalized learning program based on a specific ontology, as well as to get bibliographies complying with their learning requirements. For example, the general man- ager may be a physician - then, his/her train- ing needs will have to focus more on man- agement issues; in other hospitals the manag- er may be an economist, which means an in- creased emphasis in his/her training process on medical issues related to the hospital func- tioning. It follows the importance of the per- sonalization process, depending on the pro- file resulted from the basic profession, that will capitalize the already acquired knowledge to identify the most suitable learning programs which are provided by the e-learningsystem ontology.
Figure (7) represents the academic activities diagram for the proposed E-Learningsystem. It consists of three activities that are the lecture feedback, the material assignment, and the material project. For these activities, the student (U) sends an activation for the community scheduler (S) to perform the TSLA (i.e. the sending process), and also the instructor (i) sends an activation to access the RSLA (i.e. resources). Finally, the BSLA is performed by sending the lecture feedback, Material assignment, and Material project.
For example, in companies, e-Learning is used to deliver training courses to employees and in universities, e- Learning is used for enrolment of students in different courses, provides teaching without any face-to-face interaction, or on-campus facilities, but through internet that is online. As a whole, e- Learning includes Distance Learning (DL), Computer Based Teaching (CBT), Computer Aided Instruction (CAI), and Life Long Learning (LLL) principle. So, we see that, e-Learning consists of various types of databases, storing information for user access. To implement e-Learning, data mining can help to construct e-textbook, e-reading, digital libraries, etc.
The requirements specifications for a project management user centred e-learningsystem implied in conjunction with another specific methods and techniques, the development of a survey – based on questionnaire addressed to potential final users (see www.ici.ro/sinpers). In this article is synthesized the responses of more than 250 persons of various economical sectors and organizations, with different skills and expertise in project management domain. The survey results are very useful for project progress: first for the instructional process design based on personalisation principle and secondly for the educational content development and improvement, based on actual experience and requirements of potential users.
Valk, Rashid & Elder (2010) however posited that of the many different forms of ICTs, mobile phones are thought, for several reasons, to be a particularly suitable tool for advancing education in developing regions. Keegan (2005) stated that because of the lack of infrastructure for ICT (cabling for Internet and telecom) in certain areas in Africa, the growth of wireless infrastructure is enormous - even more rapid than in many first world countries. For Keegan (2005), using the mobile phone for learning is particularly suited to Distance Education because, “if serving the mobile learners is the focus of M Learning, then D.E institutions have always been doing this---serving learners anytime, anywhere”. Mobile technologies, which include hand held computers, Personal Digital Assistants, mobile phones, lap tops, and i-Phones, are all part of the emerging information revolution taking place worldwide. People need not work with large computers on desk tops, or made to carry laptops searching for wired internet connection. According to Bradford (2010), knowledge and learning is now literally at a person‘s fingertips via the mobile phone, and that several decades ago, when the nontraditional student began impacting higher education, distance education, asynchronous education and open or virtual learning emerged as a way to continually educate students.Keegan (2005) stated that: “ one and a half billion people, all over the world, are walking around with powerful computers in their pockets and purses but they often don’t realize it, because they call it something else…. today’s high-end cell phones have the computing power of the mid-1990’s PC, while consuming only one one-hundredth of its energy” . Statistics, as indicated below, have established the exponential growth of mobile phones in sub Saharan Africa, even surpassing the figures in some developed countries. This is indeed a pointer to the important role mobile phones are expected to play in educational delivery in Nigeria. For example,
We find that, controlling for individual time-invariant unobservable characteristics, work- ing while remaining in school has a depressing effect on their proficiency test scores. The magnitude of these effects range from 3% of a standard deviation in test scores to 8% which represents from one quarter to one half of a year of lost learning. The results are robust to idiosyncratic preferences and we perform robustness checks to show that the results are not due to idiosyncratic trends or economic shocks at the household level. Additionally, we find that the magnitude of the negative impact increases with a students ability and that there are both lingering and cumulative negative effects from working whir in school. These results provide valuable information to policy makers who wish to understand what types of child work to target for elimination and the effect of child labor on human capital accumu- lation. Finally, we find possible channels through which child labor can impact learning as participants in the labor market are more likely to report that they miss school days, turn homework in late and complete homework while in school rather than at home.
The work reported in this paper is based on a novel idea to enhance the productivity of the previously developed systems by integration of data mining and AI techniques as mentioned above. Accordingly, a new architecture of Learning Apprentice System is being proposed and implemented in the field of medical claim processing for identifying and fixing (if possible) errors in medical claims. The proposed architecture is based on the development in relational database environment to maintain the medical billing data is in relational format (as generally data is available in this format) and database management system. Subsequently, implementation of data mining module and rule engine/inference engine is in languages like Structure Query Language (SQL). It is evident from our experience that the proposed architecture has enhanced the performance of the respective system. Moreover, the proposed architecture will also eliminate need of transforming data from relational environment into the environment of rule engine/ inference engine.
process, there were a few exceptions: for odors 3 and 8 no KC became specific. Population sparseness was increased in odor 7, without reaching single cell specificity. The lack of specific response in these simulations likely arose because only a small fraction of all PN combinations had real KC targets in our model. Similarly, a single experiment in vivo sampling only a fraction of the total KC population may reveal no responses to a given odor . Overall, odor representations were significantly sparser after synaptic tuning through SRDP (p,0.0002, paired t-test on Figure 1. Synaptic plasticity and coincidence detection in the model Kenyon cell. (A) The SRDP rule was applied every 100 ms (large box over dashed time grid, see Materials and Methods). For each KC spike within that window, the instantaneous PN firing rate was estimated in the preceding 100 ms (small box; for this potentiation example: 3 spikes, or 30 Hz). (B) SRDP learning rule look-up table for potentiation (white), depression (light gray), and no change (dark grey), embodying the interaction of a PN discrete covariance term and a KC binary term. Axes indicate spike counts in 100 ms windows, as exemplified in A. (C) The pairing time window for STDP was determined by two exponentially decaying functions critically depending on the timing difference between the onset of the excitatory postsynaptic potential (EPSP) and the peak of the postsynaptic action potential. (See Materials and Methods; from . Copyright 1998 by the Society for Neuroscience. Reprinted with permission.) (D) Linear dependence of synaptic change on instantaneous synaptic strength (factor Ci, Equation 1). Values normalized by the initial conductance g*. (E) Representative connection between a PN axon (black) and a KC dendritic tree (gray) in the MB calyx (light gray). (From . Reprinted with permission from AAAS.) (F) Gaussian distributions of increasing width ( = 26STD, abscissa) generated input trains of 10 spikes with decreasing probability of triggering a KC spike (see also [G]). Probability was higher than 50% for widths of 18 ms or less (dashed line). (G) Input combinations generated in (F) resorted according to the actual interval between first and last spike (range). Coincidence detection events (KC spike; black bars, 2-ms steps) predominated against non-firing combinations (gray bars) for ranges up to 26 ms (upper panel: total count; lower panel: relative count). Coincident spikes within a 20 ms range were always detected.
The function f : X → Y is the unknown, usually nonlinear, target function, where X denotes the input space, and Y is the output space. The ultimate goal in black-box system identification consists in finding a regression model h ∈ H : X → Y that approximates f in its entire domain, based on a finite set of measurements in the system O. Set H denotes the candidate models or hypotheses. Over the last decades, a number of learning models have been proposed to approximate or learn f , such as piecewise affine models (PAPADAKIS; KABURLASOS, 2010), neural networks (NARENDRA; PARTHASARATHY, 1990), fuzzy local models (TAKAGI; SUGENO, 1985; REZAEE; FAZEL ZARANDI, 2010), kernel- based methods (GREGORCIC; LIGHTBODY, 2008; ROJO-ALVAREZ et al., 2004), hybrid models (HABER; UNBEHAUEN, 1990; LIMA; COELHO; VON ZUBEN, 2007) and polynomial models (BILLINGS; CHEN; KORENBERG, 1989). Some of them will be discussed later in this thesis.
B. Language-independent analysis of the documents Interesting and in the multilingual globalized world useful is the use of language-independent document analysis. It requires no language-specific tools and can therefore be deployed in the environment of almost any language that is using alphabetic writing system, in which each symbol represents just one phoneme – consonant or vowel – especially Latin, Cyrillic and Greek. Our team is currently focused on syllabic or even logographic languages, but at this point there have not been a sufficient number of experiments.
Collaborative system is currently underway to many organizations of the fact that it is not just a catch phrase or fad, but it is truly an essential shift in the way technology delivers value to various businesses nature. Schools are the place for handling information and knowledge and, in most developed countries, Internet has been a source of information as well as a tool for teaching and learning. Thus, it is crucial to have a transformation in our education field to allow new generations to become competent in the technology use. The purpose of this paper is to find out the technique that is able to enhance the collaborative learning process which is known as Think-Pair-Share. This study also aims at proposing a collaborative system that will apply the Think-Pair- Share technique to ease the collaborative process among teacher and students. The CETLs project is meant to address the support for collaborative learning and the establishment of shared understanding among students and teachers. This paper also introduces a collaborative framework for CETLs which adapt the use of Think-Pair-Share in a collaborative environment.
st udy w it h m ult iple pr ogr am s m ay r ev eal differences in how pr ogr am s v iew lear ning env ir onm ent s as a m eans t o incr ease lear ning. I t w ould also be int er est ing t o invest igat e how inst r uctors could im plem ent changes to cour ses based on dat a fr om each adm inist r at ion of t he CLS. Adding open ended quest ions t o t he CLS for st udent s and inst r uct ors m ay shed addit ional insight on learning. St udent s could be ask ed t o descr ibe how t heir com for t abilit y w it h classm at es, inst r uct or , and course cont ent changed t hroughout t he sem est er and inst r uctor s could be asked t o descr ibe how t hey used t he dat a for m t he CLS to infor m t heir t eaching.
From these responses, we are inclined to say that the knowledge structure of experienced mathematics educators is very similar. Even though the knowledge of an individual expert consists of both a cognitive element — the individual‘s viewpoints and beliefs, and a technical element —the i ndividual‘s context specific skills and abilities , we can use experienced mathematics educators' knowledge structure as the basic knowledge about how to solve questions in problem based linear algebra system.
Security assessment is analysis performed to determine whether, and to what extent, a power system is “reasonably” safe from serious interference to its operation. Thus, security assessment involves the evaluation of available data to estimate the relative robustness (security level) of the system in its present state or some near-term future state. The SSA problem is considered as an important aspect in power system operation. The main difficulty lies in the fact that electric power systems are highly nonlinear. The solution of a nonlinear system of equations (named the load flow equations) is necessary in order to determine the power flow pattern and the voltage profile of the system. Time constrained is the main problem to solve systems of several thousand buses within a few seconds on a desktop computer. Difficulties do arise in solving the power flow equations for unusual or highly stressed operating conditions resulting in either slow, or no, convergence to a solution. The problem is further complicated when power system is deregulated. In recent years, this deregulation of power system has turned SSA into a challenging task for which acceptably fast and accurate assessment methodology is essential. Therefore, a crucial need for faster and more accurate methods is required for SSA.
The motivation of this paper is to obtain an optimal control policy in a stochastic environment which can be applied to a wide variety of applications  commonly known as Markov Decision Problems (MDPs). Markov decision problems provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying a wide range of optimization problems. Artificial Intelligence (AI) offers scope in solving such problems using techniques of Dynamic Programming (DP), Heuristic approaches and Reinforcement Learning (RL). AI is defined as the study and design of intelligent agents  that perceives its environment and takes actions that maximize its chances of success. An MDP framework  has the following elements: a) State of the system, b) Actions, c) Transition probabilities, d) Transition rewards, e) a Policy and f) a Performance Metric. Dynamic programming is guaranteed to give optimal solutions to MDPs.
http://joemls.tku.edu.tw at any time and place. The United States started the NLII (National Learning Infrastructure Initiative) program in 1994 to promote e-learning, and in 1996, Taiwan began developing e-learning with the promotion of distance learning in higher education. E-learning is currently one of Taiwan’s key national science and technology programs. In 2002, the Executive Yuan, the highest level of government councils in Taiwan, started a six-year program “Challenge 2008— National Development Plan of the National Science Council” and completed the first stage of its E-Learning Program by 2007. The goal of this program was to promote e-learning with in Taiwan with a view to enhance the country’s overall competitiveness. In 2008, the National Science Council merged the “E-Learning Program” and “Digital Archives Program” into the “Taiwan e-Learning and Digital Archives Program” (TELDAP), with the aim of bringing together government, academic, and private sector resources to make Taiwan a center of global e-learning. According to the analyses of Tseng, Chang, Tutwiler, Lin, & Barufaldi, the effectiveness of this program was significant. In 2000, one of the European Union’s targets for 2010 was to make Europe the world’s most competitive knowledge-based economy, in which e-Learning would be a key strategy, with ICT (Information & Communication Technologies) applied to teaching resources and services to improve learning quality.
During the last decade, Information Technology (IT) has been the primary force driving the transforma- tion of roles in the education industry. More specifically, the World Wide Web (WWW) and associated technologies provided a new playground with new rules and tools to conduct instruction and create novel approaches to learning. We have seen the application of IT in education in the form of CD-ROMs. With the evolution of the WWW we saw education marketed as long distance learning, web based learner centered environments, internet based learning environments, and self instructed learning. With all the different models used on the web, few have studied their acceptance and their effectiveness on learning. Many educational institutions today have embarked in the development of web based courses. However, they face enormous difficulty in achieving successful strategies including the delivery, effec- tiveness, and acceptance of the courses. This is mainly due to the fact that the problem of developing a successful web based course involves multiple inter-related dimensions ranging from technology related issues to pedagogical considerations.