A Four Dimensional Model of Formal and Informal Learning

Learning systems focused on collaborative learning are often described in terms of formal and informal learning, however definitions of formal and informal learning vary, which makes it difficult to compare systems that may have been described using different perspectives. In this paper we present a framework for describing formality in e-learning systems, which can account for the most common perspectives: formality focused on Learning Objective, Learning Environment, Learning Activity and/or Learning Tool. Our framework can be used to compare different e-learning systems, and can also describe collaborative systems where different students can take very different roles in the activity, and the degree of formality can vary according to the role

Design of a scrutable learning system

Personal Learning Environments (PLEs) refer to systems that allow individual learners to manage and control their own learning in their own space and at their own pace. In this work we explore the different ways in which a learning experience can be informal, and propose a 4D model of informal learning to characterise the informal aspects of a learning experience.The model includes dimensions for learning objectives, the learning environment, learning activities and learning tools, and reveals how much of the experience is really under the control of the learner. In an analysis of mobile tools presented in the mLearn 2008 conference we show that many emerging m-learning systems focused on informality in the environment dimension but not in the others.To solve this problem this report proposes a scrutable learning model approach that allows personal learners to take control of their learning objectives while still allowing the system to intelligently support them with appropriate learning activities and resources. In addition an experimental design is described based around a prototype of a scrutable learning system for mobile devices

Mobile VLE vs. Mobile PLE: How Informal is Mobile Learning?

Mobile Learning Systems are often described as supporting informal learning; as such they are a good fit to the idea of Personal Learning Environments (PLEs), software systems that users choose and tailor to fit their own learning preferences. This paper explores the question of whether existing m-learning research is more in the spirit of PLEs or Virtual Learning Environments (VLEs). To do this we survey the mobile learning systems presented at M-Learn 2007 in order to see if they might be regarded as informal or formal learning. In order to categorise the systems we present a four dimensional framework of formality, based on Learning Objective, Learning Environment, Learning Activity and Learning Tools. We use the framework to show that mobile systems tend to be informal in terms of their environment, but ignore the other factors. Thus we can conclude that despite the claims of m-learning systems to better support informal and personal learning, today’s m-learning research is actually more in the spirit of a VLE than a PLE, and that there remains a great deal of unexplored ground in the area of Mobile PLE systems

Using Hidden Markov Model for Stock Day Trade Forecasting

Around the world, the Hidden Markov Models (HMM) are the most popular methods in the machine learning and statistics for modeling sequences, especially in speech recognition domain. According to the number of patent applications for speech recognition technology form 1988 to 1998, the trend shows that this method has become very mature. In this thesis, we will make a new use of the HMM and apply it on day trading stock forecast. However, the HMM is based on probability and statistics theory. In a statistics framework, the HMM is a composition of two stochastic processes, a Hidden Markov chain, which accounts for temporal variability, and an observable process, which accounts for spectral variability. The combination contains uncertainly status just likes the stock walk trace. Therefore, the HMM and the stock walk trace have the same idea by coincidence. In this thesis, we will try to learn the stock syntax; just like how the HMM model was used in speech recognition in different languages, and the take the next step ahead in price prediction. Additionally, the stock market is the reflection of the economy. The stock trace is impacted by many factors such as policy, psychology, microeconomics, economics, and capital, etc. There, in this thesis, the TAIFEX Taiwan index futures (TX) and day trade are used to avoid all the uncertainty factors. After the all experiments, it is proven that the HMM is better than the benchmark methodRandom Walk method and the Investment Trust & Consulting Association method- Modified Trading method. Moreover, the result is very conspicuous by the statistics testing of significance

Theory Modeling and Empirical Evidence for Value-at-Risk based Assets Allocation Insurance Strategies

Constant Proportion Portfolio Insurance (CPPI) is the most popular portfolio insurance strategy using hedging strategy to protect principal while a wave upward or downward trend in the market is noted. Nevertheless, since the original CPPI was proposed, its performance has been limited to relevant parameters of strategy. And since there is no clear, definite and systematic rule of decision has get been proposed, it also has unstable performance and worse upside capture, especially for the multiplier (Mv) in model parameters, it has far great influence to end-of-period return. If Mv can be decided with its initial value setting and dynamic tuning via certain appropriate approach, under a decent mechanism of market timing selection, the strategy can therefore acquire excess return of min-max operation due to sharp improvement of upside capture, and also can provide hedging function within the insured volume when the market declines. This paper presents a systematic method using the value-at-risk control method to dynamically adjust the CPPI strategy parameter Mv, called asset allocation insurance strategy value-at-risk based asset allocation insurance strategy model (VALIS). We proof that the proposed model is a dynamic asset allocation insurance strategy, which is conservative but also aggressive; and shows that it is in compliance with the characteristics of idea portfolio insurance strategy, and is feasible and effective. From an empirical study of the Pan-Pacific market, we found that in any type of market or trend it is clearly better than the major benchmark indices, and it outperform other traditional portfolio insurance strategy

Towards A Holographic Model of D-Wave Superconductors

The holographic model for S-wave high T_c superconductors developed by Hartnoll, Herzog and Horowitz is generalized to describe D-wave superconductors. The 3+1 dimensional gravitational theory consists a symmetric, traceless second-rank tensor field and a U(1) gauge field in the background of the AdS black hole. Below T_c the tensor field which carries the U(1) charge undergoes the Higgs mechanism and breaks the U(1) symmetry of the boundary theory spontaneously. The phase transition characterized by the D-wave condensate is second order with the mean field critical exponent beta = 1/2. As expected, the AC conductivity is isotropic below T_c and the system becomes superconducting in the DC limit but has no hard gap.Comment: 14 pages, 2 figures, Some typos corrected, Matched with the published versio