一种形式化的学习过程建模

目前对E-learning技术进行深入的研究对于丰富我国教育方式和提高总体教育水平有着重要的意义。学习过程的建模则是为实现学习管理系统,将E-learning的相关理念运用于实际,打下坚实的理论基础。本文首先介绍了学习设计理念以及Petri网的概念。其次,依次描述了学习活动和活动构件、技能、学习过程的建模过程,并在在此基础上补充了活动结构的概念。最后,对学习过程建模在学习管理系统中的应用提出了展望。     

The possible and the impossible in multi-agent learning

This paper surveys recent work on learning in games and delineates the boundary between forms of learning that lead to Nash equilibrium, and forms that lead to weaker notions of equilibrium or to none at all.     

面向不平衡数据流的自适应加权在线超限学习机算法

一般的在线学习算法对不平衡数据流的分类识别会遇到较大困难,特别是当数据流发生概念漂移时,对其进行分类会变得更困难.文中提出面向不平衡数据流的自适应加权在线超限学习机算法,自动调整实时到达的训练样本的惩罚参数,达到在线学习不平衡数据流的目的.文中算法可以适用于不同偏斜程度的静态数据流的在线学习和发生概念漂移时数据流的在线学习.理论分析和在多个真实数据流上的实验表明文中算法的正确性和有效性.     

An algorithmic theory of learning: Robust concepts and random projection

We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that each example contains a huge amount of information? We provide a novel algorithmic analysis via a model of robust concept learning (closely related to “margin classifiers”), and show that a relatively small number of examples are sufficient to learn rich concept classes. The new algorithms have several advantages—they are faster, conceptually simpler, and resistant to low levels of noise. For example, a robust half-space can be learned in linear time using only a constant number of training examples, regardless of the number of attributes. A general (algorithmic) consequence of the model, that “more robust concepts are easier to learn”, is supported by a multitude of psychological studies.     

Learning Fallible Deterministic Finite Automata

We consider the problem of learning from a fallible expert that answers all queries about a concept, but often gives incorrect answers. The expert can also be thought of as a truth table describing the concept which has been partially corrupted. In order to learn the underlying concept with arbitrarily high precision, we would like to use its structure in order to correct most of the incorrect answers. We assume that the expert's errors are uniformly and independently distributed, occur with any fixed probability strictly smaller than 1/2, and are persistent. In particular, we present a polynomial time algorithm using membership queries for correcting and learning fallible Deterministic Finite Automata under the uniform distribution.     

个性化的远程学习模型

随着互联网应用的发展,人们越来越多地把学习过程放到网络上来,而不再局限于传统的教室学习,因此基于Ｗｅｂ的远程学习得到了迅速推广。但是由于受教育对象之间存在着鲜明的个性特征差异,而目前存在的远程教育系统模式单一,学生只能被动地接受完全相同的学习内容,而不能主动地根据自己的需要选择、调度、控制自己的学习过程,这样,再丰富的教育资源也未能得到充分利用。为了从根本上解决这一矛盾,提高教育信息资源的利用度,使学生在学习过程中变被动为主动,实现因材施教,文章结合个性化服务的思想,提出个性化学习的概念,并系统地叙述了个性化的远程学习模型ＰＤＬＭ的原理和构成,给出了实现ＰＤＬＭ系统的具体思路和解决方案。个性化的远程学习将成为远程教育应用的新模式。     

Some single-machine and m-machine flowshop scheduling problems with learning considerations

Scheduling with learning effect has drawn many researchers’ attention since Biskup [D. Biskup, Single-machine scheduling with learning considerations, European Journal of Opterational Research 115 (1999) 173-178] introduced the concept of learning into the scheduling field. Biskup [D. Biskup, A state-of-the-art review on scheduling with learning effect, European Journal of Opterational Research 188 (2008) 315-329] classified the learning approaches in the literature into two main streams. He claimed that the position-based learning seems to be a realistic model for machine learning, while the sum-of-processing-time-based learning is a model for human learning. In some realistic situations, both the machine and human learning might exist simultaneously. For example, robots with neural networks are used in computers, motor vehicles, and many assembly lines. The actions of a robot are constantly modified through self-learning in processing the jobs. On the other hand, the operators in the control center learn how to give the commands efficiently through working experience. In this paper, we propose a new learning model that unifies the two main approaches. We show that some single-machine problems and some specified flowshop problems are polynomially solvable.     

浅析网络环境下的协作学习

随着信息技术的发展，网络为教育带来了新的生机与活力，网络环境成为学习者学习的重要环境之一。如何有效地利用网络环境进行协作学习、提高协人学习的效率，成为理论研究界研究的热点。本文结合笔者的教学实践和研究，从协作学习的概念和特点入手，分析了网络环境下协作学习的组织模型、资源设计与环境设计、学习过程中教师角色的转变和作用及其评价方法与实施，最后提出了目前研究中存在的几个问题。     

Online Learning versus Offline Learning

We present an off-line variant of the mistake-bound model of learning. This is an intermediate model between the on-line learning model (Littlestone, 1988, Littlestone, 1989) and the self-directed learning model (Goldman, Rivest & Schapire, 1993, Goldman & Sloan, 1994). Just like in the other two models, a learner in the off-line model has to learn an unknown concept from a sequence of elements of the instance space on which it makes guess and test trials. In all models, the aim of the learner is to make as few mistakes as possible. The difference between the models is that, while in the on-line model only the set of possible elements is known, in the off-line model the sequence of elements (i.e., the identity of the elements as well as the order in which they are to be presented) is known to the learner in advance. On the other hand, the learner is weaker than the self-directed learner, which is allowed to choose adaptively the sequence of elements presented to him.We study some of the fundamental properties of the off-line model. In particular, we compare the number of mistakes made by the off-line learner on certain concept classes to those made by the on-line and self-directed learners. We give bounds on the possible gaps between the various models and show examples that prove that our bounds are tight.Another contribution of this paper is the extension of the combinatorial tool of labeled trees to a unified approach that captures the various mistake bound measures of all the models discussed. We believe that this tool will prove to be useful for further study of models of incremental learning.     

新时代下的学习新模式-泛在学习

泛在学习作为一种新型的学习模式,它基于高速移动通信网路技术,通过无线便携设备获取教育信息,学习资源和教育服务.本文介绍了泛在学习的基本概念,讨论了其实现条件和方式以及优势和局限,在此基础上展望泛在学习的未来.     

新时代下的学习新模式——泛在学习

泛在学习作为一种新型的学习模式,它基于高速移动通信网路技术,通过无线便携设备获取教育信息,学习资源和教育服务。本文介绍了泛在学习的基本概念,讨论了其实现条件和方式以及优势和局限,在此基础上展望泛在学习的未来。     

建立以人为本的虚拟学习社区

随着网络信息化的高度发展,虚拟学习社区已经广泛地运用于人们的学习中。当前,探讨虚拟学习社区在实际应用中存在的问题、以及如何建立有效的虚拟学习社区将对网络学习具有重要的意义。该文从概念出发,讨论目前虚拟学习社区中实际存在的一些问题,利用虚拟学习社区的特点,提出了要本着以人为本的科学发展观为原则,建立和谐、平等、有效的虚拟学习环境,使学习者能时时、处处高效地学习,从而促进学习者的全面发展。     

Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques

The portability and immediate communication properties of mobile devices influence the learning processes in interacting with peers, accessing resources and transferring data. For example, the short message and browsing functions in a cell phone provide users with timely and adaptive information access. Although many studies of mobile learning indicate the pedagogical potential of mobile devices, the screen size, computational power, battery capacity, input interfaces, and network bandwidth are too restricted to develop acceptable functionality for the entire learning processes in a handheld device. Therefore, mobile devices can be adopted to fill the gap between Web-based learning and ubiquitous mobile learning. This study first creates a website, providing functions enabling learning to take place anytime and anywhere with any available learning device, for ubiquitous learning according to various properties of mobile devices. Nowadays, learners’ behaviors on a website can be recorded as learning portfolios and analyzed for behavioral diagnosis or instructional planning. A student model is then built according to the analytical results of learning portfolios and a concept map of the learning domain. Based on the student model and learners’ available learning devices, three modules are developed to build a ubiquitous learning environment to enhance learning performance via learning status awareness, schedule reminders and mentor recommendation. Finally, an experiment is conducted with 54 college students after implementation of the ubiquitous learning website. Experimental results indicate that the proposed system can enhance three learning performance indicators, namely academic performance, task accomplishment rates, and learning goals achievement rates.     

试论网络环境下的研究性学习

研究性学习作为一种国际课程改革的共同趋势,是当前基础教育课程改革中出现的一种新理念,成为人们越来越广泛关注的焦点话题.本文从界定研究性学习的概念和内涵出发,综述了在网络环境下开展研究性学习的方法,并提出了当前研究过程中存在的一些问题.     

An examination of children's learning progression shifts while using touch screen virtual manipulative mathematics apps

The purpose of this study was to examine shifts in young children's learning progression levels while they interacted with virtual manipulative mathematics apps on touch-screen devices. A total of 100 children participated in six mathematics learning sequences while using 18 virtual manipulative mathematics touch-screen apps during clinical interviews. Researchers developed a micro-scoring tool to analyze video data from two camera sources (i.e., GoPro camera, wall-mounted camera). Our results showed that it is possible to document evidence of shifts in children's learning progressions while they are interacting with mathematics apps on touch-screen devices. Our results also indicated patterns in the children's interactions that were related to the shifts in their learning progression levels. These results suggest that an open-ended number of tasks with a variety of representations and tasks at varying levels of difficulty led to children refining their understanding and shaping their concept image of mathematical ideas resulting in incremental shifts in learning. The results of this study have important implications about how mathematical tasks in touch-screen apps may prompt children's incremental learning progression shifts to occur, and thereby promote opportunities for learning. We propose that design features in mathematics apps can be created to support and encourage these learning shifts.     

A weight adaptation method for fuzzy cognitive map learning

Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial experts beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.     

高职计算机专业探究式学习实施策略的研究

本文从探究式学习的概念、基本特征出发,得出了探究式学习的核心要素是"问题",提出了高职院校计算机专业实施探究式学习应采取的策略,对促进新时期高职教育课程改革和提升高职院校计算机专业教育水平有着重要意义。     

Limiting Behaviour in Parameter Optimal Iterative Learning Control

This paper analyses the concept of a Limit Set in Parameter Optimal Iterative Learning Control (ILC). We investigate the existence of stable and unstable parts of Limit Set and demonstrates that they will often exist in practice. This is illustrated via a 2-dimensional example where the convergence of the learning algorithm is analyzed from the error's dynamic behaviour. These ideas are extended to the AT-dimensional cases by analogy and example.     

模糊形态学双向联想记忆网络的性质

为模糊形态学双向联想记忆网络(FMBAM)提出一个学习算法。在理论上证明只要存在使给定的模式对集合成为FMBAM的平衡态集合,则该学习算法总能计算出相应的最大连接权矩阵对。该最大连接权矩阵对能使FMBAM对任意输入在一步内就进入平衡态,并且神经网络全局收敛到平衡态。FMBAM的每个平衡态都是Lyapunov稳定的。当训练模式存在摄动时,利用该学习算法训练的FMBAM,对训练模式摄动拥有好的鲁棒性。