基于Android的移动视频学习平台的构建

随着校园无线WiFi网络的不断完善和移动智能终端的迅速普及,越来越来的师生开始使用移动终端进行学习活动,为适应这种新型的学习方式,充分发挥已有教学视频资源的价值,我们设计开发了基于Android的移动视频学习平台,为师生提供了一个在校园内可以随时随地进行教学视频学习的平台。     

学习系统平台的设计与实现

以开源的Darodo5技术为基础,建立一个功能强大的网络学习系统平台,实现学校教学管理、数据、资源的统一、唯一及完全共享,引导实现教师教学和学生学习方式的变革。学习系统不单是实现了常规的远程教学和培训,而且是学校教学管理信息系统,为参与其中的师生提供服务,同时还提供对学习过程及教学资源的共享、设计、开发、运用、管理、评价及标准化。     

国网远程培训系统浅析

随着知识的不断更新,多媒体技术与网络技术的飞速发展,向传统教育提出了挑战,也为远程教育和终身学习的实现提供了可能,网络环境下的学习具有个性化、协同性、资源丰富等特点,有利于培养各公司部门管理人员的自主学习能力和创新精神。在分析我国现有网络教学平台的基础上,依据现代教学设计理论和学习理论提出了网络学习平台前台与后台功能模块设计与开发目标,分析了各模块的特点。并论述了网络学习人力资源支撑环境的组成。     

基于Android的在线考试练习系统设计与实现

在校大学生需要参加各类考试,如：课程考试、计算机等级考试、英语4/6级考试、教师资格考试以及考研等。目前针对在校学生的移动考试练习服务还不多见,为此设计并开发了一款基于Android平台的移动在线考试练习系统。将移动终端作为一种功能强大的教学工具,为大学生提供了一种自主学习的在线服务,使他们可以充分利用零碎时间,随时随地进行考前测试练习。     

基于教学平台的学习活动设计

教学平台是在已有教务管理系统基础上直接构建"一体化数字化教学支撑环境",能够为教师提供支持建构主义教学设计理念的学习活动。本文结合教学平台的教学功能,在活动理论和以学习活动为中心的教学设计理论指导下,探讨了基于教学平台的学习活动设计的模式与流程。     

基于AJAX和自动推理技术构建交互式数学Web服务

数学辅助学习平台一直是数学教育和计算机领域的研究热点。传统设计方法在教学互动、智能教育和界面友好性等环节存在若干制约因素。集成领先的AJAX思想和成熟的自动推理算法,提出新的数学Web服务系统设计方法,辅以MathML数学表示语言等多项先进技术,构建一个交互式数学Web服务学习平台。实际运行结果表明,平台具有良好的表现形式和运行性能,为使用者提供一个良好的交互式学习环境。     

远程开放教育平台的设计开发与实践探索

该文探讨了远程开放教育平台的资源建设、综合管理、学习评价、激励机制等方面的设计和开发,以实体授课与虚拟网络相结合的模式,形成以远程开放教育网为平台、各街镇社区学校为服务终端的远程开放教育网络格局。并在制定激励措施、开辟特色专栏、丰富学习资源、多终端学习、理顺运行机制、促进内涵发展等方面进行了实践探索,为远程开放教育平台的建设提供有益借鉴。     

基于WLAN技术的移动学习终端的设计与应用

移动学习的兴起为实现人类终身学习提供了可能。文章介绍了WLAN技术在移动学习领域的应用,利用嵌入式系统技术和无线网络技术,构建了一种移动学习终端的设计模型,为移动学习平台的开发提供参考。     

基于Pocket PC的移动学习平台及其关键技术研究

本文设计和实现了基于Pocket PC的移动学习平台,讨论系统开发中的关键技术问题.系统支持交互式学习和非交互式学习,以C#实现,并在一家国内领先的提供移动学习服务的公司的实施中取得了成功.     

可视化在高校在线教学系统数据挖掘中的应用研究

文献显示对在线教学平台进行数据挖掘可产生良好的作用。本研究依托一个高校在线教学平台的教学数据,针对该教学平台已有数据分析功能的缺陷,采用教育数据挖掘中的“统计和可视化”方法,开发了一套基于Springboot的在线学习服务可视化分析系统,通过可视化分析展示了该平台投入运行以来的整体情况,以期为教学管理部门提供较为可靠的决策参考。最后总结可视化在高校在线教学系统的教育数据挖掘中的应用方法,以及研究的局限性。     

Learning from a Population of Hypotheses

We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.     

On the Complexity of Function Learning

The majority of results in computational learning theory are concerned with concept learning, i.e. with the special case of function learning for classes of functions with range {0, 1}. Much less is known about the theory of learning functions with a larger range such as or . In particular relatively few results exist about the general structure of common models for function learning, and there are only very few nontrivial function classes for which positive learning results have been exhibited in any of these models.We introduce in this paper the notion of a binary branching adversary tree for function learning, which allows us to give a somewhat surprising equivalent characterization of the optimal learning cost for learning a class of real-valued functions (in terms of a max-min definition which does not involve any learning model).Another general structural result of this paper relates the cost for learning a union of function classes to the learning costs for the individual function classes.Furthermore, we exhibit an efficient learning algorithm for learning convex piecewise linear functions from ^d into . Previously, the class of linear functions from ^d into was the only class of functions with multidimensional domain that was known to be learnable within the rigorous framework of a formal model for online learning.Finally we give a sufficient condition for an arbitrary class of functions from into that allows us to learn the class of all functions that can be written as the pointwise maximum ofk functions from . This allows us to exhibit a number of further nontrivial classes of functions from into for which there exist efficient learning algorithms.     

Transfer in variable-reward hierarchical reinforcement learning

Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across different SMDPs.     

The Power of Self-Directed Learning

This article studies self-directed learning, a variant of the on-line (or incremental) learning model in which the learner selects the presentation order for the instances. Alternatively, one can view this model as a variation of learning with membership queries in which the learner is only charged for membership queries for which it could not predict the outcome. We give tight bounds on the complexity of self-directed learning for the concept classes of monomials, monotone DNF formulas, and axis-parallel rectangles in {0, 1, , n – 1} ^d . These results demonstrate that the number of mistakes under self-directed learning can be surprisingly small. We then show that learning complexity in the model of self-directed learning is less than that of all other commonly studied on-line and query learning models. Next we explore the relationship between the complexity of self-directed learning and the Vapnik-Chervonenkis (VC-)dimension. We show that, in general, the VC-dimension and the self-directed learning complexity are incomparable. However, for some special cases, we show that the VC-dimension gives a lower bound for the self-directed learning complexity. Finally, we explore a relationship between Mitchell's version space algorithm and the existence of self-directed learning algorithms that make few mistakes.     

Toward Efficient Agnostic Learning

In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables.     

一种新的连续动作集学习自动机

学习自动机（Learning automation,LA）是一种自适应决策器。其通过与一个随机环境不断交互学习从一个允许的动作集里选择最优的动作。在大多数传统的LA模型中,动作集总是被取作有限的。因此,对于连续参数学习问题,需要将动作空间离散化,并且学习的精度取决于离散化的粒度。本文提出一种新的连续动作集学习自动机（Continuous action set learning automaton,CALA）,其动作集为一个可变区间,同时按照均匀分布方式选择输出动作。学习算法利用来自环境的二值反馈信号对动作区间的端点进行自适应更新。通过一个多模态学习问题的仿真实验,演示了新算法相对于3种现有CALA算法的优越性。     

Exploring learnability between exact and PAC

We study a model of probably exactly correct (PExact) learning that can be viewed either as the Exact model (learning from equivalence queries only) relaxed so that counterexamples to equivalence queries are distributionally drawn rather than adversarially chosen or as the probably approximately correct (PAC) model strengthened to require a perfect hypothesis. We also introduce a model of probably almost exactly correct (PAExact) learning that requires a hypothesis with negligible error and thus lies between the PExact and PAC models. Unlike the Exact and PExact models, PAExact learning is applicable to classes of functions defined over infinite instance spaces. We obtain a number of separation results between these models. Of particular note are some positive results for efficient parallel learning in the PAExact model, which stand in stark contrast to earlier negative results for efficient parallel Exact learning.     

不同程度的监督机制在自动文本分类中的应用

自动文本分类技术涉及信息检索、模式识别及机器学习等领域。本文以监督的程度为线索，综述了分属全监督，非监督以及半监督学习策略的若干方法-NBC(Naive Bayes Classifier)，FCM(Fuzzy C-Means)，SOM(Self-Organizing Map)，ssFCM(serni-supervised Fuzzy C-Means)gSOM(guided Self-Organizing Map)，并应用于文本分类中。其中，gSOM是我们在SOM基础上发展得到的半监督形式。并以Reuters-21578为语料，研究了监督程度对分类效果的影响，从而提出了对实际文本分类工作的建议。     

Context counts: How learners' contexts influence learning in a MOOC

Massive Open Online Courses (MOOCs) require individual learners to self-regulate their own learning, determining when, how and with what content and activities they engage. However, MOOCs attract a diverse range of learners, from a variety of learning and professional contexts. This study examines how a learner's current role and context influences their ability to self-regulate their learning in a MOOC: Introduction to Data Science offered by Coursera. The study compared the self-reported self-regulated learning behaviour between learners from different contexts and with different roles. Significant differences were identified between learners who were working as data professionals or studying towards a higher education degree and other learners in the MOOC. The study provides an insight into how an individual's context and role may impact their learning behaviour in MOOCs.     

Indexing,Elaboration and Refinement: Incremental Learning of Explanatory Cases

This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good lessons to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.