基于网络环境的综合实践活动初探——“心系奥运”案例研究

【案例背景】 随着网络技术的不断发展，人们的学习内容和学习方式也随之发生了巨大的变化。互联网上丰富的学习资源和便捷的信息传递与浏览方式使学生的学习具有更大的主动性，当学生在现实生活中无法获得更多的信息时，网络能够把学生带到更广阔的世界，给学生的学习提供帮助。     

基于网络学习学生信息素质的培养探索与实践

网络学习的实质是知识信息在网络平台上组织、传递、吸收、反馈的一个多向交互过程.参加网络学习的学生是否具备良好的信息素质直接决定着参加网络学习学生的学习能力.本文根据信息素质的定义,解析了信息素质的要素,结合网络学习学生的特点,提出培养基于网络学习学生信息素质的方法,为培养网络学生的信息素质指出了重要途径.     

用一首诗来开启物理课堂——基于网络的探究性学习活动设计一例

基于网络的探究性学习活动就是借助网络营造一个信息化的学习环境．充分发挥网络在收集信息、分析信息、共享信息、反馈信息和教学评价上的独特优势．充分发挥学生在学习活动中的主观能动性．以探究性的方式完成学习任务。目前．这种方式正在成为信息技术与物理课程整合研究中的一个热点问题．笔者分析具有以     

LFKT:学习与遗忘融合的深度知识追踪模型

知识追踪任务旨在根据学生历史学习行为实时追踪学生知识水平变化,并且预测学生在未来学习表现.在学生学习过程中,学习行为与遗忘行为相互交织,学生的遗忘行为对知识追踪影响很大.为了准确建模知识追踪中学习与遗忘行为,本文提出了一个兼顾学习与遗忘行为的深度知识追踪模型LFKT.LFKT模型综合考虑了四个影响知识遗忘因素,包括学生重复学习知识点的间隔时间、重复学习知识点的次数、顺序学习间隔时间以及学生对于知识点的掌握程度.结合遗忘因素,LFKT采用深度神经网络,利用学生答题结果作为知识追踪过程中知识掌握程度的间接反馈,建模融合学习与遗忘行为的知识追踪模型.通过在真实在线教育数据集上的实验,与当前知识追踪模型相比,LFKT可以更好地追踪学生知识掌握状态,并具有较好的预测性能.     

浅谈网络教学中学生学习能力的培养及教师的基本素养

网络教学与传统教学模式相比,更能培养学生信息获取、分析、加工、创新、利用、交流的能力。网络学习中学生该具有基本的计算机文化基础知识。教师该具有有效的教学方式和沟通方法。教师在网络教学中应该充当指导者而非表演者。网络教学能够培养学生良好的信息素养,把信息技术作为支持终身学习和合作学习的手段,为适应信息社会的学习、工作和生活打下必要的基础。     

融合IRT的图注意力深度知识追踪模型

知识追踪，旨在根据学生的历史答题表现实时追踪学生的知识状态(知识的掌握程度)并且预测学生未来的答题表现。目前的研究仅仅探索了问题或概念本身对学生答题表现的直接影响，而往往忽略了问题及包含的概念中存在的深层次信息对学生答题表现的间接影响。为了更好地利用这些深层次信息，一种融合项目反应理论的图注意力深度知识追踪模型GAKT-IRT被提出。模型将图注意力网络应用于知识追踪领域，取得了显著的提升效果，并使用IRT增加了模型的可解释性。首先，通过图注意力网络层获得问题的深层次特征表示；接着，根据结合了深层次信息的学生历史答题序列对学生的知识状态进行建模；然后，使用IRT对学生未来的答题表现进行预测。在6个公开真实在线教育数据集上的对比实验结果证明了，GAKT-IRT模型可以更好地完成知识追踪任务，在预测学生未来答题表现上具有明显的优势。     

网络教学让课堂焕发新活力

随着计算机和网络技术的不断发展,网络教学成为现代教育领域的一个热点。网络教学,通常采用的方式是建立相关主题学习网站,构建现实和虚拟相结合的学习情境,学生与教师在平台上实现基于网络的交互式学习,也包括利用网络进行信息的检索与搜集以及信息发布的学习活动。     

网络自主学习适应能力培养的实践研究

多媒体网络技术的应用有力地推动着各个学科的教学改革,网络环境下的自主学习是一种新的学习形式,它对学生的适应能力有很高的要求。该文采用实证法,剖析了学生在网络自主学习模式中的态度和看法,从教学实验中所出现的问题入手,利用形成性评价与终结性评价的方式,断裂学生在网络环境下积极、自主地索取信息的能力,最终形成自主学习的意识。     

多信息融合的轨迹追踪系统设计与实现

轨迹可以看做是对象随着时间变化在空间中留下的印迹.近年来,随着移动终端使用的普及以及生活的信息化,大量的轨迹数据在日常生活中日益积累并为不同的应用所服务.针对用户在移动社交网络以及校园信息化统一管理平台留下的位置痕迹信息,研究和开发了多信息融合的轨迹追踪系统Argo.Argo系统分析了微博、邮件、BBS、一卡通等应用层留下的位置痕迹信息,并结合覆盖校园的无线接入点,采用无线接入点被动定位获取用户位置,实现了多信息融合下的用户轨迹追踪.实验结果表明,该系统能够有效地实现轨迹追踪,并依此提供更好的服务.     

网络环境下大学生英语自主学习策略的研究

培养大学生自主学习英语的能力作为英语课程改革的一部分,对于改变学生的学习方式,培养学生的实践创新精神和提高学生的语言综合应用能力都具有十分重要的作用。随着现代信息技术的迅猛发展,网络技术在英语教育中的应用日益广泛和深入。但同时在网络环境下培养学生自主学习英语能力时也出现了一些问题,本文通过对大学生英语自主学习的状况的调查,提出问题,结合本校网络化英语教学实践来阐述在网络环境下学生自主学习英语能力的优劣,并从理论和实践层面上对英语自主学习进行深入研究。     

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.     

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.     

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.     

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.     

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。     

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.     

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.     

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。