Not everybody needs help to seek help: Surprising effects of metacognitive instructions to foster help-seeking in an online-learning environment

Offering help functions is a standard feature of computer-based interactive learning environments (ILE). Nevertheless, a number of recent studies indicate that learners are not using such help facilities effectively. We compared the effects of different metacognitive supports to foster learners’ help-seeking behavior in an ILE for plant identification. Four groups of university students (n = 51), each receiving a different metacognitive instruction, had to determine living plants. They had to think-aloud and were video recorded during the experiment. At the end of the session they completed a knowledge test. The surprising effect was that students in all groups were effective help-seekers. They adapted their help-seeking behavior to the complexity of the plants in an effective way. The results indicate that for students on university level effective help-seeking seems to depend largely on motivational factors.     

行为特征分析模式下的网页分类技术研究

现有网页分类技术忽略用户个性行为的差异.为此,提出一种结合用户行为特征分析的网页分类技术.运用知识规则发现、页面特征提取等方法,分析Web用户的访问历史和个性化定制信息,学习并掌握用户的行为和兴趣.针对用户的认知特征,提供合适的Web页面分类模式,能在一定程度上改进单纯统计学网页分类方法在自然语言理解上的不足.实验数据表明,该分类方法与多种统计学方法相结合实施网页分类均能有效地提高分类准确率,使网页分类结果更接近分类的真实情形和要求.     

Determining students’ level of page viewing in intelligent tutorial systems with artificial neural network

The concept of level of page viewing (LPV) refers to the extent to which a student actively revises the pages that he or she has to study in tutorial systems. In the present study, an artificial neural network (ANN) model, which is composed of 5 inputs, 20 and 30 neurons, 2 hidden layers, and 1 output, was designed to determine the students’ LPV. After this network was trained, it was integrated into a web-based prototype teaching system, which was developed by ASP.net C# programming language. Additionally, Decision Tree method is tried to determine students’ LPV. However, this method gave wrong results according to expected LPV values. In this system, the student first studies the pages uploaded by the teacher onto the system. After studying all the pages within the scope of a topic, the student can go to the test page for evaluation purposes. LPVs of a student who wants to navigate to the test page are calculated by an ANN module added to the system. On the condition that one or more of the LPV’s are not up to the desired level, the student is not allowed to take the test and is informed of the pages with missing LPV’s so that he can re-study these pages. This prototype system developed based on ANN to determine students’ LPV is essential for intelligent tutorial systems, geared to provide intelligent assistance and guidance. The system can track the pages which the students did not study sufficiently and thus direct them to relevant pages. How much activity the students perform on each page to study is observed before they actually take the test, and the areas which should be further revised are determined much in advance.     

Order and delay selection for neural network modelling by identification of linearized models

An approach to selecting the order and delay for neural network modelling of nonlinear dynamic systems is proposed by identifying local linear models at points spanning the system operating range. The method is based on relationships between linear and nonlinear models and is developed for three popular nonlinear model structures; nonlinear autoregressive with exogenous inputs (NARX), NARX with a linear noise model and nonlinear autoregressive moving-average with exogenous inputs (NARMAX). Simulation results illustrate the application of the method, and the suitability of the orders and delays selected are demonstrated by nonlinear system identification using radial basis function neural networks. The method is also shown to indicate the suitability of a particular nonlinear model structure for representing a system.     

Analysis and modeling of the semantically associated network on the Web

The semantically associated network on the Web is a Semantic Link Network built by mining the associated relation between Web pages. The associated link from page A to page B indicates that users who have browsed page A is likely to also browse page B. This paper explores the statistical properties of the associated network on the Web. Web pages of a specific domain are automatically downloaded by a Web crawler to build an associated network. We analyze the associated network at different domain thresholds and classify the topology into three states, that is, the original state, the kernel state and the final state. A mathematical model is built to study the in‐degree distribution, the out‐degree distribution and the total‐degree distribution for both the kernel state and the final state. By tuning the model parameters to reasonable values, we obtain the distinct power‐law forms for the three degree distributions with exponents that agree well with the statistical data. The proposed model can not only describe the evolving processes of the associated network on the Web, but also provides theory basis for complex applications such as semantic community discovery, intelligent browsing and recommendation. Copyright © 2009 John Wiley & Sons, Ltd.     

Design a personalized e-learning system based on item response theory and artificial neural network approach

In web-based educational systems the structure of learning domain and content are usually presented in the static way, without taking into account the learners’ goals, their experiences, their existing knowledge, their ability (known as insufficient flexibility), and without interactivity (means there is less opportunity for receiving instant responses or feedbacks from the instructor when learners need support). Therefore, considering personalization and interactivity will increase the quality of learning. In the other side, among numerous components of e-learning, assessment is an important part. Generally, the process of instruction completes with the assessment and it is used to evaluate learners’ learning efficiency, skill and knowledge. But in web-based educational systems there is less attention on adaptive and personalized assessment. Having considered the importance of tests, this paper proposes a personalized multi-agent e-learning system based on item response theory (IRT) and artificial neural network (ANN) which presents adaptive tests (based on IRT) and personalized recommendations (based on ANN). These agents add adaptivity and interactivity to the learning environment and act as a human instructor which guides the learners in a friendly and personalized teaching environment.     

A Multimethod Evaluation of Online Trust and Its Interaction with Metacognitive Awareness: An Emotional Design Perspective

Despite the rapidly increasing numbers of informational websites and learners using the World Wide Web to research topics, empirical evidence on the relationships between website design elements and website trustworthiness is scarce. This study used self-reported and behavioral screen-capture data to investigate the impact of Don Norman's (2003) emotional design levels and metacognitive awareness on website trustworthiness during an information search learning task. The results suggest that the interaction effects of website visual appeal (visceral level) and website usability (behavioral level) can override the effects of the quality or relevance of the information (reflective level) on website evaluation. In addition, in the context of limited time to find the answers, these effects on the evaluation of website trustworthiness are not moderated by users’ metacognitive awareness.     

基于用户反馈的搜索引擎排名算法

以Web 2.0中用户行为作为研究对象,通过发掘用户反馈方式,提出用户反馈分值的概念,对用户反馈影响搜索结果排名的具体方法以及相应实现进行研究,提出了一种基于神经网络的网页排序算法。该算法引入BP神经网络模型,根据用户反馈分值选择样本训练神经网络。将传统搜索结果输入到经过训练的神经网络进行计算,根据计算出的结果所表示的网页相关性强弱判断后进行二次排序。该算法利用了神经网络具有的模式识别能力,有效地将用户反馈和搜索引擎结合起来,使得搜索结果更加符合用户的搜索要求。     

Faster Web page allocation with neural networks

To maintain quality of service, some heavily trafficked Web sites use multiple servers, which share information through a shared file system or data space. The Andrews file system (AFS) and distributed file system (DFS), for example, can facilitate this sharing. In other sites, each server might have its own independent file system. Although scheduling algorithms for traditional distributed systems do not address the special needs of Web server clusters well, a significant evolution in the computational approach to artificial intelligence and cognitive engineering shows promise for Web request scheduling. Not only is this transformation - from discrete symbolic reasoning to massively parallel and connectionist neural modeling - of compelling scientific interest, but also of considerable practical value. Our novel application of connectionist neural modeling to map Web page requests to Web server caches maximizes hit ratio while load balancing among caches. In particular, we have developed a new learning algorithm for fast Web page allocation on a server using the self-organizing properties of the neural network (NN).     

Not everybody needs help to seek help: Surprising effects of metacognitive instructions to foster help-seeking in an online-learning environment

Offering help functions is a standard feature of computer-based interactive learning environments (ILE). Nevertheless, a number of recent studies indicate that learners are not using such help facilities effectively. We compared the effects of different metacognitive supports to foster learners’ help-seeking behavior in an ILE for plant identification. Four groups of university students (n = 51), each receiving a different metacognitive instruction, had to determine living plants. They had to think-aloud and were video recorded during the experiment. At the end of the session they completed a knowledge test. The surprising effect was that students in all groups were effective help-seekers. They adapted their help-seeking behavior to the complexity of the plants in an effective way. The results indicate that for students on university level effective help-seeking seems to depend largely on motivational factors.     

语言先验知识对神经网络模型自然语言处理任务的影响

随着互联网的发展及硬件的更新,神经网络模型被广泛应用于自然语言处理、图像识别等领域。目前,结合传统自然语言处理方法和神经网络模型正日益成为研究的热点。引入先验知识代表了传统方法的惯例,然而它们对基于神经网络模型的自然语言处理任务的影响尚不清楚。鉴于此,该文尝试探究语言层先验知识对基于神经网络模型的若干自然语言处理任务的影响。根据不同任务的特点,比较了不同先验知识和不同输入位置对不同神经网络模型的影响。通过大量的对比实验发现: 先验知识并不是对所有任务都适用,在神经网络模型的合适位置加入合适的先验知识方可加快模型的收敛速度,提高相关任务的效果。     

智能专题化信息搜集Crawler

介绍了基于Web内容和结构挖掘的专题化智能Web爬行Crawler系统,并重点介绍其中CA（C＆S）算法,该算法充分利用神经网络可以方便地模拟网络的拓扑结构和并行计算的特点,采用加强学习判断网页与主题的相关度,在进行相关度计算时,不考虑网页的全部内容,而通过提取网页的HTML描述中的重要标记,对Web网页进行内容和结构分析,从而判断爬行到的网页与主题的相关性,以提高信息搜集的效率和精确性。     

基于注意力机制和集成学习的网页黑名单判别方法

搜索引擎作为互联网主要应用之一,能够根据用户需求从互联网资源中检索并返回有效信息。然而,得到的返回列表往往包含广告和失效网页等噪声信息,而这些信息会干扰用户的检索与查询。针对复杂的网页结构特征和丰富的语义信息,提出了一种基于注意力机制和集成学习的网页黑名单判别方法,并采用本方法构建了一种基于集成学习和注意力机制的卷积神经网络（EACNN）模型来过滤无用的网页。首先,根据网页上不同种类的HTML标签数据,构建多个基于注意力机制的卷积神经网络（CNN）基学习器;然后,采用基于网页结构特征的集成学习方法对不同基学习器的输出结果执行不同的权重计算,从而实现EACNN的构建;最后,将EACNN的输出结果作为网页内容分析结果,从而实现网页黑名单的判别。所提方法通过注意力机制来关注网页语义信息,并通过集成学习的方式引入网页结构特征。实验结果表明,与支持向量机（SVM）、K近邻（KNN）、CNN、长短期记忆（LSTM）网络、GRU、结合注意力机制的卷积神经网络（ACNN）等基线模型相比,所提模型在所构建的面向地理信息领域的判别数据集上具有最高的准确率（0.97）、召回率（0.95）和F₁分值（0.96）,验证了EACNN在网页黑名单判别工作中的优势。     

基于带权语言网络的网页关键词抽取

论述了网页文档带权语言网络的建立过程,给出了结合介数指标与紧密度指标的词语综合中心度度量方法,实验表明采用该方法的关键词抽取结果能够很好地符合网页主题。     

基于语义的中文网页检索

用户期望搜索引擎能提供基于语义的网页信息检索。基于本体、基于自然语言理解、基于文本统计分析的方法是实现中文网页语义检索的主要途径。分析了它们的实现方法、技术挑战和优、缺点;建议中文网页语义检索系统的开发应选择与普通用户联系紧密的应用领域,并以汉语词汇为索引单元,适量地采用中文信息处理技术。基于语义的中文网页检索应在以下方面加强研究:语义相关性评价方法、本体构建和实体抽取算法、基于语义的索引、大规模语义标注样本集开发等。     

EduMiner: Using text mining for automatic formative assessment

Formative assessment and summative assessment are two widely accepted approaches of assessment. While summative assessment is a typically formal assessment used at the end of a lesson or course, formative assessment is an ongoing process of monitoring learners’ progresses of knowledge construction. Although empirical evidence has acknowledged that formal assessment is indeed superior to summative assessment, current e-learning assessment systems however seldom provide plausible solutions for conducting formative assessment. The major bottleneck of putting formative assessment into practice lies in its labor-intensive and time-consuming nature, which makes it hardly a feasible way of achievement evaluation especially when there are usually a large number of learners in e-learning environment. In this regard, this study developed EduMiner to relieve the burdens imposed on instructors and learners by capitalizing a series of text mining techniques. An empirical study was held to examine effectiveness and to explore outcomes of the features that EduMiner supported. In this study 56 participants enrolling in a “Human Resource Management” course were randomly divided into either experimental groups or control groups. Results of this study indicated that the algorithms introduced in this study serve as a feasible approach for conducting formative assessment in e-learning environment. In addition, learners in experimental groups were highly motivated to phrase the contents with higher-order level of cognition. Therefore a timely feedback of visualized representations is beneficial to facilitate online learners to express more in-depth ideas in discourses.     

基于学习自动机和用户兴趣的PageRank算法研究

针对传统PageRank算法存在的平分链接权重和忽略用户兴趣等问题,提出一种基于学习自动机和用户兴趣的页面排序算法LUPR。在所提方法中,给每个网页分配学习自动机,其功能是确定网页之间超链接的权重。通过对用户行为进一步分析,以用户的浏览行为衡量用户对网页的兴趣度,从而获得兴趣度因子。该算法根据网页间的超链接和用户对网页的兴趣度衡量网页权重计算每个网页的排名。最后的仿真实验表明,较传统的PageRank算法和WPR算法,改进后的LUPR算法在一定程度上提高了信息检索的准确度和用户满意度。     

一种基于自组织映射神经网络的Web页面个性化推荐模型

人工神经网络(ANN)为Web挖掘技术提供了一种新思路.针对传统聚类方法面对大量动态增长的Web日志数据健壮性和灵活性不够的缺点,提出了一种基于自组织映射神经网络的用户信息聚类模型,最后给出的实例证明在此基础上的Web页面的个性化推荐算法能够有效地进行Web页面个性化推荐.     

基于构件的Web系统前馈神经网络可靠性模型

对基于构件结构复杂度较高的Web系统进行可靠性评估时,基于状态或基于路径的软件可靠性评估模型计算复杂度较高,鲁棒性不足。为此,提出了一种计算复杂度低、鲁棒性强的基于构件的前馈神经网络可靠性模型CBPRM。CBPRM将Web系统中各构件的可靠性作为前馈神经网络输入,并基于构件可靠性敏感度对神经元进行动态优化,Web系统可靠性评估由前馈神经网络输出实现。理论分析和实验结果表明,在基于构件结构复杂度较高的Web系统可靠性评估中,CBPRM的计算复杂度低于对比模型,并可确保可靠性评估精度。     

手机广告推荐中的用户兴趣建模研究*

为了增强基于WAP网页的手机广告推荐中用户建模的准确性,并对"非邀"式广告推荐中脱离用户兴趣试探性推荐进行修正,针对手机广告推荐中手机屏幕小、用户注意力集中等特点,根据用户对广告的访问历史和操作模式建立其广告兴趣模型和非兴趣模型,同时分析用户网页访问模式探测其网页兴趣度,在此基础上建立用户综合兴趣模型。分别采用基于网页兴趣模型、基于广告兴趣模型和基于用户综合兴趣模型进行广告推荐,随着样本空间增大,综合兴趣模型的查准率明显优于另两者。实验验证了用户综合兴趣模型在手机广告推荐中的有效性和优越性。