Beyond intelligent tutoring systems: Situations,interactions, processes and affordances

The idea of an intelligentsystem to support learning has been epitomisedby that of an intelligent tutoring system(ITS). However, ITSs are, in fact, just aparticular kind of intelligent system tosupport learning whose components reflect thevalues of the particular view that ITSsemphasise in regard to the nature of knowledge,learning and teaching, which have led to anarchitecture that focuses on representing theknowledge to be learned (domain model),inferring the learner's knowledge (learnermodel), and planning instructional steps to thelearner (teaching model). On the other hand,other views of learning may lead to differentneeds in terms of knowledge representation,reasoning, and decision making capabilities inthe intelligent systems that support them.Constructivist views, for example, emphasisedifferent values and may require an entirelydifferent architecture of intelligent system tosupport its philosophy of learning. This paperpresents an architecture of an intelligentsystem to support learning that is able toaddress the issues that arise fromconstructivist theories of learning in a waythat, rather than opposing to the standard ITSarchitecture, characterises a broader view inall its components which can be appropriatelyattuned to address the issues of particularphilosophies.     

Beyond intelligent tutoring systems: Using computers as METAcognitive tools to enhance learning?

Framed by the existing theoretical andempirical research on cognitive and intelligenttutoring systems (ITSs), this commentaryexplores two areas not directly or extensivelyaddressed by Akhras and Self (this issue). Thefirst area focuses on the lack of conceptualclarity of the proposed constructivist stanceand its related constructs (e.g., affordances,situations). Specifically, it is argued that aclear conceptualization of the novelconstructivist stance needs to be delineated bythe authors before an evaluation of theirambitious proposal to model situationscomputationally in intelligent learningenvironments (ILEs) can be achieved. The secondarea of exploration deals with the similaritiesbetween the proposed stance and existingapproaches documented in the cognitive,educational computing, and AI in educationliterature. I believe that the authors are at acrossroads, and that their article presents aninitial conceptualization of an important issuerelated to a constructivist-based approach tothe computational modeling of situations inILEs. However, conceptual clarity isdefinitively required in order for theirapproach to be adequately evaluated and used toinform the design of ILEs. As such, I invitethe authors to re-conceptualize their frameworkby addressing how their constructivist stancecan be used to address a particular researchagenda on the use of computers as metacognitivetools to enhance learning.     

基于本体的智能学习系统的架构设计

目前的学习系统只能机械地呈现给学习者一些学习材料,而不能满足学习者个性化学习的需求,不能根据学生认知模型智能地组织符合学习者特点和需求的教学内容和教学策略．将本体运用到智能学习系统中,是一种基于本体的智能学习系统的系统架构,对系统的组成部分进行详细描述,以帮助学习者更好定位学习内容．     

高校智慧学习空间:内涵、发展与趋势

新媒体新技术支持下的高校各学科教与学的变革越发受到重视,作为教学载体的学习空间从传统型向智慧型的演化成为人们的关注焦点,是未来学习环境改革的重要方向。以对现有相关文献和应用实例的深入分析为基础,总结并提出了高校智慧学习空间的特征与结构模型;采用关键词共现分析等方法,对近年来高校智慧学习空间的建设与发展开展了探究;剖析了高校智慧学习空间在实际建设中存在的一些问题,并预测了其发展趋势。     

智能导学系统在远程教育中的应用研究

将智能导学系统应用于现代远程教育中,系统运用数据挖掘技术对学习者访问网络记录进性分析,提取出学习者的学习过程以及对知识点的掌握情况,从而针对学习者个体评价学习效果、优化学习方案、以及督促和鞭策学习者学习,真正实现个性化智能导学。     

学生智慧学习能力内涵及评价研究

学生智慧学习能力评价是指诊断学生是否具备了个性化深度学习的综合能力,并是否促进了学生智慧能力的养成。文章基于对学生智慧学习能力内涵的辨析,依据布鲁姆教学目标分类学说确定了学生智慧学习能力评价的核心要素,并在此基础上构建了学生智慧学习能力评价指标体系,以期为智慧学习能力的评价提供参考。     

Imagining, seeking, inventing: the future of learning and the emerging discovery networks

Extrapolating from current developments in the study of learning and imagining how learning might be shaped moving forward, this article considers 12 trends concerning the future of learning. Learning will become more diverse, more contextual, less discipline-bound, and less institutionally-bound. It will span professional and institutional sectors, and move beyond national borders. It will move increasingly online and extend beyond humans to encompass machines and machine/human blends. It will become more interactive, more distributed, and more biologically connected. Drawing on an understanding of these trends, new roles for teachers and for educational institutions are developed. The result is that learning is likely to occur through multiple discovery networks that blend research and teaching to address real world problems in environments supported by robust software infrastructures. Multiple nested discovery networks will operate on a global scale and be negotiated by individual learners sometimes guided by teachers. Prediction is very difficult, especially about the future—Niels Bohr The future is already here - it’s just unevenly distributed—William Gibson The best way to predict the future is to invent it—Alan Kay     

Forecasting reading anxiety for promoting English-language reading performance based on reading annotation behavior

To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In addition to forecasting immediately the reading anxiety levels of learners, the proposed PRAPM can be used to identify the key factors that cause reading anxiety based on the fired prediction rules determined by the developed decision tree. By understanding these key factors that cause reading anxiety, instructors can apply reading strategies to reduce reading anxiety, thus promoting English-language reading performance. To assess whether the proposed PRAPM can assist instructors in reducing the reading anxiety of learners, this study applies the quasi-experimental method to compare the learning performance of three learning groups, which are, respectively, supported by a CDRAS with individual annotations, collaborative annotations, and collaborative annotations with online instructor's support to reduce reading anxiety by the proposed PRAPM. The instructional experiment was conducted on Grade 7 students at Taipei Municipal Wan-Fang high school. Experimental results indicate that the average correct prediction rate of the proposed PRAPM in identifying the reading anxiety levels of learners was as high as 70%. Moreover, analytical results show that the collaborative annotation with online instructor's support for reducing reading anxiety by the proposed PRAPM indeed helps learners reduce reading anxiety, particularly for the male learners, showing that gender difference exists. Furthermore, based on online instructor's support for reducing reading anxiety by the proposed PRAPM, the correlation analysis also shows that the online instructor's interaction with the male learners is significantly correlated with the reading anxiety reduction. Furthermore, English-language learning performance of the three learners groups, which were given a CDRAS with different learning mechanisms, was significantly promoted.     

基于Agent的网上协同学习环境的研究

目前网上协同学习环境已经成为远程教学中的一个新的研究热点,本文应用面向Agent的方法和技术提出了基于Agent的网上协同学习环境的组成结构和功能实现,解决了网上协同学习中的教学过程控制和学习者交互控制问题,并指出了今后尚待研究的一些问题.     

Problem-based learning in formal and informal learning environments

Problem-based learning (PBL) is a student-centered instructional strategy in which students solve problems and reflect on their experiences. Different domains need different approaches in the design of PBL systems. Therefore, we present one case study in this article: A Java Programming PBL. The application is developed as an additional module for the Learning Management System (LMS). This way the LMS is extended by PBL functionality and the LMS learning resources can be used in PBL.     

让我们和学生靠得更近——江宁区中小学生学情的调查与分析

本研究利用问卷调查法对南京市江宁区的中小学学生从认知倾向、思维类型、学习快乐度和学习成果四个方面入手,了解学生学习的基本状况,得到了较为可信的结果,并在此基础上提出了积极的教学建议。     

注重制度建设 落实激励政策切实推进人才培养模式改革

在外聘教师担任辅导教师的情况下，建立助理辅导教师制度有利于为学员提供更好的学习支持服务，有利于尽快建立适应试点工作的教师队伍；推行个别化学习模式，要把课内外教学看成一个整体，综合确定一个合理的计酬方法，切实落实激励政策；指导教师是个目标性、专业性很强的工作，应由本专业教师担任；开放教育更注重教学过程和学习方法，在知识和获取知识能力两方面更注重能力的培养。     

空间学习:三维世界的“潜行者”

空间学习是学习科学的一个研究领域,其学习场所主要是在三维世界中,学习的对象是三维世界中的内容.文章首先对空间学习领域进行概述,在此基础上归纳出空间学习研究的主要内容,包括空间学习的认知过程、促进空间学习的思维表征工具系统、空间学习的实践探究三个方面;并对其研究成果以及相关典型案例进行介绍;最后,评析了空间学习领域的发展并总结该领域对我国空间学习研究的启示.     

智力投资智力资本智力资本报酬

人才智力投资是家庭及社会在培养具有智慧和能力的创新人才的过程中所花费的代价,它应该转化为智力资本,并同物质资本一样,以资本收益的方式取得投资回报———智力资本报酬。本文以一个22岁大学毕业的学生在教育方面的投资为例,用大量的数据确认了人才智力投资额,并建立了智力资本报酬的回报模型。     

Learning,Empowerment and Judgement

Here is a distinction that appears very simple, looks compelling and seems to be deeply rooted in our reflections on learning. The distinction is between activities of learning that involve training and those that involve reasoning. In the former, the pupil is a passive recipient of habits of mind and action. The mechanism by which they acquire these habits is mimesis, not reasoning. In contrast, learning by reasoning involves considerable mental activity by the pupil who has to work out what to think and do. The very mechanism by which the pupil learns is her own capacity to reason, to things work out for herself.

In this paper I argue that there is no basis for this distinction. I argue that, contrary to the dominant empiricist thinking about such things, learning by reasoning is the only credible form of learning. I start with a brief characterisation of the distinction and an account of why it seems so compelling. In §2 I review the empirical evidence from developmental psychology for a rationalist account of language learning as learning by reasoning. In §§3 and 4 I provide a philosophical argument against the place of training and in favour of a rationalist model of learning by reasoning.     

智能教育下基于情境感知的自适应学习系统构建

伴随着大数据和人工智能在教育领域的渗透和融合,个性化学习成为当前和未来教育关注的焦点,而自适应学习系统为个性化学习提供了一种实践路径.在分析自适应学习基本模型的基础上,结合自适应超媒体系统通用模型AEHS,引入了学习情境,构建了基于情境感知的自适应学习系统模型.为了提高自适应结果的精准性,根据学习情境中的学习者要素、时间要素、空间要素和设备要素,对学习者进行学习情境分组,以此为基础,通过学习者模型、领域模型和教学模型的协同作用,在自适应引擎的驱动下,生成自适应学习结果.最后,结合动态变化的时间因素,提供了个性化资源推荐的实现思路.     

人工智能时代教育人工智能研究与应用现状、问题及对策

为了促进教育与技术融合,提高教育的“教育力”,实现人工智能技术促进教育发展,做到以人为本、因材施教,基于人工智能环境,从现有人工智能技术入手,构建人工智能框架,使教育工作更加高效,为了促进教育与技术融合,提高教育的“教育力”,实现人工智能技术促进教育发展,做到以人为本、因材施教,基于人工智能环境,从现有人工智能技术入手,构建人工智能框架,使教育工作更加高效,教育活动更能体现学生个性。研究表明,人工智能技术与教育结合可更好促进教育发展,培养社会所需人才;基于人工智能的学习可为师生提供一个良好的教育教学环境。通过对关键技术和典型应用的分析,构建教育人工智能技术框架,并针对现有教育人工智能存在的问题提出解决方案。     

中学英语教学中应用智能教学系统的效果评估——以“希赛可”为例

该文介绍将智能英语教学系统"希赛可"应用于初中一年级英语常规教学的准实验对比研究。为获得"希赛可"系统的应用效果,对实验班和对比班的学生历次考试成绩进行分析,发现该系统能够缩小学生的成绩差距;特别是对于基础较差的学生,成绩改善效果更好。之所以出现这样的实验结果,是因为该实验过程着重强调"希赛可"平台上的词汇练习等基础内容,对基础薄弱的学生更具针对性。     

澳大利亚昆士兰州开放教育网络概览

澳大利亚昆士兰州开放教育网络（QOLN）在运用开放学习的方法来传递教学并支持地区性的,乡村和偏远社区的学习中发挥了得要作用,介绍了这个网络的操作,网络的产品与服务,网络的教学传递和支持系统以及该网络的未来发展计划等。     

基于问题的学习模式在计算机基础课教学中的应用研究

基于网络的学习模式是目前大家普遍认同的主流学习模式,而基于问题的教学理念又是学生依据个体差异和认知能力高低获取知识的最佳途径,因此二者的有机融合便成为了当前教育界研究的热点问题。本文研究的思路是基于将传统课堂授课与在线学习相融合,以问题为中心取代以课程为中心的教学理念,真正探索教学的个性化和学习过程的自主化,做到因材施教和个性化教学。实践结果显示了该教学模式的正确性和有效性,能够在提升学生学习兴趣的同时,大幅度提高学习的效率。