Learning Prolog in a schema-based environment

A set of basic construct Prolog schemata are presented as the basic constructs of a structured Prolog for recursive list processing. Prolog schema hierarchies are given that group classes of Prolog programs within each of the basic construct Prolog schemata via most-specific schemata which high-light their similarities and differences. A schema-based instructional approach based upon this classification of Prolog programs is provided as a method of introducing recursion to novice Prolog programmers. The incorporation of this approach to teaching recursive Prolog programming in an intelligent tutoring system is presented. Finally, a schema-based Prolog programming environment embedding this tutoring system in addition to a standard Prolog development system is proposed which promotes the acquisition and use of structured programming constructs in Prolog.     

基于Client/Server的课件系统设计与实现

ICAI系统已经成为国际上计算机辅助教学研究的主要方向.基于Client/Server的计算机智能教学(ICAI)的实现技术,采用VB、SQL Server、IIS等支撑平台,建立了一个用于计算机教学的ICAI试验系统.     

ICAI系统中数据挖掘研究

学生在学习过程中未达到学习目标,说明存在知识缺陷,如何智能性重构学习路径弥补知识缺陷是ICAI的关键技术I。CAI有其本身的规律及特点,利用数据挖掘算法,能较好提高ICAI的因材施教的能力。本文通过分析研究挖掘算法,提出在目标测评ICAI系统中挖掘算法的合理实现方法。     

计算机辅助教学的智能化历程及其启示

计算机在教学中的应用经历了程序教学到计算机辅助教学的过程,目前,正由智能计算机辅助教学向适应性学习支持发展。由于智能计算机辅助教学系统过于强调理想化的"教",多年来一直难以克服技术实现与应用效果之间的矛盾。适应性学习支持系统既发挥了系统的优势,又注重对"学"的适应性支持,是当前网络学习平台智能化的一个研究热点。作为一个交叉研究领域,计算机辅助教学应注意吸取和借鉴其他学科的新思想、新技术和新方法,注重以多媒体学习的认知理论作为指导思想。     

A prolog-based tool for French grammar analysis

In this article we look at some traditional approaches to the use of computers in the teaching of modern languages. Pointing out their inadequacies, we describe a system based around the use of a natural language parser for teaching French, known as frog. The role of Artificial Intelligence techniques in general and the benefits of using the programming language Prolog in particular for this task are described. We then present a system which is based around the use of such general techniques to cope with free form French sentences but which can be generalised as the basis of future work for a language-independent teaching shell.     

基于人工智能的CAI系统开发

通过对传统CAI系统现状的分析,把人工智能技术引入到CAI系统的设计中,阐述了ICAI系统的特点和设计的思想依据,并讨论了ICAI系统的结构、各部分设计以及设计过程中的一些关键技术.     

ICAI技术探析

论述了ICAI系统的组成——领域知识模块、教师模块、学生模块，在学生模块中给出了纠错功能的实现算法，简要介绍了ICAI系统的特点和教育心理学基础．     

一种基于Internet的ICAI系统设计方案

在信息社会，受知识的迅速更新、人才需求的多元化等因素的影响，使得我们传统的“精英”教育力不从心，因此，基于互联网络的远程教育悄然兴起。但同时也带来了诸如：ICAI中知识如何表示、教师模型和学生模型如何建立等技术问题。一种利用Microsoft的Agent技术来构建基于Internet的智能化计算机辅助教学系统的技术设计方案便是一种尝试。     

ICAI的智能实现设计方案

讨论了通过在计算机智能教学系统建立合理的数据库模型,来实现教学系统的智能化功能--智能的根据的学生实际情况组织学习内容,确定学生学习方法,并对学生的学习行为给予评价.同时实现教师根据不同教学方法对教学资源进行灵活的重构功能.     

社会生态理论视角下的大学生思想政治工作

当前,高校大学生思想政治教育工作面临许多新的挑战。社会生态理论是新兴的社会工作指导方法,它主要强调在为案主提供服务时应注重案主本人与社区、家庭、学校、社会之间的整体性,以及在这个系统中案主所处的环境与案主之间的交互作用。这种实务模式对指导现今大学生思想政治教育工作具有重要的借鉴意义。它强调大学生思想工作应在个体不同系统之间展开联系,并随着学生需求及时代的变化而变化。     

Observational studies about novices interacting in a Prolog environment based on tools

This paper examines the general issues related to the interaction of novices in a Prolog environment based on tools to support both the operational and the declarative aspects of the language. The tools are based on graphical representations for the Prolog program and for Prolog execution of programs. This research aims to investigate how students come to understand and integrate these different views of Prolog and how the tools affect their understanding of the language.Observations conducted during this study show that the novices had an initial conceptual model of the virtual machine. Such models guided their interpretation of the Prolog program and of the behavior of the machine. Their misunderstandings may be interpreted as having resulted from their current models. These models evolved during their interaction within the environment. The type of feedback created by the tools provided changes in the students perspective related to the knowledge being represented (program) and the interlocutor in the process (virtual machine), which led them to the construction of the conceptual model of the language.     

INTELLIGENT DISCOVERY SYSTEMS (IDS)

In the literature on Intelligent Computer‐Aided Instruction (ICAI), and more widely, in that on artificial intelligence in education, we can distinguish, as a general rule, between intelligent tutoring systems and learning environments (also called micro‐worlds). In this article, we have attempted to merge these two approaches so as to produce a synthesis which we propose calling intelligent discovery systems.     

基于网络环境的多媒体智能教学系统的开发

介绍了一种基于网络的多媒体智能教学系统，以及人工智能技术，反馈原理，信息融合技术在ICAI课件中的应用，同时在此基础上开发出一个具有实用价值的教学系统。     

What's wrong? Understanding beginners' problems with Prolog

This paper reviews psychological research on programming and applies it to the problems of learning and teaching Prolog. We present a psychological model that explains how a certain class of errors in programs comes about. The model fits quite well with the results of a small sample of students and problems. The problems that underlie these and other errors seem to be (a) the complexity of the Prolog primitives (unification and backtracking) and (b) the misfit between students' naive solutions to a problem and the constructs that are available in Prolog (e.g. iterative solutions do not map easily to recursive programs). This suggests that learning Prolog could be helped by (1) coherent and detailed instruction about how Prolog works, (2) emphasis on finding recursive solutions that do not rely on primitives such as assignment and (3) instruction in programming techniques that allow students to implement procedural solutions.     

一种构造Prolog程序子句本体的方法

为了使Prolog推理引擎可以从一定程度的语义上来区分子句,通过概念图的形式对Prolog程序的子句建立本体.子句本体的概念模型通过其谓词参数和头子目标来建立,而头子目标是指出现在规则体首部并且位于任何谓词调用之前的子目标集合.所提出的方法把一个Prolog程序转换成包含其子句本体的Prolog CG程序.实验表明,通过对Prolog子句建立本体,可以尽早地去除与当前目标明显不匹配的子句,从而使得目标求解树的规模减小.在概念图的描述形式下,子句本体使得Prolog程序的语义更清晰可读,在某些情况下能明显加快程序的求解过程.     

基于web的智能辅助教学系统的多模式研究

随着多媒体、超媒体以及网络技术的发展,基于网络的应用得到了快速的发展。CAI应用发展的一个主要方向就是网络智能计算机辅助教学（ICAI）,它能为人们相互学习交流、共享网络资源提供更大的方便。理想的智能教学系统应能提供多种教学模式及相应教学内容,适应不同学生的学习风格和满足不同学生的学习兴趣和需求,对学生实施个性化的教学。     

Artificial intelligence applications to high-technology training

Recent advances in artificial intelligence (AI) could be used to improve occupational instruction in complex subjects with sophisticated performance goals, such as those required for high-technology jobs. Educational devices incorporating AI would “understand”what, whom, andhow they were teaching and could therefore tailor content and method to the needs of an individual learner without being limited to a repertoire of prespecified responses. Intelligent Computer Assisted Instruction (ICAI) encompasses a spectrum of approaches, including Socratic tutoring systems, simulation environments with embedded coaches, and “empowering environments” which aid workers in using intelligent tools in complementary cognitive partnerships. This article focuses on (a) depicting how present training methods might change if intelligent instructional devices were incorporated, (b) delineating the current state of the art in the areas of research needed to produce such systems, and (c) indicating in which types of adult educational settings ICAI might be productive and cost effective.     

智能计算机辅助教学系统实现的探讨

随着人工智能技术的引入，计算机辅助教学(CAI)发展成为智能计算机辅助教学(ICAI)，ICAI不但克服了传统CAI的许多弱点，而且大大地提高和改善了教学质量和效率。本文首先对智能计算机辅助教学系统的特征和功能进行分析和研究，并在此基础上提出ICAI系统的实现方法。     

Analysing novices analysing Prolog: what stories do novices tell themselves about Prolog?

Informal observations of Prolog learners showed that, despite being presented with correct information and models, students still tended to construct their own idiosyncratic explanations of events, and, characteristically, they defended these ‘stories’ fiercely when tutorial intervention was attempted. Although the stories were often so flawed that the student's future progress was potentially hampered, it was nevertheless true that learning could not have proceeded at all without them. It seems that if we are to understand the novice Prolog programmer, we need to know about these stories, their source, and what, if anything, they have in common from one learner to another. Pain and Bundy (1987) posed the question “What stories should we tell novice Prolog programmers?” in order to teach them Prolog. In our research, we ask: “What stories do novices Prolog programmers tell themselves?” in order to learn Prolog. Observational studies undertaken showed that students used tacit knowledge of human discourse processes both to interpret the language used to communicate with the computer and to interpret the behaviour of the machine. Students did not appreciate the fundamental differences between natural discourse (as takes place amongst humans) and formal discourse (as takes place between humans and machines), and confused elements of the discourse levels. This can be an effective initial learning strategy, but unless its limitations are recognised, programs are inevitably incomplete at some level. Examples from these studies are reported here with illustrative protocol fragments.     

一类状态空间图表示的产生式系统的prolog求解

本文介绍了产生式系统和状态空间图,结合Prolog提出了一种生成产生式系统的一个通用模型,最后通过对几个实例进行了计算机模拟,结果表明,该模型具有良好的适用性.