智能网络教学系统研究综述

智能网络教学系统是现代化教学领域的一个研究热点,它结合了网络技术及人工智能技术,为学生学习提供了一种新的选择。本文首先研究了智能教学系统的框架,讨论了与其相关主要技术和理论,最后综合论述了智能网络教学系统的研究现状,并展望了其发展前景。     

基于Web的智能教学系统模型

智能教学系统是当前计算机应用的重要领域之一,文中主要研究在国家知识基础设施(NKI)支撑下的基于Web的智能教学系统模型：NKI-Tutor。NKI-Tutor包括七个模块：学生模型、教学模型、交互模型、NKI知识库、专家模型、出题模块和模拟工具,其主要特点是：(1)教学内容覆盖多个学科知识,且是动态的知识库;(2)交互模块给教师提供可操作的教学策略描述语言,便于教师在系统中设计个性化的教学策略。     

智能教学系统

计算机在教育领域日益广泛深入的应用引起了教育事业的迅速发展，现在计算机已成为有效的数学媒体和教育管理工具，它使教学模式、教学组织、教学过程、教材观念与形式、课堂教学结构，甚至教学思想和教学理论都有了一些变革．在此基础上形成一门将教育学理论与计算机科学技术结合起来的研究领域——计算机辅助教学（CAI—－ComputerAssistedInstruction）．一、教学软件计算机在教学领域的应用是相当广泛的，一切源于教学，服务干教学，所研制、开发出的软件统称为教学软件或教育软件。开发教育软件，即用计算机技术实现教育思想；教育…     

智能教学系统中学习效果评价研究

在智能教学系统中,如何对教学质量和学习效果进行正确的评价越来越重要了,要想提高系统的智能化程度．就必须不断的完善系统评价体系。传统的智能教学系统仅仅限于对学习效果的评价,不能反映出学生认知水平所处的认知阶段。本章结合人的认知水平方面的研究成果,利用科学的评价方法,对智能教学系统评价系统进行了重新设计。     

智能计算机辅助教学系统TCCA

ＴＣＣＡ是一个基于微型机的中医“经络腧穴学“智能教学系统。它面向高级，中级和普及型三类学生及教学专家，采用统一的泛概念网知识描述结构以及灵活的教学策略，利用个别辅导，会话和模拟相结合的教学模式进行教授。本文介绍系统功能，设计思想和实现结构。     

计算机故障诊断智能教学系统的研究

本文以人工智能技术为基础,研究智能教学系统框架模型,并以计算机故障诊断技术为案例,设计计算机故障诊断智能教学系统,为用户提供计算机故障诊断自主学习的平台.     

智能教学系统中范例推理研究

智能教学系统是目前计算机人工智能领域与教育技术领域的研究热点之一,本文阐述了智能教学系统的概念和特征,并对系统基本框架进行了说明,阐述范例推理的特点和一般过程,对在智能教学系统构建过程使用范例推理来实现模型构建、教学策略选择等作了进一步研究.     

基于知识网格的智能教学系统

在教学实践中,很难准确、系统的评估学生对所学知识的掌握程度。学生不能理解所学概念的原因是缺乏必备基础知识（先决知识）,不理解所学这些知识对以后要学课程影响。要解决这个问题,运用描绘和个人的分析的方法是十分困难的。基于知识网格智能教学系统使用知识网格能够建立一个可共享的,协作的基于网络的知识体系。它用知识网格中的一个节点作为起始点进行＂知识漫步＂,建立一个＂缺失知识链＂。告诉学生目前所学的知识体系结构。最终,评测结果将会被送到网上不同的虚拟组织。这些组织是由教师组成的,所以他们的建议会更有参考价值。     

基于WEB的智能教学系统的研究和设计

随着Internet技术的飞速发展,远程教学系统应运而生。本文从智能化和个性化的网络教学角度出发,探讨了智能教学系统的总体需求和设计方案,所设计的系统应用ASP.NET技术,以树型导航的方式来实现网络学习,并能在学习之后进行习题测试和在线答疑。     

基于情感识别的智能教学系统研究

针对传统的智能教学系统(ITS)在情感方面的缺失,提出了基于情感识别技术的ITS模型.该系统模型在传统的教学系统上新增情感识别模块,利用人脸表情识别以及文本识别等技术所构建,可以获取和识别学生的学习情感,并根据学习情感进行相应的情感激励策略,实现情感化的教学.     

基于宠物游戏的智能教学系统

通过利用游戏宠物扮演各种教育角色,扩展智能教学系统(intelligent tutoring system,ITS)中的学生模型,从而完成宠物游戏与ITS的有机结合.设计了一个基于宠物游戏的游戏化智能教学系统架构,架构中的宠物模块能记录学习者的学习活动、分析学习者的学习进程.基于该软件架构,实现了一款游戏化智能教学系统--宠物星际旅行,将宠物游戏的游戏化特性引入到了小学分数知识的学习过程中.测试结果表明,学生对该游戏越专注,就能越快地理解和运用所学知识.     

智能教学系统中基于案例推理的教学策略研究

把基于案例推理(CBR)技术引入智能教学系统中教学策略的推理和控制中,提出了在全局教学策略和局部教学策略控制中CBR应用模型,并提出了在这种模式下,案例库的初始化、案例的学习与修正方法.该模型应用以前学生学习的经验作为以后学生学习的引导,从而实现教学策略的自动组织、自动规划,实现学生的自主选择,自由学习.最后进行了总结,提出了下一步研究的方向.     

User modelling and user adapted interaction in an intelligent tutoring system

User modelling and user-adapted interaction are crucial to the provision of true individualised instruction, which intelligent tutoring systems strive to achieve. This paper presents how user (student) modelling and student adapted instruction is achieved in FITS, an intelligent tutoring system for the fractions domain. Some researchers have begun questioning both the need for detailed student models as well as the pragmatic possibility of building them. The key contributions of this paper are in its attempt to rehabilitate student modelling/adaptive tutoring within ITSs and in FITS's practical use of simple techniques to realise them with seemingly encouraging results; some illustrations are given to demonstrate the latter.     

基于概念网络的智能教学系统--OMT

从知识管理的角度出发，利用知识表达工具概念网络（Connet）为智能教学系统OMT（Olympic Math Tutoring）构造知识模型，以“知识树”的形式呈现领域知识的层次和逻辑关系。同时使用覆盖知识树的概率网络建立学生模型，结合习题参数的设计达到解释学生学习行为、为学生提供个性化教学的目的。     

智能导师系统在军事训练中应用现状及发展

根据国内当前军事训练所面临的实际困境及智能导师系统(ITS)在教学训练应用中所具有的功能和经济价值,对ITS在军事训练中的应用进行了初步研究.介绍了ITS的发展历程、ITS的相关概念及其技术特点.以ITS在国外应用及发展为主线,重点论述了欧美国家军事训练中ITS应用的现状和相关的典型系统.提出在军事训练领域中我们应用ITS技术的一些建议.     

Automatic program debugging for intelligent tutoring systems

Program debugging is an important part of the domain expertise required for intelligent tutoring systems that teach programming languages. This article explores the process by which student programs can be automatically debugged in order to increase the instructional capabilities of these systems. The research presented provides a methodology and implementation for the diagnosis and correction of nontrivial recursive programs. In this approach, recursive programs are debugged by repairing induction proofs in the Boyer-Moore logic. The induction proofs constructed and debugged assert the computational équivalence of student programs to correct exemplar solutions. Exemplar solutions not only specify correct implementations but also provide correct code to replace buggy student code. Bugs in student code are repaired with heuristics that attempt to minimize the scope of repair. The automated debugging of student code is greatly complicated by the tremendous variability that arises in student solutions to nontrivial tasks. This variability can be coped with, and debugging performance improved, by explicit reasoning about computational semantics during the debugging process. This article supports these claims by discussing the design, implementation, and evaluation of Talus, an automatic debugger for LISP programs, and by examining related work in automated program debugging. Talus relies on its abilities to reason about computational semantics to perform algorithm recognition, infer code teleology, and to automatically detect and correct nonsyntactic errors in student programs written in a restricted, but nontrivial, subset of LISP. Solutions can vary significantly in algorithm, functional decomposition, role of variables, data flow, control flow, values returned by functions, LISP primitives used, and identifiers used. Solutions can consist of multiple functions, each containing multiple bugs. Empiricial evaluation demonstrates that Talus achieves high performance in debugging widely varying student solutions to challenging tasks.     

A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

The role of probability-based inference in an intelligent tutoring system

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the considerations that arise when one attempts to operationalize the abstract framework of probability-based reasoning in a practical ITS context. The discussion revolves around HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics system. HYDRIVE supports generalized claims about aspects of student proficiency through probabilitybased combination of rule-based evaluations of specific actions. The paper highlights the interplay among inferential issues, the psychology of learning in the domain, and the instructional approach upon which the ITS is based.     

Student modelling in intelligent tutoring systems

Student modelling is a special type of user modelling which is relevant to the adaptability of intelligent tutoring systems. This paper reviews the basic techniques which have been used in student modelling and discusses issues and approaches of current interest. The role of a student model in a tutoring system and methods for representing information about students are discussed. The paper concludes with an overview of some unresolved issues and problems in student modelling.Electric Brain Company