阅读理解智能教学系统的结构与设计

讨论了智能教学系统的一般结构及学生模型的建立,并着重分析了阅读理解智能教学系统的结构与功能,及该系统教学设计与媒体设计的要点与方法。     

开源智能教学系统的架构及功能框架研究——以GIFT系统为例

开源智能教学系统是人工智能在教学领域的重要应用方向。与传统智能教学系统相比,开源智能教学系统的适应性、灵活性、自主性更强。GIFT平台由美国军队研究实验室主导开发,该平台支持在学科教学、医疗培训、技能训练等多个教育领域实现基于情境的智能课程开发。GIFT主要由传感器模块、学习者模块、教学模块、领域模块这四大基础模块支撑其体系架构,提供教学创作、教学管理和教学评估三大基本功能。基于GIFT平台的多个教学实例,显现出该平台能在不同学习领域适应不同情境下的教学或训练需求,且在极大程度上降低了教师开发智能课程的技术门槛。     

智能教学代理的系统特性及设计框架

智能化已经成为网络教学系统发展的一个必然趋势, 这种智能化的教学系统有利于提高现有网络教学的教学效果, 并能维持及增强学生的学习动机.智能导师系统(ITS)及智能教学代理是当前网络教学系统智能化的两种主要路径, 较之ITS, 智能教学代理能更好地嵌入到已有的网络教学系统中.基于对国内外应用智能代理的网络教学系统的原型和实用设计方案的考察, 分析了应用于网络教学的智能教学代理的系统特性, 从智能代理的运行机制、智能代理与网络学习环境的接口、代理行为的构建及连接、代理行为的控制、与网络教学系统的集成等几个方面, 探析了基于智能代理的网络教学系统的设计框架.     

网络智能教学系统中双层学生模型的设计

本文探讨了在网络智能教学系统中建立一个双层动态学生模型的方法,模型的初始层采用复合认知型学生模型,高级层利用Hopfield神经网络算法,在初始层数据的基础上加入兴趣、爱好、知识状态、学习历史等信息进行评价、分类.该模型克服了单一学生模型结构数据处理的障碍,能够灵活、全面地对学生的学习特征进行分析,有效改善了智能网络教学系统的教学决策过程.     

智能教学系统支持英语学习的案例研究

在阐述智能教学系统及其组成架构的基础上,结合PIMS移动智能教学系统支持英语学习的个案,介绍了PIMS系统框架及其在教学中的应用,认为案例具有重视学生初始学习能力的评估、动态生成适应性学习内容、动态调整学习策略、关注智能教学系统的教学应用及其效果等优点,同时提出PIMS智能教学系统进一步研究的建议。     

基于Web的智能教学系统的研究

传统的智能网络教学系统在适应性和个性化方面存在严重缺陷。而基于Web的智能教学系统则提出了一种基于Web的智能教学系统模型,给出了智能教学系统的知识库的设计、学生模型的构建方法及教学策略的设计,从而形成了一种对学生实施有效教学的技术,具有智能性。     

智能化——计算机辅助教学系统(CAI)的发展方向

本文介绍了计算机辅助教学以及智能计算机辅助教学系统(ICAI),论述了智能计算机辅助教学系统(ICAI)的本质特征和理论基础,分析了智能计算机辅助教学的体系结构原理。     

数学智能教学系统构建特点、策略与评估——基于WoS的文献计量分析与梳理

《义务教育数学课程标准（2022年版）》中指出,数学教育要促进信息技术和数学课程的融合,而智能教学系统是人工智能辅助教育的重要应用,其特点在数学教学领域受到了广泛的关注。然而,如何构建符合数学特征的智能教学系统？数学智能教学系统的设计应当关注哪些方面？如何评估数学智能教学系统的效果？当前,研究者仍未能构建出一个统一的框架。本文基于Wo S数据库对数学智能教学系统研究进行计量分析,构建了数学智能教学系统的实证研究分析框架进行文献的梳理,并基于梳理结果总结智能教学系统辅助数学学习的研究特点和未来发展趋势,为数学智能教学系统的框架设计提供参考。     

计算机辅助教学系统的研究与设计

探讨了智能计算机辅助教学系统的设计思想、设计目标,并给出了系统的教学过程和结构、以及部分设计方法。     

基于人工智能的计算机辅助教学研究

概述了计算机辅助教学的现状,阐述了智能计算机辅助教学系统的内容,探讨了开发智能计算机辅助教学系统的未来发展方向。     

The Relative Effectiveness of Human Tutoring,Intelligent Tutoring Systems,and Other Tutoring Systems

This article is a review of experiments comparing the effectiveness of human tutoring, computer tutoring, and no tutoring. “No tutoring” refers to instruction that teaches the same content without tutoring. The computer tutoring systems were divided by their granularity of the user interface interaction into answer-based, step-based, and substep-based tutoring systems. Most intelligent tutoring systems have step-based or substep-based granularities of interaction, whereas most other tutoring systems (often called CAI, CBT, or CAL systems) have answer-based user interfaces. It is widely believed as the granularity of tutoring decreases, the effectiveness increases. In particular, when compared to No tutoring, the effect sizes of answer-based tutoring systems, intelligent tutoring systems, and adult human tutors are believed to be d = 0.3, 1.0, and 2.0 respectively. This review did not confirm these beliefs. Instead, it found that the effect size of human tutoring was much lower: d = 0.79. Moreover, the effect size of intelligent tutoring systems was 0.76, so they are nearly as effective as human tutoring.     

智能教学系统中支持协作学习的群体感知模型

自主学习和协作学习是相互促进、相互依赖的网络学习方式。然而,目前典型的网络学习系统(智能教学系统和计算机支持的协作学习系统)大多将自主学习和协作学习强制分离,忽视了自主学习过程中协作交流的激励作用以及协作学习过程中学习者的自主学习需求。因此,扩展现有的智能教学系统,构建一个自主学习和协作学习有机融合的数字化学习环境,有助于更好地满足网络环境下师生的学习需求。支持协作学习的群体感知模型由群体感知信息、群体感知本体和群体感知规则三部分组成,采用本体技术建模协作感知信息,运用语义Web规则语言刻画协作感知机制,为实现协作学习的信息交互提供通用模型。基于该模型构建的智能教学系统,在原有ITS自主学习环境的基础上,扩展了参与者可以相互感知、相互交流的协作学习模块,实现与现有智能教学系统的无缝整合,为开发具有协作学习功能的智能教学系统提供了技术参考。     

Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems

There has been much debate about instructional strategies for computerized learning environments. Many of the arguments designed to choose between the various philosophies have appealed, at least implicitly, to the behavior of effective human teachers. In this article, we compare the guidance and support offered by human tutors with that offered by intelligent tutoring systems. First, we review research on human tutoring strategies in various domains. Then we investigate the capabilities of a widely used technique for providing feedback, model tracing. Finally, we contrast the types of guidance and support provided by human tutors with those in intelligent tutoring systems, by examining the process of recovering from impasses encountered during problem solving. In general, the support offered by human tutors is more flexible and more subtle than that offered by model tracing tutors, but the two are more similar than sometimes argued.     

Hinting as a Tactic in One-on-One Tutoring

One-on-one tutoring is a particularly effective mode of instruction, and we have studied the behavior of expert tutors in such a setting. A tactic commonly used by our expert tutors is hinting, that is, the prompting of a student to recollect information presumed to be known to him or her, or the prompting of a student to make an inference needed to solve a problem or answer a question, or both. Hints may directly convey information or may point to information the student already possesses. Another tactic prompts the student in a step-by-step manner (in a directed line of reasoning) to an answer. Our tutors generated 315 hints and directed lines of reasoning in 30 hr of tutoring. The surface structure of hints is complex and varied, reflecting, in part, the fact that the utterances making up hints often serve multiple functions. Hinting is triggered by student errors but ceases when it appears that the student is unable to respond appropriately. Hints encourage the student to engage in active cognitive processes that are thought to promote deeper understanding and long-term retention. It is our intention to apply our knowledge of tutorial dialogue generation to the building of an intelligent tutoring system (ITS).     

人工智能技术的远程教育应用探索——“希赛可”智能型网上英语学习系统

本文首先指出现有计算机辅助语言学习软件和系统的优点和不足,以及我国英语教学对于网上智能型人机对话系统的实际需求。然后介绍了我们研制的网上智能型英语学习系统—希赛可:依次包括它的理论基础,系统构架,在英语教学上的独特创新功能,以及需要完善和改进的地方。     

电工学智能教学专家系统的研究与开发

采用知识工程语言CLIPS研究、开发的电工学智能教学专家系统是基于互联网的新型专家系统。该系统包括学生模型、教师模型、教学模块、诊断模块、测试模块和知识库建设、方法库、试题库、策略库等部分,以最适合学生特点的方式组织教案和教学手段,诊断、纠正学习过程中出现的错误,实现个性化的电工学课程教学。     

Multisite Randomized Controlled Trial Examining Intelligent Tutoring of Structure Strategy for Fifth-Grade Readers

Abstract

This article reports on a large scale randomized controlled trial to study the efficacy of a web-based intelligent tutoring system for the structure strategy designed to improve content area reading comprehension. The research was conducted with 128 fifth-grade classrooms within 12 school districts in rural and suburban settings. Classrooms within each school were randomly assigned to intervention or control groups. The intervention group used the intelligent web-based tutoring system for the structure strategy (ITSS) for 30 to 45 min each week as a partial substitute for the language arts curriculum for the entire school year. The structure strategy teaches students how to read and comprehend expository texts by identifying the text structure and creating strategic mental representations of the text. The web-based tutoring system delivered the structure strategy training with modeling, practice tasks, assessment, and feedback. The control classrooms used the school's language arts curriculum for the full language-arts time. Results show that the ITSS delivered structure strategy training improved reading comprehension measured by a standardized test and researcher designed measures.     

试析知识管理技术在智能教学系统中的应用

智能教学系统是开放式的人机交互系统.这个系统在结构与过程上都与知识管理系统有着诸多相似之处,本文在综合分析知识管理及其技术的基础上,从广义与狭义两个维度探讨了知识管理技术在智能教学系统设计中的研究与应用.     

基于Web的智能网络教学框架模型的设计与实现

基于Web的智能网络教学系统综合了传统智能教学系统的优势,同时又结合了Web的特点,是智能教学的发展方向。该文首先分析网络教学的特点及目前的网络系统的弊端,在此基础上提出一个面向网络教学、实现双主体互动教学模式的新型智能化框架模型,提出面向重用网络教学应用模式概念,并论述若干相关技术及进一步的研究方向。