基于聚类和案例推理的个性化教学序列生成

针对智能教学系统中个性化教学序列生成规则难于获取的问题,提出了将案例推理技术应用于教学序列生成,设计了学生案例相似度的计算方法.为了在案例数量增大时获得较好的检索速度和命中率,提出通过关键属性聚类来对案例进行划分并设计了用于聚类的惰性2-中心点算法,最后以实例验证了系统设计的可用性.     

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.     

限定领域智能导学系统问题生成及对话管理技术

智能导学是当前信息化教学研究的一个热点领域,将导学技术应用到难于识记并需要严格遵守的规章规程类知识领域,将极大地提高领域人员对相关知识的快速理解与掌握能力.采用面向对象的知识表示方法,提高了对通用主题和问题模板表示的灵活性.设计了基于模板的领域知识表示方法和导学问题生成方法,可实现一个主题知识生成多种问句.采用约简的有限状态转移方法,避免了对话管理中状态数量庞大的问题.最后,本文以船舶损管领域为例,将此方法运用到船舶损管训练语音导学原型系统中.系统运行良好,充分表明这种设计方法是切实可行的.     

基于ZigBee无线传感器网络的实验室智能教学系统

针对教育中由于缺乏记录数据而无法分析学生成绩导致在动手能力经验培养上花费太高的问题,设计了一种基于ZigBee无线传感网络的实验室智能教学系统。首先,基于嵌入式操作系统TinyOS设计了无线传感网络节点;然后,设计了采集仪和协调器;最后,利用本文设计的系统对80名学生进行了实验,实验及分析结果表明,无线传感器网络学习环境明显有助于实验课程,学生成绩有了明显提高,相比高分组学生,低分组学生的成绩提高更为明显。     

机载嵌入式空战训练的研究与进展

超视距作战是现代空战的主要方式,实兵对抗演练和地面模拟器对超视距空战的训练存在不足。嵌入式训练有机结合了实在仿真与虚拟仿真,弥补了前两种训练方式的缺陷,实现训练与作战的无缝连接。首先阐述了嵌入式训练概念和优势。重点研究了嵌入式训练的关键支撑技术：机载嵌入式训练系统架构,超视距作战虚拟兵力生成,嵌入式训练中情景意识研究和智能教学,总结了国内外嵌入式训练的研究现状。最后分析了嵌入式训练系统存在的问题,并对其发展方向进行了展望。     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

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.     

Design of a braille writing tutor to combat illiteracy

Less than 3% of the 145 million blind people living in developing countries are literate (Helander, Prejudice and dignity: An introduction to community-based rehabilitation. New York: UNDP 1998). This low literacy rate is partly due to the lack of trained teachers and the challenges associated with learning to write braille on a traditional slate and stylus. These challenges include writing from right to left, writing mirrored images of letters, and receiving significantly delayed feedback. Extensive conversations with the Mathru Educational Trust for the Blind near Bangalore, India, revealed the need for a robust, low-power, low-cost braille writing tutor. We present an iterative and participatory process resulting in the creation and refinement of a prototype braille writing tutor system. This system uses a novel input device to capture a student’s activity on a slate using a stylus and uses a range of techniques to teach braille writing skills to both beginner and advanced students. We report on lessons learned from the implementation of this project and from a 6-week pilot study at Mathru, and outline future directions for improvement.

自然语言处理系统中的事件类名词的耦合处理

介绍了自然语言处理系统中事件类名词耦合的一种处理方法。事件类名词是汉语名词的一个特殊子类,这类名词兼事物性与动作性于一身。给出了事件类名词的语义认知基础,并采用基于知识的方法,应用概念从属理论,对事件类名词进行详细的语义分析。在此基础上,对事件类名词的耦合情况进行分类,并给出了事件名词解耦处理的具体方法。这种对事件类名词的耦合处理方法在智能辅导领域中得到了较好的应用。     

AutoTutor: Incorporating Back-Channel Feedback and Other Human-Like Conversational Behaviors into an Intelligent Tutoring System

This paper describes our recent attempts to incorporate human-like conversational behaviors into the dialog moves delivered by an animated pedagogical agent that simulates human tutors. We first present a brief overview of the modules comprising AutoTutor, an intelligent tutoring system. The second section describes a set of conversational behaviors that are being incorporated into AutoTutor. The behaviors of interest involve variations in intonation, head movements, arm and hand movements, facial expressions, eye blinking, gaze direction, and back-channel feedback. The final section presents a recent empirical study concerned with back-channel feedback events during human-to-human tutoring sessions. The back-channel feedback events emitted by tutors are mostly positive (63%), mostly verbal (77%), and immediately follow speech-act boundaries or noun-phrase boundaries (83%). Tutors also deliver back-channelevents at a very high rate when students are emitting dialog, about 13 events per minute. Conversely, 88% of students' back-channel feedback events are head nods, and they occur at unbounded locations (63%).