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.     

The evaluation of an intelligent tutoring system

Abstract

Numerous intelligent tutoring systems (ITSs) have been developed to date. However, one aspect of their development which is consistently ignored, judging from the literature, is the crucial activity of evaluation. It is also ironical that researchers generally agree that the benefits of this activity could be far-reaching. ITSs are often described to some degree of detail but an appraisal as to their (potential) usefulness is seldom given. The relative novelty of doing this was the main motivation for this paper, which starts by attempting to shed some light on why evaluation has become such a taboo activity, not only in intelligent tutoring, but in artificial intelligence (AI) research in general. It then overviews a tutor, the fractions intelligent tutoring system (FITS), and reports on how it was appraised. The approaches used are neither ideal nor generally accepted, but may well provide an adequate starting point in the belief that an attempt at an honest evaluation of any sort is better than no evaluation at all.     

A conversational intelligent tutoring system to automatically predict learning styles

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61–100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%.     

An intelligent tutoring system for the Dijkstra-Gries methodology

The paper describes the design and implementation of an intelligent tutoring system for the Dijkstra-Gries programming methodology as defined by Gries (1981) in “The Science of Programming”. The first part of the paper identifies the requirements of intelligent tutoring systems in general and those of the methodology in particular. It shows the suitability of the Smalltalk environment for developing expandable intelligent systems and the compatibility of Smalltalk's object-oriented paradigm with the Gries methodology's goal/plan approach to programming. We then describe how these requirements are met: an overview of the system's support of the methodology and the modules that enable the system to respond intelligently. As an example, a reusable tutorial part is presented, first from a student's perspective, then from an author's perspective. Finally the results of an evaluation of the system drawn from actual student experience are presented     

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.     

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.     

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.     

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.     

An object-oriented software life cycle of an intelligent tutoring system

Abstract  This paper describes the application of an object-oriented model of life cycle in the development of an intelligent tutoring system for secondary students in the domain of algebraic powers. The system is called EasyMath and has been developed using the Rational Unified Process, which is an object-oriented process proposed for use with the Unified Modelling Language. The Rational Unified Process supports multiple recursions of the phases of the software life cycle. This has been enriched to include empirical studies and formative evaluations appropriate for knowledge-based educational software.     

An adaptive and innovative question-driven competition-based intelligent tutoring system for learning

ISCARE (Information System for Competition based on pRoblem solving in Education) is a new and innovative intelligent tutoring system that we have designed and implemented. This tool allows the competition among students for improving their learning process in a course. The tool takes some ideas from the Swiss-system widely used in chess and adapts them to the educational area. The competition is based on different tournaments and rounds. In each round, students are assigned in pairs of two, which compete one against another, and each pair receives different questions that students have to solve in a limit of time. Students can see their partial ratings after each round and their final rating after a tournament. A lot of knowledge from different disciplines was used to design, and implement this system, as ISCARE includes different functionality such as the students’ registration into the system, the creation of tournaments, the registration and assignment of students to tournaments, the management of each tournament life cycle (started, in execution, finished, etc.), the addition of the different exercises to tournaments, the calculation of pairs of students for each round with different algorithms, the assignment of exercises per round and pair, the scorings of the students per round and tournament, the management of the students’ ratings, or the visualization of information. This paper presents the ISCARE intelligent tutoring system, describing its different options, menus, or functionality as well as its architecture and the specific modeling to achieve the desired features.     

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.     

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     

Adaptive systems: from intelligent tutoring to autonomous agents

Computer systems which can automatically alter aspects of their functionality or interface to suit the needs of individuals or groups of users have appeared over the years in a variety of guises. Most recently, attention has focused on intelligent interface agents, which are seen as specialised, knowledge-based systems acting on behalf of the user in some aspect of the interaction. Similar requirements for automatic adaptation have been noted in intelligent tutoring systems, natural-language systems and intelligent interfaces. The paper brings together the research which has emanated from a number of backgrounds, and provides a unifying perspective on adaptive systems in general. An architecture for adaptive systems and a methodology for their development are presented. The paper also describes software support for producing adaptive systems, and offers some experimental evidence to justify both the desirability and feasibility of exploiting an adaptive system approach to human-computer interaction     

Designing a feedback component of an intelligent tutoring system for foreign language

In this paper, we provide a model of corrective feedback generation for an intelligent tutoring system for Spanish as a foreign language. We have studied two kind of strategies: (1) Giving-Answer Strategies (GAS), where the teacher directly gives the desired target form or indicates the location of the error and (2) Prompting-Answer Strategies (PAS), where the teacher pushes the student less directly to notice and repair their own error. Based on different experimental settings and comparisons with face-to-face tutoring mode, we propose the design of a component of effective teaching strategies into ITS for Spanish as a foreign language.     

TEx-Sys model for building intelligent tutoring systems

Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student’s characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the first time, our approach to research, development and implementation related to intelligent tutoring systems and ITS authoring shells. Our research relies on the traditional intelligent tutoring system, the consideration that teaching is control of learning and principles of good human tutoring in order to develop the Tutor–Expert System model for building intelligent tutoring systems in freely chosen domain knowledge. In this way we can wrap up an ongoing process that has lasted for the previous fifteen years. Prototype tests with the implemented systems have been carried out with students from a primary education to an academic level. Results of those tests are advantageous, according to surveys, and the implemented and deployed software satisfies functionalities and actors’ demands.     

Automatic summary assessment for intelligent tutoring systems

Summary writing is an important part of many English Language Examinations. As grading students’ summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to support automatic evaluation of summaries. However, their performance is not satisfactory for assessing summary writings. To improve the performance, this paper proposes an ensemble approach that integrates LSA and n-gram co-occurrence. As a result, the proposed ensemble approach is able to achieve high accuracy and improve the performance quite substantially compared with current techniques. A summary assessment system based on the proposed approach has also been developed.     

Conversation rebuilding: From the foreign language classroom to implementation in an intelligent tutoring system

This paper shows how an innovative “communicative” technique in teaching foreign languages—Conversation Rebuilding (CR)—readily lends itself to implementation in an Intelligent Tutoring System (ITS). Classroom language teachers using CR get students to formulate acceptable utterances in a foreign idiom by starting from rough approximations (using words the students know) and gradually zeroing in on the utterance which a native speaker of that idiom might produce in a similar setting. The ITS presented here helps students do the “zeroing in” optimally. It lets them express themselves temporarily in an “interlingua” (i.e., in their own kind of French or English or whatever they are studying), as long as they make something of their communicative intent clear, that is, as long as the System can find a semantic starting point on which to build. The ITS then prods the students to express themselves more intelligibly, starting from the “key” elements (determined by a heuristic based on how expert classroom teachers proceed) and taking into consideration the students' past successful or unsuccessful attempts at communication. To simplify system design and programming, however, conversations are “constrained”: students playact characters in set dialogs and aim at coming up with what the characters actually say (not what they could possibly say). While most Intelligent Computer Assisted Language Learning (ICALL) focuses the attention of students on norms to acquire, the ICALL implementation of CR presented in this paper focuses the attention of students on saying something—indeed, almost anything—to keep the conversation going and get some kind of meaning across to the other party. It sees successful language acquisition primarily as the association of forms with intent, not simply as the conditioning of appropriate reflexes or the elaboration/recall of conceptualized rules (which are the by-products of successful communication). Thus, in espousing this hard-line communicative approach, the present paper makes a first, non-trivial point: ICALL researchers might usefully begin by investigating what the more able teachers are doing in the classroom, rather than by building elaborate computer simulations of out-dated practices, as happens all too often. The paper then goes on to describe the architecture of a prototype ITS based on CR—one that the authors have actually implemented and tested—for the acquisition of English as a foreign language. A sample learning session is transcribed to illustrate the man-machine interaction. Concluding remarks show how the present-day limits of ICALL (and Artificial Intelligence in general) can be partially circumvented by the strategy implemented in the program, i.e. by making the students feel they are creatively piloting an interaction rather than being tested by an unimaginative machine.     

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.