Student modelling by a knowledge-based system

ELECTRE is a project to build an intelligent tutoring system for learning basic electricity. This paper describes a student model based on the student's cognitive processes. This model includes, for each student, his/her domain knowledge and the specific heuristics. Moreover, it uses meta-knowledge of problem solving. This model is simulated by a knowledge-based system that controls the solving processes by meta-rules. A case study is presented.     

智能辅导系统

六十年代初期,计算机技术开始应用于教学领域,这时的计算机辅助教学(CAI)系统只能于简单的课程安排和进阶测验。七十年代,随着人工智能(AI)的不断发展和成熟,人们开始考虑智能性引     

An intelligent tutoring module controlled by BDI agents for an e-learning platform

INES (INtelligent Educational System) is an operative prototype of an e-learning platform. This platform includes several tools and technologies, such as: (i) semantic management of users and contents; (ii) conversational agents to communicate with students in natural language; (iii) BDI-based (Believes, Desires, Intentions) agents, which shape the tutoring module of the system; (iv) an inference engine; and (v) ontologies, to semantically model the users, their activities, and the learning contents. The main contribution of this paper is the intelligent tutoring module of the system. Briefly, the tasks of this module are to recognize each student (checking his/her system credentials) and to obtain information about his/her learning progress. So, it can be able to suggest to each student specific tasks to achieve his/her particular learning objectives, based on several parameters related to the existing learning paths and the student’s profile.     

Tutoring bishop-pawn endgames: An experiment in using knowledge-based chess as a domain for intelligent tutoring

Most research in computer chess has focused on creating an excellent chess player, with relatively little concern given to modeling how humans play chess. The research reported in this article is aimed at investigating knowledge-based chess in the context of building a prototype chess tutor, UMRAO, which helps students learn how to play bishop-pawn endgames. In tutoring it is essential to take a knowledge-based approach, since students must learn how to manipulate strategic concepts, not how to carry out large-scale lookahead searches.UMRAO uses an extension of Michie's advice language to represent expert and novice chess plans. For any given endgame, the system is able to compile the plans into a strategy graph, which elaborates strategies (both well formed and ill formed) that students might use as they solve the endgame problem. A strategy graph can be compiled off-line, where real-time performance is not important. Later, during tutoring, the strategy graph can be accessed quickly in order to understand a student's moves in terms of his or her strategies. With such understanding, UMRAO is able to provide appropriate knowledge-based feedback to the student. Anderson et al. have called this tutoring paradigm model tracing, but in the chess domain model tracing can be used without the need for immediate feedback that Anderson has required in his more complex abstract problem-solving domains. The chess domain thus allows experimentation with a variety of tutoring styles that range from immediate feedback to optional feedback, from strict tutor control of the feedback to student initiative in the choice of feedback. This points out UMRAO's most promising contribution: re-establishing chess as a vehicle for research in other areas of artificial intelligence, in this case intelligent tutoring systems. UMRAO also makes technical contributions to knowledge-based chess and to intelligent tutoring as well.     

A knowledge-based system for postgraduate engineering courses

This paper describes a novel approach to the development and implementation of an educational tool based on knowledge-based technology employing an expert system shell. Software has been developed which, after providing basic background information, proceeds to analyse the student's learning pattern to establish which next question, explanation, or topic to propound in order to aid the learning process and ensure that fundamental knowledge is gained by the student at his/her own pace. While the system described has been designed and implemented specifically to supplement, rather than supplant, the taught part of an MSc. course in Manufacturing Systems Engineering and Management, the methodology formulated can be used to develop similar knowledge-based systems for other technical as well as non-technical subjects at both undergraduate and postgraduate levels in Higher Education.     

基于人工情绪的智能情感网络教学系统研究

针时传统智能网络教学系统在情感教学方面的缺陷，基于人工情绪技术提出了一种Web环境下的智能情感网络教学系统结构．该系统由学习情绪模型、情绪教学模型、认知教学模型和学生模型等主要模块所构成，可以获取和识别每个学生的学习表情，并能够根据不同学生的学习情绪和学习效果，实现认知和情感相互协调的个性化教学．     

Student diagnosis in practice; bridging a gap

This paper presents a novel framework for looking at the problem of diagnosing a student's knowledge in an Intelligent Tutoring System. It is indicated that the input and the conceptualisation of the student model are significant for the choice of modeling technique. The framework regards student diagnosis as the process of bridging the gap between the student's input to the tutoring system, and the system's conception and representation of correct knowledge. The process of bridging the gap can be subdivided into three phases, data acquisition, transformation and evaluation, which are studied further. A number of published student modeling techniques are studied with respect to how they bridge the gap.     

Interactive reconstructive student modeling: A machine‐learning approach

Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved.

This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about the student to focus the search on the portion of the problem space the student is likely to traverse while solving the problem. Furthermore, the approach is not only incremental, but also truly interactive because it involves the student in explicit dialogs about his or her goals. In such a way, it is possible to determine whether the student knows the operator he or she is trying to apply. Pedagogical actions and the student model are generated interchangeably, thus allowing for dynamic adaptation of instruction, problem generation, and immediate feedback on student's errors. The approach presented is examined in the context of the symbolic integration tutoring system (SINT), an intelligent tutoring system (ITS) for the domain of symbolic integration.     

The design of the SCENT automated advisor

The SCENT (Student Computing ENvironmenT) project is concerned with building an intelligent tutoring system to help student programmers debug their LISP programs. The major thrust of current SCENT investigations is into the design of the SCENT advisor which is meant to provide debugging assistance to novice students. Six conceptual levels constitute the advisor. At the lowest level is the "raw data," consisting of the student's (possibly buggy) program. This can be interpreted by a "program behaviour" level which can produce traces, cross-reference charts, etc. from the student's program. These traces, etc. can be analyzed by "observers" for interesting patterns. At the next level are "strategy judges" and "diagnosticians" which determine which strategy the student has used in his/her program and bugs in this strategy. A "task expert" provides task-specific input into the process of analyzing the student's solution, and a "student-knowledge component" provides student-specific input into this process. Information from the six levels interacts in a variety of ways and control is similarly hierarchical. This necessitates a blackboard-style scheme to coordinate information dissemination and control flow.
This paper discusses the objectives of SCENT and focusses on organizing the process of debugging student programs. A complete example is given to illustrate how entities at the six levels interact and to indicate the kinds of information sharing that occur in the SCENT advisor. The paper concludes with an evaluation of the strengths and weaknesses of this approach to automated debugging, and suggestions about directions for further exploration.     

Abductive Theorem Proving for Analyzing Student Explanations to Guide Feedback in Intelligent Tutoring Systems

The Why2-Atlas tutoring system presents students with qualitative physics questions and encourages them to explain their answers through natural language. Although there are inexpensive techniques for analyzing explanations, we claim that better understanding is necessary for use within tutoring systems. In this paper we motivate and describe how the system creates and uses a deeper proof-based representation of student essays in order to provide students with substantive feedback on their explanations. We describe in detail the abductive reasoner, Tacitus-lite+, that we use within the tutoring system. We also discuss evaluation results for an early version of the Why2-Atlas system and a subsequent evaluation of the theorem-proving module. We conclude with the discussion of work in progress and additional future work for deriving more benefits from a proof-based approach for tutoring applications.     

A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.     

The Covering Law Model Applied to Dynamical Cognitive Science: A Comment on Joel Walmsley

In a 2008 paper, Walmsley argued that the explanations employed in the dynamical approach to cognitive science, as exemplified by the Haken, Kelso and Bunz model of rhythmic finger movement, and the model of infant preservative reaching developed by Esther Thelen and her colleagues, conform to Carl Hempel and Paul Oppenheim’s deductive-nomological model of explanation (also known as the covering law model). Although we think Walmsley’s approach is methodologically sound in that it starts with an analysis of scientific practice rather than a general philosophical framework, we nevertheless feel that there are two problems with his paper. First, he focuses only on the deductivenomological model and so neglects the important fact that explanations are causal. Second, the explanations offered by the dynamical approach do not take the deductive-nomological format, because they do not deduce the explananda from exceptionless laws. Because of these two points, Walmsley makes the dynamical explanations in cognitive science appear problematic, while in fact they are not.     

The double-edged effects of explanation prompts

Explanation prompts usually foster conceptual understanding. However, it has been claimed within cognitive load theory that prompts can take cognitive load to the upper limit when learning complex contents. Under such circumstances, prompts focusing the learners’ attention on specific aspects (e.g., conceptual aspects such as elaborations on domain principles) might have some costs: Other important aspects (e.g., procedural aspects such as how to calculate) cannot be processed deeply. Thus, we expected that conceptually-oriented explanation prompts would foster the detailedness of explanations, the number of elaborations on domain principles, and conceptual knowledge. In addition, we tested the influence of such prompts on the number of calculations performed during learning and procedural knowledge. We conducted an experiment in which we employed conceptually-oriented explanation prompts in a complex e-learning module on tax law. Tax law university students (N = 40) worked on this e-learning module under two conditions: (a) conceptually-oriented explanation prompts, (b) no prompts. The prompts led to double-edged effects: positive effects on the detailedness of explanations and on the number of elaborations on domain principles, as well as on conceptual knowledge and simultaneously negative effects on the number of calculations performed during learning as well as on procedural knowledge.     

Multi Level Knowledge in Modeling Qualitative Physics Learning

The goal of the reported research is the development of a computational approach that could help a cognitive scientist to interactively represent a learner's mental models, and to automatically validate their coherence with respect to the available experimental data. In a reported case-study, the student's mental models are inferred from questionnaires and interviews collected during a sequence of teaching sessions. These putative cognitive models are based on a theory of knowledge representation, derived from psychological results and educational studies, which accounts for the evolution of the student's knowledge over a learning period. The learning system WHY, able to handle (causal) domain knowledge, shows how to model the answers and the causal explanations given by the learner.     

An ontological approach to designers’ idea explanation style: Towards supporting the sharing of kansei-ideas in textile design

In human-centered design activities, each designer has his or her own ideas about emotional aspect (or kansei) of new products. It is a key issue to share this vague kansei-idea appropriately at the earliest stage of design activities. This paper shows a novel ontological engineering approach to support kansei-idea sharing. The approach focuses on an idea explanation style as the wisdom of the design team. Ontological engineering has been making contributions to systematize knowledge and vocabulary by modeling them. Needless to say, it is difficult to model the vague kansei-idea itself. However, if the modeled object is shifted from the kansei-idea to the kansei-idea explanation style, it can provide the benefit of modeling. We investigated the effectiveness of the ontological engineering approach, and concluded that to construct an ontological framework of designers’ explanations is especially useful regarding these points: clarification of the essence of the explanation style, discovery of problems in explanations, and analyzing difficulties in acquiring explanation style for novices. From the investigation, what we can support and how a support system should be designed became clear. Furthermore, we built a kansei-idea sharing support system, and obtained the results of its initial trials.     

Using a Student Model to Improve Explanation in an ITS

This paper presents the use of a student model to improve the explanations provided by an intelligent tutoring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to build the studdent model.The diagnosis is based on the comparison of the behaviours of the student and the expert.The student model is consulted by the “explainer”and “debugging”procedures in order to re-order the sequence of the explanation.     

Explanations in Mathematics Classrooms: A Discourse Analysis

Abstract

This article focuses on student explanations as a discourse practice central to mathematics teaching and learning. I discuss classrooms as hybrid discourse spaces and focus on how talk is used to accomplish social action. In doing so, I contrast several different social and sociomathematical norms for explanation and suggest that students’ choices of discourse practices position them within the classroom. Further, I caution educators against assuming that complete and detailed explanations are always best to support student learning. I discuss how explanations that are coconstructed by several students can actually support joint engagement in mathematical work and help peers stay “on the same page” while avoiding hierarchical positioning.     

Cooperative learning strategies for intelligent tutoring systems

Intelligent tutoring systems (ITS) are evolving towards a more cooperative relationship between the system and the student. More and more, learning is considered as a constructive process rather than a simple transfer of knowledge. This trend has brought to light new cooperative tutoring strategies. One of these tutoring strategies, the learning companion, designed to overcome some of the limitations of the classical tutoring model, involves a student and two simulated participants: a tutor and another student. More recently, a new strategy, learning by disturbing, has been proposed. In this strategy, the simulated student is a troublemaker whose role is to deliberately disturb the human student. This article describes the learning by disturbing strategy by contrasting it with the learning companion strategy. In addition, links are drawn between this new strategy and the psychology of learning, in particular the cognitive dissonance theory. An indicator has been developed that measures discord between the ideas, helping to pinpoint the concepts that are most likely to be misunderstood by the learner. Doing so allows one to plan more efficiently the interventions of the troublemaker.     

一种基于Muti-agent的个性化网络教学系统框架

当前智能教学系统对于用户提出的个性化学习服务实现效果尚不理想。本文将agent技术引入智能教学系统,构建了一种基于多agent的个性化网络教学系统框架,分析了框架内各agent的功能和工作过程,提出了一个学生学习个性化特征提取和处理的模型。通过动态跟踪学习记录,采用向量相似度计算寻找一种与学习者相适应的学习风格,为学生提供个性化的学习服务。