Using Dialogue Features to Predict Trouble During Collaborative Learning

A web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent electronic agent that will aid them in their learning has the potential for improving on-line learning. The agent would follow the discussion and interact with the participants when it detects learning trouble of some sort, such as confusion about the problem they are working on or a participant who is dominating the discussion or not interacting with the other participants. In order to recognize problems in the dialogue, we investigated conversational elements that can be utilized as predictors for effective and ineffective interaction between human students. These elements can serve as the basis for student and group models. In this paper, we discuss group interaction during collaborative learning, our representation of participant dialogue, and the statistical models we are using to determine the role being played by a participant at any point in the dialogue and the effectiveness of the group. We also describe student and group models that can be built using conversational elements and discuss one set that we built to illustrate their potential value in collaborative learning. An erratum to this article is available at .     

INSTRUCT: Modeling students by asking questions

The paper reports an approach to inducing models of procedural skills from observed student performance. The approach, referred to as INSTRUCT, builds on two well-known techniques, reconstructive modeling and model tracing, at the same time avoiding their major pitfalls. INSTRUCT does not require prior empirical knowledge of student errors and is also neutral with respect to pedagogy and reasoning strategies applied by the student. Pedagogical actions and the student model are generated on-line, which allows for dynamic adaptation of instruction, problem generation and immediate feedback on student's errors. Furthermore, the approach is not only incremental but truly active, since it involves students in explicit dialogues about problem-solving decisions. Student behaviour is used as a source of information for user modeling and to compensate for the unreliability of the student model. INSTRUCT uses both implicit information about the steps the student performed or the explanations he or she asked for, and explicit information gained from the student's answers to direct question about operations being performed. Domain knowledge and the user model are used to focus the search on the portion of the problem space the student is likely to traverse while solving the problem at hand. The approach presented is examined in the context of SINT, an ITS for the domain of symbolic integration.     

基于Web的智能教学系统的研究与实现

基于Web的智能教学系统综合传统的ITS系统优势,同时又结合Web的特点,系统具有智能性,能够智能地引导学生学习。它是以认知科学为理论基础,综合利用人工智能技术、教育心理学、计算机科学等多门学科的成果而形成的一种对学生实施有效教学的技术。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,并探讨了学生模型的学习评价,最后对实现系统的关键技术进行了研究。     

Using Co-Lab to build System Dynamics models: Students’ actions and on-line tutorial advice

Modeling offers a promising form of constructivist learning for students. By making and executing models of dynamic systems in a computer environment, students are stimulated to learn about the specific domain that is modeled as well as about the process of modeling in general. However, learning by modeling also leads to characteristic student mistakes, based on a combination of faulty domain knowledge and insufficient modeling skills. In this article, we describe a method of generating advice to students during their modeling process. The on-line advice system was informed by our observations of a teacher who gave advice via a textual communication tool to students building models with a System Dynamics model editor. The first version of the on-line advice system was evaluated in two ways: first, three teachers evaluated the advice the system generated for students’ final solutions; second, we analyzed the advice the system provided as it was used by a sample of students who were building a physics model. These evaluations showed that the overall approach, including matching a student solution to a family of reference solutions together with the other mechanisms of the advice system, is valid. However, they also highlighted the difficulty of building ‘intelligent’ support to help students to improve their models and gain modeling expertise. The article concludes with a discussion of our current efforts to improve the advice system based on the lessons learnt, which suggest extension of the range of solution representations and of the operations of the advice method.     

Extending Domain Theories: Two Case Studies in Student Modeling

By its very nature, artificial intelligence is concerned with investigating topics that are ill-defined and ill-understood. This paper describes two approaches to expanding a good but incomplete theory of a domain. The first uses the domain theory as far as possible and fills in specific gaps in the reasoning process, generalizing the suggested missing steps and adding them to the domain theory. The second takes existing operators of the domain theory and applies perturbations to form new plausible operators for the theory. The specific domain to which these techniques have been applied is high-school algebra problems. The domain theory is represented as operators corresponding to algebraic manipulations, and the problem of expanding the domain theory becomes one of discovering new algebraic operators. The general framework used is one of generate and test—generating new operators for the domain and using tests to filter out unreasonable ones. The paper compares two algorithms, INFER^* and MALGEN, examining their performance on actual data collected in two Scottish schools and concluding with a critical discussion of the two methods.     

基于云模型的Agent 联盟评价

针对多 Agent 系统中影响联盟功效的因素存在较强的模糊性和不确定性的问题,将定性定量转换模型—–云模型引入 Agent 联盟评价中,提出一种新的基于云模型的联盟评价方法.与已有方法相比,该方法简单有效,易于工程实现,可较好地处理评价指标的模糊性和不确定性,从而为复杂控制系统中的最佳联盟选择提供更科学、更合理的决策.     

Methods and techniques of adaptive hypermedia

Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.     

基于模糊理论的智能呼叫决策系统的研究与设计

针对呼叫系统中接入控制的决策问题,从影响呼叫客户重要性的四个因素出发,构造了客户重要程度评估集,并提出了在呼叫系统中基于模糊综合评判的呼叫评估方法及相关算法实现,较好地解决了处理客户呼叫的问题,然后设计了智能呼叫决策系统,最后通过与非智能化系统的损失量比较,验证了该智能决策系统的有效性和可行性。     

科技项目在线协同评审工作流系统设计与实现*

基于协同工作流技术设计了科技项目在线协同评审模型,并对系统予以实现。协同工作流系统以信息沟通便捷的特点实现了人机协同,通过过程整合有效地提高了工作效率。     

Tradeoff and Sensitivity Analysis in Software Architecture Evaluation Using Analytic Hierarchy Process

Software architecture evaluation involves evaluating different architecture design alternatives against multiple quality-attributes. These attributes typically have intrinsic conflicts and must be considered simultaneously in order to reach a final design decision. AHP (Analytic Hierarchy Process), an important decision making technique, has been leveraged to resolve such conflicts. AHP can help provide an overall ranking of design alternatives. However it lacks the capability to explicitly identify the exact tradeoffs being made and the relative size of these tradeoffs. Moreover, the ranking produced can be sensitive such that the smallest change in intermediate priority weights can alter the final order of design alternatives. In this paper, we propose several in-depth analysis techniques applicable to AHP to identify critical tradeoffs and sensitive points in the decision process. We apply our method to an example of a real-world distributed architecture presented in the literature. The results are promising in that they make important decision consequences explicit in terms of key design tradeoffs and the architecture's capability to handle future quality attribute changes. These expose critical decisions which are otherwise too subtle to be detected in standard AHP results. Liming Zhu is a PHD candidate in the School of Computer Science and Engineering at University of New South Wales. He is also a member of the Empirical Software Engineering Group at National ICT Australia (NICTA). He obtained his BSc from Dalian University of Technology in China. After moving to Australia, he obtained his MSc in computer science from University of New South Wales. His principle research interests include software architecture evaluation and empirical software engineering. Aybüke Aurum is a senior lecturer at the School of Information Systems, Technology and Management, University of New South Wales. She received her BSc and MSc in geological engineering, and MEngSc and PhD in computer science. She also works as a visiting researcher in National ICT, Australia (NICTA). Dr. Aurum is one of the editors of “Managing Software Engineering Knowledge”, “Engineering and Managing Software Requirements” and “Value-Based Software Engineering” books. Her research interests include management of software development process, software inspection, requirements engineering, decision making and knowledge management in software development. She is on the editorial boards of Requirements Engineering Journal and Asian Academy Journal of Management. Ian Gorton is a Senior Researcher at National ICT Australia. Until Match 2004 he was Chief Architect in Information Sciences and Engineering at the US Department of Energy's Pacific Northwest National Laboratory. Previously he has worked at Microsoft and IBM, as well as in other research positions. His interests include software architectures, particularly those for large-scale, high-performance information systems that use commercial off-the-shelf (COTS) middleware technologies. He received a PhD in Computer Science from Sheffield Hallam University. Dr. Ross Jeffery is Professor of Software Engineering in the School of Computer Science and Engineering at UNSW and Program Leader in Empirical Software Engineering in National ICT Australia Ltd. (NICTA). His current research interests are in software engineering process and product modeling and improvement, electronic process guides and software knowledge management, software quality, software metrics, software technical and management reviews, and software resource modeling and estimation. His research has involved over fifty government and industry organizations over a period of 15 years and has been funded from industry, government and universities. He has co-authored four books and over one hundred and twenty research papers. He has served on the editorial board of the IEEE Transactions on Software Engineering, and the Wiley International Series in Information Systems and he is Associate Editor of the Journal of Empirical Software Engineering. He is a founding member of the International Software Engineering Research Network (ISERN). He was elected Fellow of the Australian Computer Society for his contribution to software engineering research.     

Methodological Approaches to Development and Implementation of Multi-agents Investment DSS Using JADE Platform

In these latter days software agents are used for the development and implementation of intellectual decision support systems. In order to implement intelligence in a system some or several dozen of software agents are used and the made system becomes multi-agent. For the development of these systems a set of methodologies, i.e., the sequence of consequent steps of analysis, designing and implementation, is offered. The carried out analysis of the methodologies showed that as a rule they are limited by the spectrum of their pending problem （within the pales of the requirements of specific applied task, within the pales of the possibilities of technical implementation） or within the pales of amount of detail. The variety of methodologies is influenced by the fact that for the development of these systems the requirements and attitudes are offered by the specialists of related spheres such as software, numeral intellect engineers. In the course of the development of hardware and software appeared possibilities to implement mobile multi-agents systems, however, there is no one united mobile multi-agent systems design methodology, whereas existing systems are underdeveloped and their number is small. In this article we introduce the course of the designing of an intellectual real time multi-agent investment management decision support information system adapting and combining some methodologies where the choice to use either communicating or mobile agents is the question of rather technical implementation than methodological. In the article we introduce two ways of system implementation by JADE platform： the first one-using communicating agents, and the second one-using mobile agents.     

智能决策支持技术在远程教育系统中的应用

通过分析目前现代远程教育中比较紧迫的4类决策问题，介绍了一种基于模型库的远程教育智能决策支持系统框架下的IDSS的设计与实现，讨论了系统建模及算法实现、模型的存储、模型库的管理及维护等技术。     

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

The popularity of intelligent tutoring systems (ITSs) is increasing rapidly. In order to make learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner or the context. One of the possible adaptation techniques is adaptive item sequencing by matching the difficulty of the items to the learner's knowledge level. This is already accomplished to a certain extent in adaptive testing environments, where the test is tailored to the person's ability level by means of the item response theory (IRT). Even though IRT has been a prevalent computerized adaptive test (CAT) approach for decades and applying IRT in item‐based ITSs could lead to similar advantages as in CAT (e.g. higher motivation and more efficient learning), research on the application of IRT in such learning environments is highly restricted or absent. The purpose of this paper was to explore the feasibility of applying IRT in adaptive item‐based ITSs. Therefore, we discussed the two main challenges associated with IRT application in such learning environments: the challenge of the data set and the challenge of the algorithm. We concluded that applying IRT seems to be a viable solution for adaptive item selection in item‐based ITSs provided that some modifications are implemented. Further research should shed more light on the adequacy of the proposed solutions.     

Stakeholder requirement evaluation of smart industrial service ecosystem under Pythagorean fuzzy environment for complex industrial contexts: A case study of renewable energy park

This study focuses on ways to systematically evaluate stakeholder requirements when developing a smart industrial service ecosystem (SISE) in a complex industrial context. The SISE development requires considering the service requirement from both the complex industrial context and service ecosystem manners. This study proposes a systematic framework for stakeholder requirement evaluation in SISE. The first part of the framework is the industrial context-viable system model with ecological thinking (IC-VESM) to elicit the service requirements for the SISE, which facilitates a systematic analysis of the service value proposition and service requirement elicitation in the operational lifecycle of an entire industrial context. This second part of the framework proposes a method for evaluating service requirements that is both feasible and systematic. This is achieved by combining the Fuzzy Kano and AHP methods in a Pythagorean fuzzy (PF) environment. The PF Kano computes the categories and determines the weights of service requirements from a consumer perspective, while the PF AHP hierarchically analyzes the service requirements and provides pairwise comparison paths for design experts. Finally, an illustrative case study in a renewable energy context was used to demonstrate the feasibility and effectiveness of the methodology. The proposed theoretical model provides more reliable and systematic outcomes than traditional methods when eliciting service requirements and evaluating complex smart industrial service solutions. The study has practical implications by providing useful insights for companies to recognize key smart service requirements in complex industrial contexts and to improve sustainable development.     

Intelligent Scheduling with GUESS (Generically Used Expert Scheduling System): Development and Testing Results

Scheduling is a prevalent function that is omnipresent throughout many industries and applications (Pinedo, 1995; Morton & Pentico, 1993; Zweben & Fox, 1994; Brown & Scherer, 1995). A great need exists for developing scheduling toolkits that can be generically applied to a number of different scheduling problems. To meet this need, more research is warranted for developing a state-of-the-art generic constraint problem-solver as related to scheduling. This paper discusses the development and testing of the Generically Used Expert Scheduling System (GUESS).     

Using preference leveled evaluation functions to construct fuzzy measures in decision making and evaluation

This work discusses some new types of bipolar preferences and defines conservativeness of them. Then, the study defines parameterized fuzzy measures and proposes three methods to generate them. Based on given conservativeness preference and some special evaluation functions, we majorly discuss preference leveled evaluation functions method to construct fuzzy measures, allowing the generated fuzzy measures to effectively embody the preferences of decision makers. We also present some related properties such as different types of monotonicities, with three numerical instances showing the applicability of the proposed methods.     

Assumption‐Based Argumentation Equipped with Preferences and its Application to Decision Making,Practical Reasoning,and Epistemic Reasoning

The existing approaches that map the given explicit preferences into standard assumption‐based argumentation (ABA) frameworks reveal some difficulties such as generating a huge number of rules. To overcome them, we present an assumption‐based argumentation framework equipped with preferences (p_ABA). It increases the expressive power of ABA by incorporating preferences between sentences into the framework. The semantics of p_ABA is given by extensions, which are maximal among extensions of ABA with regard to the extension ordering “lifted” from the given sentence ordering. As a theoretical contribution of this study, we show that prioritized logic programming can be formulated as a specific form of p_ABA. The advantage of our approach is that not only does p_ABA enable us to express different kinds of preferences such as preferences over rules, over goals, or over decisions by means of sentence orderings but we can also successfully obtain solutions from extensions of the p_ABA expressing the respective knowledge for various applications such as epistemic reasoning, practical reasoning, and decision making with preferences in a uniform and domain‐independent way without suffering from difficulties of the existing approaches.     

The growth of service and the service of growth: Using system dynamics to understand service quality and capital allocation

This paper discusses how system dynamics can help understand a service company's growth potential as well as its limitations. The model discussed here is being built for a European restaurant chain which grew from nothing to over 200 outlets in less than a decade. The model highlights two conflicting pressures: the need to spend on meeting customer expectations and hence build sales versus the need to meet profit targets from headquarters and thus win the capital to fund expansion. We use the model to study how management policies affect the achievable rate of growth. The issues discussed are relevant to any service based company facing the problem of maintaining and improving service quality against the pressure of performance expectations set by shareholders or corporate owners. We also briefly discuss the benefit of using such models for executive training. The model will be used as a basis for educating the emerging generation of managers who will have to cope with the tensions described in the model.