Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student’s behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student’s knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.     

Agent based affective tutoring systems: A pilot study

An important trend in the development of Intelligent tutoring systems (ITSs) has been that providing the student with a more personalized and friendly environment for learning. Many researchers now feel strongly that the ITSs would significantly improve performance if they could adapt to the affective state of the learner. 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. However, ATSs are not widely employed in the tutoring system market. In this paper, a survey was conducted to investigate the critical factors affecting learner’s satisfaction in ATSs based on an ATS developed by us. The results revealed that learner’s attitude toward affective computing, agent tutor’s expressiveness, emotion recognition accuracy, number of emotions recognized by agent tutor, pedagogical action and easy of the use of the system have significant influence on learner’s satisfaction. The results indicate institutions how to further strengthen the ATSs’ implementation.     

基于认知诊断和神经网络的试题得分预测

随着教育越来越走向信息化,大量的教育数据会被保存,在海量教育数据中挖掘出学生的潜在信息是智能教育中非常值得研究的问题之一.针对目前大多数得分预测都是预测一个总分,无法具体到每一题得分预测的问题,对考试中存在的主要题型进行了研究,结合现有试题得分预测方法,指出其优势与不足,提出基于认知诊断和神经网络分别预测客观题和主观题...     

The cognitive apprenticeship analogue: a strategy for using ITS technology for the delivery of instruction and as a research tool for the study of teaching and learning

In this paper, a general overview of the components which have characterized the development of intelligent tutoring systems (ITS) over the past fifteen years is provided. Accompanying the overview of each component is a discussion of limitations which, we feel, restrict the extent to which ITS technology can be useful as an instructional delivery vehicle and as a tool which can be used to learn about the processes which underlie teaching and learning.These limitations, however, can be compensated for by altering how and for what purposes ITSs are developed and implemented. Our goal in writing this paper, in addition to discussing the problems associated with present ITS approaches, is to present a set of suggestions which we feel can guide the development and implementations of ITSs such that their potential for useful instructional tools can be enhanced and extended. We argue for the development of ITSs which: (i) progressively and as much as possible reduce the a priori restrictions that are placed on learners as they learn new content. Technology should empower the learner as a learner, enabling him/her to uncover the “mysteries” of new knowledge. Technology should not rob learners of the joy of discovery the “aha!” experience. It should, however, facilitate the integration of new findings into existing cognitive frameworks, and provide opportunities for learners to examine and expose misunderstandings and misconceptions about how the “universe” operates; (ii) enable educators to reliably determine and report what is being learned and mastered against some set of standards which exist independent of the learning environment; and (iii) provide opportunities for educators to learn about how learning is occurring and intercede (real-time if necessary) in ways that can alter and improve (either temporarily or permanently) the environment within which student interactions are occurring.     

Pedagogical agent in Multimedia Interactive Modules for Learning – MIMLE

Using new game-based learning systems in college education is neither an easy nor a simple task. The aim of such systems is to keep attention, teach students or assess their knowledge through a game. With the aim of keeping students’ attention through a game, in this paper we show the implementation of game-based learning systems with a pedagogical agent. We presents two models for assessing student’s knowledge used by a pedagogical agent which is a part of the new class of Multimedia Interactive Modules for Learning – MIMLE. One of the models is used for activating the agent. It is realized as a window of Help option and built in accordance to Marcov decision process theory (MDP). The basic goal of this mode is to determine the minimal intervention of the agent towards making the right direction concerning the studying process based on simulation learning. With the second, long-term model, we have assessed student’s knowledge in the current game level that is used to decide students should pass on to the next level of learning or if they should stay on the same level.     

Student modeling approaches: A literature review for the last decade

This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10 years are described, the aspects of students’ characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student’s mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students’ learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling.     

Temporal Data Mining for Educational Applications

Intelligent tutoring systems (ITSs) acquire rich data about students' behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.     

Ontology based approach to Bayesian student model design

Probabilistic student model based on Bayesian network enables making conclusions about the state of student’s knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine “a priori” probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable non-empirical mathematical determination of conditional probabilities, while “a priory” probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining “a priori” probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented.     

Student modeling for a web-based self-assessment system

Detecting weaknesses in students’ knowledge may constitute an objective of testing. Computerized test systems, which can be tailored to students’ knowledge level, are appropriate to realize this objective. These systems are not only used to reveal the students’ knowledge, but also help tutors understand the problems in the educational process. This paper reviews the student modeling problem for computer-based test systems, and also proposes a novel method for the graphical representation of student knowledge. First, we present our test practice system ‘Intelligent’, which is followed by the evaluation of the system in real classroom conditions as well as the effect over students’ knowledge acquisition.     

“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.     

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.     

Knowledge-based modeling for disruption management in urban distribution

Disruption management in urban distribution is the process of achieving a new distribution plan in order to respond to a disruption in real time. Experienced schedulers can respond to disruptions quickly with common sense and past experiences, but they often achieve the new distribution plan by a fuzzy, sometimes inconsistent, and not well-understood way. The method is limited when the problem becomes large scale or more complicated. In this case, optimization techniques consisting of models and algorithms may complement it. However, as the distribution system’s state changes constantly with the plan-executing process and disruptions are diversified, real-time modeling is very difficult. Hence in order to achieve the real-time modeling process, the research in the paper focuses on a knowledge-based modeling method, which combines the knowledge of experienced schedulers with the OR knowledge concerning models and algorithms. Policies, algorithms and models are represented by proper knowledge representation schemes in order to support automated or semi-automated modeling by computers. The modeling process is demonstrated by a case to show how the different kinds of knowledge representation schemes cooperate with each other to support the modeling process. In the knowledge-based modeling process, based on the knowledge of experienced schedulers, a qualitative policy for handling the disruption based on the current distribution system’s state is achieved firstly; and then based on OR knowledge, the corresponding model and algorithm are constructed to quantitatively optimize the policy. The integration of the two kinds of knowledge not only effectively supports the real-time modeling process, but also combines the advantages of both to achieve more practical and scientific solutions to different kinds of disruptions occurring under different distribution system’s states.     

谈艺术设计专业素描教学的创造性思维的开发

艺术设计专业素描基础教学应摆脱传统的绘画式教学模式,注重对学生的艺术思维及观念的开发与培养,提倡在教学中培养学生的设计意识,在具体教学中,引导学生把构成知识与素描结合起来,通过教师精心设计的课题训练,使教学过程中的设计性、试验性引发学生的训练兴趣,强调学生寻找自己的语言方式,强化对画面意念的表达及语言个性化的营造,从而使教学尽可能地挖掘学生的艺术潜力和开发其主观创造性思维。     

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.     

A fuzzy linguistic algorithm for adaptive test in Intelligent Tutoring System based on competences

The Computerized Adaptive Tests (CAT) are common tools for the diagnosis process in Intelligent Tutor System based on Competency education (ITS-C). The item selection process to form a CAT plays a key role because it must ensure the selection of the item that best contributes to student assessment at any time. The item selection mechanisms proposed in the literature present some limitations that decrease the efficiency of CAT and its adaptation to the student profile. This paper introduces a new item selection algorithm, based on a multi-criteria decision model that integrates experts’ knowledge modeled by fuzzy linguistic information that overcomes previous limitations and enhances the accuracy of diagnosis and the adaptation of CAT to student’s competence level. Finally, such an algorithm is deployed in a mobile tool for an ITS-C.     

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.     

SEDAF: AN INTELLIGENT EDUCATIONAL SYSTEM FOR MATHEMATICS

The field of artificial intelligence and education, in which AI techniques and methodologies are used to build sophisticated intelligent educational systems, is developing rapidly. In this paper we present an intelligent educational system for teaching high school and college students how to analyze and draw graphs of mathematical functions. The system, named SEDAF, has been developed in a knowledge engineering environment and runs on a Lisp-machine workstation. We illustrate the various modules constituting SEDAF: the user interface; an expert module, capable of solving problems in the subject domain; a diagnosis module, which points out possible reasons for students' errors; a student modeling module, capable of building an explicit representation of the learning status of the student; and a remedial subsystem, called a therapy module, constituted by means-ends tutorial rules that execute teaching actions on the base of the status of the student model. The goal of the presentation is to stress the innovative aspects of the architecture of SEDAF, in particular the use of metalevel knowledge to embed in the system the teaching expertise that allows the system to personalize its behavior to the specific student and to pursue a didactic plan.     

Gaze tutor: A gaze-reactive intelligent tutoring system

We developed an intelligent tutoring system (ITS) that aims to promote engagement and learning by dynamically detecting and responding to students' boredom and disengagement. The tutor uses a commercial eye tracker to monitor a student's gaze patterns and identify when the student is bored, disengaged, or is zoning out. The tutor then attempts to reengage the student with dialog moves that direct the student to reorient his or her attentional patterns towards the animated pedagogical agent embodying the tutor. We evaluated the efficacy of the gaze-reactive tutor in promoting learning, motivation, and engagement in a controlled experiment where 48 students were tutored on four biology topics with both gaze-reactive and non-gaze-reactive (control condition) versions of the tutor. The results indicated that: (a) gaze-sensitive dialogs were successful in dynamically reorienting students’ attentional patterns to the important areas of the interface, (b) gaze-reactivity was effective in promoting learning gains for questions that required deep reasoning, (c) gaze-reactivity had minimal impact on students’ state motivation and on self-reported engagement, and (d) individual differences in scholastic aptitude moderated the impact of gaze-reactivity on overall learning gains. We discuss the implications of our findings, limitations, future work, and consider the possibility of using gaze-reactive ITSs in classrooms.     

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.     

Design and evaluation of visualization support to facilitate decision trees classification

The loosely coupled relationships between visualization and analytical data mining (DM) techniques represent the majority of the current state of art in visual data mining; DM modeling is typically an automatic process with very limited forms of guidance from users. A conceptual model of the visualization support to DM modeling process and a novel interactive visual decision tree (IVDT) classification process have been proposed in this paper, with the aim of exploring humans’ pattern recognition ability and domain knowledge to facilitate the knowledge discovery process. An IVDT for categorical input attributes has been developed and experimented on 20 subjects to test three hypotheses regarding its potential advantages. The experimental results suggested that, compared to the automatic modeling process as typically applied in current decision tree modeling tools, IVDT process can improve the effectiveness of modeling in terms of producing trees with relatively high classification accuracies and small sizes, enhance users’ understanding of the algorithm, and give them greater satisfaction with the task.