Showing papers on "Intelligent tutoring system published in 2004"

Affect and Learning: An Exploratory Look into the Role of Affect in Learning with AutoTutor

Abstract: The role that affective states play in learning was investigated from the perspective of a constructivist learning framework. We observed six different affect states (frustration, boredom, flow, confusion, eureka and neutral) that potentially occur during the process of learning introductory computer literacy with AutoTutor, an intelligent tutoring system with tutorial dialogue in natural language. Observational analyses revealed significant relationships between learning and the affective states of boredom, flow and confusion. The positive correlation between confusion and learning is consistent with a model that assumes that cognitive disequilibrium is one precursor to deep learning. The findings that learning correlates negatively with boredom and positively with flow are consistent with predictions from Csikszentmihalyi’s analysis of flow experiences.

Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration

Abstract: Intelligent tutoring systems are quite difficult and time inten- sive to develop. In this paper, we describe a method and set of software tools that ease the process of cognitive task analysis and tutor development by allowing the author to demonstrate, instead of programming, the behav- ior of an intelligent tutor. We focus on the subset of our tools that allow authors to create "Pseudo Tutors" that exhibit the behavior of intelligent tu- tors without requiring AI programming. Authors build user interfaces by di- rect manipulation and then use a Behavior Recorder tool to demonstrate al- ternative correct and incorrect actions. The resulting behavior graph is an- notated with instructional messages and knowledge labels. We present some preliminary evidence of the effectiveness of this approach, both in terms of reduced development time and learning outcome. Pseudo Tutors have now been built for economics, analytic logic, mathematics, and language learn- ing. Our data supports an estimate of about 25:1 ratio of development time to instruction time for Pseudo Tutors, which compares favorably to the 200:1 estimate for Intelligent Tutors, though we acknowledge and discuss limitations of such estimates.

An Intelligent Tutoring System for Entity Relationship Modelling

Abstract: The paper presents KERMIT, a Knowledge-based Entity Relationship Modelling Intelligent Tutor. KERMIT is a problem-solving environment for the university-level students, in which they can practise conceptual database design using the Entity-Relationship data model. KERMIT uses Constraint-Based Modelling (CBM) to model the domain knowledge and generate student models. We have used CBM previously in tutors that teach SQL and English punctuation rules. The research presented in this paper is significant because we show that CBM can be used to support students learning design tasks, which are very different from domains we dealt with in earlier tutors. The paper describes the system's architecture and functionality. The system observes students' actions and adapts to their knowledge and learning abilities. KERMIT has been evaluated in the context of genuine teaching activities. We present the results of two evaluation studies with students taking database courses, which show that KERMIT is an effective system. The students have enjoyed the system's adaptability and found it a valuable asset to their learning.

ActiveMath: An Intelligent Tutoring System for Mathematics

Abstract: ActiveMath is a web-based intelligent tutoring system for mathematics. This article presents the technical and pedagogical goals of ActiveMath, its principles of design and architecture, its knowledge representation, and its adaptive behavior. In particular, we concentrate on those features that rely on AI-techniques.

Tactical Language Training System: An Interim Report

Abstract: Tactical Language Training System helps learners acquire basic communicative skills in foreign languages and cultures Learners practice their communication skills in a simulated village, where they must develop rapport with the local people, who in turn will help them accomplish missions such as post-war reconstruction Each learner is accompanied by a virtual aide who can provide assistance and guidance if needed, tailored to each learner’s individual skills The aide can also act as a virtual tutor as part of an intelligent tutoring system, giving the learners feedback on their performance Learners communicate via a multimodal interface, which permits them to speak and choose gestures on behalf of their character in the simulation The system employs video game technologies and design techniques, in order to motivate and engage learners A version for Levantine Arabic has been developed, and versions for other languages are in the process of being developed

A Prototype for an Intelligent Tutoring System for Students Learning to Program in Java

Abstract: The “Java Intelligent Tutoring System” (JITS) research project involves the development of a programming tutor designed for students in their first programming course in Java at the College or University level. This paper presents an overview of the architectural design including state-of-the-art web-based distributed architecture, the AI techniques used, and the programmer-optimized user interface. This project is a prototype being constructed which will model the domain of a small subset of the Java programming language in a very specific context. Research is in progress and it is hypothesized that the completed prototype will be sufficient to prove the concept and that a fully developed Java Intelligent Tutoring System will provide an interactively-rich learning environment for students that will result in increased achievement. Based on the success of similar Intelligent Tutoring Systems, it is also hypothesized that these students will be able to learn programming skills and gain knowledge more quickly and effectively than students in traditional educational settings.

Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities

Abstract: In this paper, we present the architecture and describe the functionality of an Intelligent Tutoring System (ITS), which uses an expert system to make decisions during the teaching process. The expert system uses neurules for knowledge representation of the pedagogical knowledge. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The expert system consists of three components: the user modelling unit, the pedagogical unit and the inference system. The pedagogical knowledge is distributed in a number of neurule bases within the user modelling and the pedagogical unit. Another important component of the ITS, for both its development and maintenance, is its knowledge management unit, which provides knowledge acquisition and knowledge update capabilities to the system, that is, offers expert knowledge authoring capabilities to the system.

An Open Learner Model for Children and Teachers: Inspecting Knowledge Level of Individuals and Peers

Abstract: This paper considers research on open learner models, which are usually aimed at adult learners, and describes how this has been applied to an intelligent tutoring system for 8-9 year-old children and their teachers. We introduce Subtraction Master, a learning environment with an open learner model for two and three digit subtraction, with and without adjustment (borrowing). It was found that some children were quite interested in their learner model and in a comparison of their own progress to that of their peers, whereas others did not demonstrate such interest. The level of interest and engagement with the learner model did not clearly relate to ability.

A collaborative intelligent tutoring system for medical problem-based learning

Abstract: This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. Students can sketch directly on medical images, search for medical concepts, and sketch hypotheses on a shared workspace. The prototype system incorporates substantial domain knowledge in the area of head injury diagnosis. A major challenge in building COMET has been to develop algorithms for generating tutoring hints. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. We compared the tutoring hints generated by COMET with those of experienced human tutors. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773).

Constructing Knowledge from Dialog in an Intelligent Tutoring System: Interactive Learning, Vicarious Learning, and Pedagogical Agents

Abstract: College students either interacted directly with an intelligent tutoring system, called AutoTutor, by contributing to mixed initiative dialog, or they simply observed, as vicarious learners, previously recorded interactive sessions. The mean pretest to posttest effect size (Cohen's d) across two studies was 1.86 in the interactive conditions and 1.12 in standard vicarious conditions. In Experiment 1, redundant onscreen printed text produced an effect size of 0.43, but the difference was not significant. In addition, the image of a talking head presenting AutoTutor's contributions to the dialog while displaying facial expressions, gestures, and gaze did not produce learning gains beyond those produced by the voice alone. In Experiment 2, the effect size was 0.71 when interactive tutoring was contrasted with the standard vicarious condition, but only 0.38 when compared to a collaborative vicarious condition. ********** Recent advances in educational technology, particularly computer-based courses (Anderson, Corbett, Koedinger, & Pelletier, 1995; Mayer, 2001), and distance learning (Barker & Dickson, 1996; Bourdeau & Bates, 1997; Moore & Kearsley, 1996; Roblyer & Edwards, 2000; Renwick, 1996; Spector, 2001), have created situations where learners are more and more likely to find themselves trying to gain knowledge in settings in which they are observers (Cox, Mckendree, Tobin, Lee, & Mayes, 1999; Fox Tree, 1999; Schober & Clark, 1989), rather than active participants. These advances have created a need for further empirical understanding of the conditions that promote learning among relatively isolated observers (e.g., Lee, Dineen, & McKendree, 1998; McKendree, Stenning, Mayes, Lee, & Cox, 1998). Little is currently known about how much is acquired by observers when compared to active participants in multimedia educational environments that are designed to promote learning (Mayer, 2001; Sweller, 1999; Wittrock, 1990). To address this issue, the present research was designed, in part, to contrast the relative learning gains of observers (i.e., vicarious learners) when compared to active participants in the learning process (Bandura, 1977; Lee et al., 1998; McKendree et al., 1998). Historically, the term vicarious learning was frequently used synonymously with observational learning, social learning, or modeling (Bandura, 1962; Rosenthal & Zimmerman, 1978). According to this perspective, by simply observing activities carried out by others, learners can master those activities without overt practice or direct incentives (Rosenthal & Zimmerman, 1978, p.xi). In two experiments, learners either interacted directly with an intelligent tutoring system (ITS), called AutoTutor (Graesser, Wiemer-Hastings, Wiemer-Hastings, Kreuz, & TRG, 1999), or they simply observed an interactive sessions. Experiment 2 also included a collaborative vicarious-learning condition. CONSTRUCTIVISM AND INTERACTIVE LEARNING According to constructivism, learners actively create meaning and knowledge by interacting with people and other objects. Rather than simply delivering information, learning environments should stimulate the learner to actively construct knowledge and provide feedback on the constructions. In the context of an ITS, knowledge construction is viewed as a sense-making activity in which the learner attempts to build a coherent representation of the tutorial contents and integrate it with existing knowledge (Graesser et al., 1999; Wittrock, 1974, 1990). Research supporting the epistemological stance of constructivist approaches to learning (Biggs, 1996; Bransford, Goldman, & Vye, 1991; Brown, 1988; Chi, deLeeuw, Chiu, & LaVancher, 1994; Derry, 1996; Mayer, 1997; Moshman, 1982; Palincsar & Brown, 1984; Papert, 1980; Piaget, 1952; Pressley & Wharton-McDonald, 1997; Rogoff, 1990; VanLehn, Jones, & Chi, 1992; Vygotsky, 1978) has a long history in psychology and education. …

Web-Based Intelligent Multimedia Tutoring for High Stakes Achievement Tests

Abstract: We describe Wayang Outpost, a web-based ITS for the Math section of the Scholastic Aptitude Test (SAT). It has several distinctive features: help with multimedia animations and sound, problems embedded in narrative and fantasy contexts, alternative teaching strategies for students of different mental rotation abilities and memory retrieval speeds. Our work on adding intelligence for adaptivity is described. Evaluations prove that students learn with the tutor, but learning depends on the interaction of teaching strategies and cognitive abilities. A new adaptive tutor is being built based on evaluation results; surveys results and students’ log files analyses.

Evaluating the Effectiveness of a Tutorial Dialogue System for Self-Explanation

Abstract: Previous research has shown that self-explanation can be supported effectively in an intelligent tutoring system by simple means such as menus. We now focus on the hypothesis that natural language dialogue is an even more effective way to support self-explanation. We have developed the Geometry Explanation Tutor, which helps students to state explanations of their problem-solving steps in their own words. In a classroom study involving 71 advanced students, we found that students who explained problem-solving steps in a dialogue with the tutor did not learn better overall than students who explained by means of a menu, but did learn better to state explanations. Second, examining a subset of 700 student explanations, students who received higher-quality feedback from the system made greater progress in their dialogues and learned more, providing some measure of confidence that progress is a useful intermediate variable to guide further system development. Finally, students who tended to reference specific problem elements in their explanations, rather than state a general problem-solving principle, had lower learning gains than other students. Such explanations may be indicative of an earlier developmental level.

The DARWARS Tactical Language Training System

Abstract: The DARWARS Tactical Language Training System (TLTS) helps learners acquire basic communicative skills in foreign languages and cultures. Learners practice their communication skills in a simulated village, where they must develop rapport with the local people, who in term will help them accomplish missions such as post-war reconstruction. Each learner is accompanied by a virtual aide who can provide assistance and guidance if needed, tailored to each learner's individual skills. The aide can also act as a virtual tutor as part of an intelligent tutoring system, giving the learner feedback on their performance. Learners communicate via a multimodal interface, which permits them to speak and choose gestures on behalf of their character in the simulation. The system employs video game technologies and design techniques, in order to motivate and engage learners. A version for Levantine Arabic has been developed, and versions for other languages are in the process of being developed. A first version is scheduled to be transitioned into use by US Special Forces in late 2004. The TLTS project has developed and integrated several advanced technologies, including speech recognition tailored for learner speech, motivational tutorial dialog, learner modeling, and multi-agent social simulations. The virtual aide in the game is implemented as a pedagogical agent, able to interact with learners at a motivational and social level as well as a cognitive level. Character behavior in the game is controlled by the Psychsim cognitive modeling system, that models the motivations of social agents. Multi-user authoring tools enable linguists, instructional designers, and simulation developers to collaborate in the specification and construction of lessons and simulations in multiple languages. The TLTS is part of the DARWARS Training Superiority program developing new technologies for military training.

A Web-Based Intelligent Tutoring System for Computer Programming

Abstract: Web Intelligence is a direction for scientific research that explores practical applications of Artificial Intelligence to the next generation of Web-empowered systems. In this paper, we present a Web-based intelligent tutoring system for computer programming. The decision making process conducted in our intelligent system is guided by Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences.

Supporting Distributed Problem-Based Learning: The Use of Feedback Mechanisms in Outline Learning

Abstract: In this chapter we discuss possibilities and shortcomings of Internet usage for distributed problem-based learning. Several problems with the use of computer-mediated communication for collaborative learning online are identified. In our approaches we use data that is automatically tracked during computer-mediated communication and extract relevant information for feedback purposes. Partly automatically, partly manually prepared the feedback is a rich resource for learners to manage their own collaboration 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB9544 IDEA GROUP PUBLISHING This chapter appears in the book, Online Collaborative Learning: Theory and Practice, edited by Tim S. Roberts. Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Supporting Distributed Problem-Based Learning 87 Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. process as well as subsequent problem-solving processes. In a synchronous and an asynchronous distributed problem-based learning environment, we show how we applied this methodology to support learners’ motivation and problem solving. Analyses show encouraging benefits of our approach in overcoming common problems with computer-mediated communication. INTRODUCTION When James Cook started his last journey to find the Northwest Passage through North America, his wife was angry with him because he had promised her that he would never go on a long voyage again. During the whole trip he was supposed to be in an ill-tempered mood totally different from his normal style, badly collaborating with his crew and behaving harshly and unfairly to the native people he met. No wonder that he was killed on the islands of Hawaii in 1779. What he did not know was that his wife had already forgiven him, so some might say that if he had seen her smile, this would have changed the whole course of history. Is this true? Does such a form of emotional feedback have an impact on people’s performance in a group situation? Did Cook die due to a lack of feedback? Nowadays, most of the white spots on Earth have been explored and Internet technologies have made the world smaller. People communicate, collaborate and even learn together using the Internet. There is much ongoing research about how to use computer-mediated communication (CMC) for task oriented groups. Actually, little research is dedicated to the use of technology for feedback purposes during online collaboration, especially in distributed problembased learning. There are also many studies exploring feedback mechanisms in individual computer-based learning, especially for knowledge acquisition purposes. Research concerning intelligent tutoring systems (ITS) has provided evidence for a meaningful use of individual feedback based on learner-program interaction (Wenger, 1987). Unfortunately, this tradition has yet not reached contemporary learning approaches using computer-supported collaborative learning (CSCL). Besides the use of computer generated feedback on a task level, there is hardly any exploration of its effects on a group’s interaction level. Although interacting and communicating is crucial to problem-based learning (PBL), most approaches transferring PBL into a network-based learning environment do not pursue approaches to give learner support on this level. Some earlier research, for example Mandl, Fischer, Frey and Jeuck (1985), discusses some computer-based feedback mechanisms and functions, but does not specifically refer to a group context. So far, these investigations have not been carried further. Possible reasons might be a lack of underlying theoretical assumptions and derivations of specific hypotheses. 15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/supporting-distributed-problembased-learning/27718

MASCARET: A Pedagogical Multi-Agent System for Virtual Environments for Training

Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Mascaret: Pedagogical multi-agents system for virtual environment for training. Cédric Buche, Ronan Querrec, Pierre De Loor, Pierre Chevaillier

Politeness in tutoring dialogs: Run the factory, that's what I'd do

Abstract: Intelligent Tutoring Systems usually take into account only the cog-nitive aspects of the student: they may suggest the right actions to perform, cor-rect mistakes, and provide explanations. However, besides cognition, educa-tional researchers increasingly recognize the importance of factors such as self-confidence and interest that contribute to learner intrinsic motivation. We be-lieve that the student affective goals can be taken into account by implementing a model of politeness into the tutoring system. This paper aims at providing an overall account of politeness in tutoring interactions (in particular, natural lan-guage dialogs), and describes the way in which politeness has been imple-mented in an intelligent tutoring system based on an animated pedagogical agent. The work is part of a larger project building a socially intelligent peda-gogical agent able to monitor learner performance and provide socially sensi-tive coaching and feedback at appropriate times. The project builds on the ex-perience gained in realizing several other pedagogical agents.

Usability Evaluation Methodology for Web-based Educational Systems

Abstract: Over the past decade the field of education has experienced the introduction of the Internet, which has radically altered the way humans teach and learn. In conjunction with other advances within ICT, here including multimedia, networking and software engineering, this has enabled the appearance of new generations of computer-based educational systems. Inclusion of intelligence and adaptivity has additionally led to the development of Web-oriented educational systems like Web-based intelligent authoring shells, which provide the means for automated generation of emulators of human teachers in the process of learning and teaching. Our research is focused on user interfaces for Web-based intelligent authoring shells, which enhance with adaptive and "Web-based" features the operating capabilities of legacy "on-site" systems. In the paper we describe the methodology for such Web-based shell's usability evaluation, along with results thus achieved. The methodology itself is composed of three usability evaluation methods including a scenario-based end-user testing, guidelines set for system review and a user-interaction satisfaction questionnaire.

Diagnostic and remedial learning strategy based on conceptual graphs

Abstract: Numerous scholars have applied conceptual graphs for explanatory purposes. This studydevised the ‘Remedial-Instruction Decisive path (RID path)’ algorithm for diagnosing in-dividual student learning situation. This study focuses on conceptual graphs. According tothe concepts learned by students and the weight values of relations among these concepts,this study established the remedial-instruction paths to identify their real missing concepts.This study applies diagnostic and remedial learning strategies to two courses – ‘Introductionand Implementation of RS-232’ and ‘Electronic Circuits Laboratory’. By analysing thescores of the midterm and final exams, evaluations of remedial learning yield positive ex-perimental results. Participants who adopt the diagnostic and remedial learning strategy havebetter academic performance. Key words conceptual graph, Internet, IT-use, remedial instruction, tutorial Introduction In online distance learning environments, the Internetis the major medium for communication between in-structors and learners. Most of this communicationtakes the form of online chatting, and in some casesvideo conferences. The methods most frequently usedby instructors to understand the situation of learnersare setting homework or examinations. However,these methods do not provide instructors with im-mediate feedback, and the feedback obtained in-evitably lags behind teaching progress. The lack offace-to-face interaction between instructors and lear-ners in on-line distance learning makes it difficult forinstructors to know the situation of learners, and thisweakness must be improved. By recording and ana-lysing learner behaviour, details of individual learningsituation can be obtained, including progress,strengths and weaknesses. Furthermore, a system canbe designed to automatically generate a series of ai-ded-learning contents for every student.The Intelligent Tutoring System (ITS) is a learningsystem that provides learning suggestions or plansaccording learner behaviour and performance. Koe-dinger and Anderson (1997) developed an ITS foralgebra problem solving, named the system PAT. ThisITS monitored learner performance in the backgroundof learning activities using two modelling techniques:model tracing and knowledge tracing. Virvou et al.(2000) proposed an intelligent tutoring system forGreek students studying the passive voice in English.Their study focused on identifying student behaviourby identifying the characteristics of student errors,then classified errors into several categories and sup-ported the helpful suggestions.The ‘Conceptual graph’ (Sowa 1976; Novak &Goin 1984) is another traditional assisted teachingmethod. A conceptual graph is composed of proposi-tions defined by two concept nodes and one connect-ing relation link. The concept nodes are arranged in ahierarchical structure. The ‘Conceptual graph’ methoddisplays knowledge structures. Teachers can use theconceptual graph to determine student comprehension

Heuristic-based entity-relationship modelling through natural language processing

Abstract: Here we propose new heuristics that assist the semi-automated generation of Entity-Relationship (ER) diagrams for database modelling from a natural language description and describe the implementation of such a system called ER-Converter. Though this is a semi-automatic transformation process, ER-Converter aims to require minimal human intervention during the process. ER-Converter has been evaluated in blind trials against a set of database problems. ER-Converter has an average of 95% recall and 82% precision. The evaluation results are discussed and demonstrate that ER-Converter could be used, for example, within the domain model of a multimedia intelligent tutoring system, designed to assist in the learning and teaching of databases.

AgentX: Using Reinforcement Learning to Improve the Effectiveness of Intelligent Tutoring Systems

Abstract: Reinforcement Learning (RL) can be used to train an agent to comply with the needs of a student using an intelligent tutoring system. In this paper, we introduce a method of increasing efficiency by way of customization of the hints provided by a tutoring system, by applying techniques from RL to gain knowledge about the usefulness of hints leading to the exclusion or introduction of other helpful hints.

Predicting learning characteristics in a multiple intelligence based tutoring system

Abstract: Research on learning has shown that students learn differently and that they process knowledge in various ways. EDUCE is an Intelligent Tutoring System for which a set of learning resources has been developed using the principles of Multiple Intelligences. It can dynamically identify user learning characteristics and adaptively provide a customised learning material tailored to the learner. This paper introduces the predictive engine used within EDUCE. It describes the input representation model and the learning mechanism employed. The input representation model consists of input features that describe how different resources were used and inferred from fine-grained information collected during student computer interactions. The predictive engine employs the Naive Bayes classifier and operates online using no prior information. Using data from a previous experimental study, a comparison was made between the performance of the predictive engine and the actual behaviour of a group of students using the learning material without any guidance from EDUCE. Results indicate correlation between student's behaviour and the predictions made by EDUCE. These results suggest that the concept of learning characteristics can be modelled using a learning scheme with appropriately chosen attributes.

Combining Competing Language Understanding Approaches in an Intelligent Tutoring System

Abstract: When implementing a tutoring system that attempts a deep understanding of students’ natural language explanations, there are three basic approaches to choose between; symbolic, in which sentence strings are parsed using a lexicon and grammar; statistical, in which a corpus is used to train a text classifier; and hybrid, in which rich, symbolically produced features supplement statistical training. Because each type of approach requires different amounts of domain knowledge preparation and provides different quality output for the same input, we describe a method for heuristically combining multiple natural language understanding approaches in an attempt to use each to its best advantage. We explore two basic models for combining approaches in the context of a tutoring system; one where heuristics select the first satisficing representation and another in which heuristics select the highest ranked representation.

Predicting Learning Characteristics in a Multiple Intelligence Based Tutoring System

Abstract: Research on learning has shown that students learn differently and that they process knowledge in various ways. EDUCE is an Intelligent Tutoring System for which a set of learning resources has been developed using the principles of Multiple Intelligences. It can dynamically identify user learning characteristics and adaptively provide a customised learning material tailored to the learner. This paper introduces the predictive engine used within EDUCE. It describes the input representation model and the learning mechanism employed. The input representation model consists of input features that describe how different resources were used and inferred from fine-grained information collected during student computer interactions. The predictive engine employs the Naive Bayes classifier and operates online using no prior information. Using data from a previous experimental study, a comparison was made between the performance of the predictive engine and the actual behaviour of a group of students using the learning material without any guidance from EDUCE. Results indicate correlation between student’s behaviour and the predictions made by EDUCE. These results suggest that the concept of learning characteristics can be modelled using a learning scheme with appropriately chosen attributes.

Game-Driven Intelligent Tutoring Systems

Abstract: With the increase of computer capabilities, many learning systems have become complex simulators with advanced interfaces close to game quality. However, many games features have not been added to them. This paper focus on this area, listing what games can provide to simulation-driven tutoring systems. We also describe JV2M as an example of a game-driven intelligent tutoring system to teach the compilation process of Java programs.

Integrated Virtual Learning System for Programmable Logic Controller

Abstract: A integrated virtual learning system is being researched and developed to teach students about programmable logic controllers (PLCs). This system, called the Virtual PLC, incorporates intelligent tutoring system, simulation, and animation technologies. This article describes the development and evaluation of modules on PLC timer and counter instructions. These modules were first developed using an intelligent tutoring system (ITS) authoring tool and animation tools. After the concept was proved positively, a Web-based ITS was developed to incorporate both modules. The authoring tool-based ITS timer modules were evaluated with 90 undergraduate manufacturing engineering students in 2002. The Web-based ITS timer and counter modules were evaluated by 38 undergraduate students in 2003. In both cases, students made statistically significant learning gains as a result of taking the modules, and rated the modules positively in terms of ease of use and understanding, clear objectives, amount of interaction, ability to motivate, relevance, and pace.

The Massive User Modelling System (MUMS)

Abstract: Effective distributed user modelling in intelligent tutoring systems requires the integration of both pedagogical and domain applications. This integration is difficult, and often requires rebuilding applications for the specific e-learning environment that has been deployed. This paper puts forth both an architecture and an implementation prototype for achieving this integration. It focuses on providing platform and language neutral access to services, without commitment to any particular ontology.

Design pattern ITS: student model implementation

Abstract: The paper presents implementation of the student model in the design pattern intelligent tutoring system. The student model is created by using a model template which is filled in with new attribute values. The same principle can be applied to other ITS as well.

Evaluating Adaptive Problem Selection

Abstract: This paper presents an evaluation study that compares two different problem selection strategies for an Intelligent Tutoring System (ITS). The first strategy uses static problem complexities specified by the teacher to select problems that are appropriate for a student based on his/her current level of ability. The other strategy is more adaptive: individual problem difficulties are calculated for each student based on the student’s specific knowledge, and the appropriate problem is then selected based on these dynamic difficulty measures. The study was performed in the context of the SQL-Tutor system. The results show that adaptive problem selection based on dynamically generated problem difficulties can have a positive effect on student learning performance.

Empirical Derivation of a Sequence of User Stereotypes for Language Learning

Abstract: The work described here pertains to ICICLE, an intelligent tutoring system for which we have designed a user model to supply data for intelligent natural language parse disambiguation. This model attempts to capture the user's mastery of various grammatical units and thus can be used to predict the grammar rules he or she is most likely using when producing language. Because ICICLE's user modeling component must infer the user's language mastery on the basis of limited writing samples, it makes use of an inferencing mechanism that will require knowledge of stereotypic acquisition sequences in the user population. We discuss in this paper the methodology of how we have applied an empirical investigation into user performance in order to derive the sequence of stereotypes that forms the basis of our modeling component's reasoning capabilities.