Showing papers on "Intelligent tutoring system published in 2000"
Journal Article•
TL;DR: Data about students' use of the help facilities of the PACT Geometry Tutor, a cognitive tutor for high school geometry, suggest that students do not always have metacognitive skills to support students in learning domain-specific skills and knowledge.
Abstract: Intelligent tutoring systems often emphasize learner control: They let the students decide when and how to use the system's intelligent and unintelligent help facilities. This means that students must judge when help is needed and which form of help is appropriate. Data about students' use of the help facilities of the PACT Geometry Tutor, a cognitive tutor for high school geometry, suggest that students do not always have these metacognitive skills. Students rarely used the tutor's on-line Glossary of geometry knowledge. They tended to wait long before asking for hints, and tended to focus only on the most specific hints, ignoring the higher hint levels. This suggests that intelligent tutoring systems should support students in learning these skills, just as they support students in learning domain-specific skills and knowledge. Within the framework of cognitive tutors, this requires creating a cognitive model of the metacognitive help-seeking strategies, in the form of production rules. The tutor then can use the model to monitor students' metacognitive strategies and provide feedback.
317 citations
Journal Article•
TL;DR: Andes as discussed by the authors is an Intelligent Tutoring System for introductory college physics that encourages the student to construct new knowledge by providing hints that require them to derive most of the solution on their own, and facilitates transfer from the system by making the interface as much like a piece of paper as possible.
Abstract: Andes is an Intelligent Tutoring System for introductory college physics. The fundamental principles underlying the design of Andes are: (1) encourage the student to construct new knowledge by providing hints that require them to derive most of the solution on their own, (2) facilitate transfer from the system by making the interface as much like a piece of paper as possible, (3) give immediate feedback after each action to maximize the opportunities for learning and minimize the amount of time spent going down wrong paths, and (4) give the student flexibility in the order in which actions are performed, and allow them to skip steps when appropriate. This paper gives an overview of Andes, focusing on the overall architecture and the student's experience using the system.
219 citations
19 Jun 2000
TL;DR: This paper gives an overview of Andes, focusing on the overall architecture and the student's experience using the system.
Abstract: Andes is an Intelligent Tutoring System for introductory college physics. The fundamental principles underlying the design of Andes are: (1) encourage the student to construct new knowledge by providing hints that require them to derive most of the solution on their own, (2) facilitate transfer from the system by making the interface as much like a piece of paper as possible, (3) give immediate feedback after each action to maximize the opportunities for learning and minimize the amount of time spent going down wrong paths, and (4) give the student flexibility in the order in which actions are performed, and allow them to skip steps when appropriate. This paper gives an overview of Andes, focusing on the overall architecture and the student's experience using the system.
213 citations
Journal Article•
TL;DR: This paper attempts, from one side, to trace the history adaptive educational hypermedia in connection with intelligent tutoring systems research and, from another side, draft its future in connected with Web-based education.
Abstract: Adaptive hypermedia is a new area of research at the crossroads of hypermedia and adaptive systems and. Education is the largest application area of adaptive hypermedia systems. The goals of this paper are to provide a brief introduction into adaptive hypermedia and supply the reader with an organized reading on adaptive educational hypermedia. Unlike some other papers that are centered around the current state of the field, this paper attempts, from one side, to trace the history adaptive educational hypermedia in connection with intelligent tutoring systems research and, from another side, draft its future in connection with Web-based education.
163 citations
19 Jun 2000
TL;DR: Adaptive hypermedia is a new area of research at the crossroads of hypermedia and adaptive systems and education is the largest application area of adaptive hypermedia systems and as discussed by the authors.
Abstract: Adaptive hypermedia is a new area of research at the crossroads of hypermedia and adaptive systems and. Education is the largest application area of adaptive hypermedia systems. The goals of this paper are to provide a brief introduction into adaptive hypermedia and supply the reader with an organized reading on adaptive educational hypermedia. Unlike some other papers that are centered around the current state of the field, this paper attempts, from one side, to trace the history adaptive educational hypermedia in connection with intelligent tutoring systems research and, from another side, draft its future in connection with Web-based education.
134 citations
19 Jun 2000
TL;DR: A learning agent that models student behavior at a high level of granularity for a mathematics tutor, which was very accurate at predicting the time students required to generate a response, and was somewhat accurate at predicted the likelihood the student's response was correct.
Abstract: We have constructed a learning agent that models student behavior at a high level of granularity for a mathematics tutor Rather than focusing on whether the student knows a particular piece of knowledge, the learning agent determines how likely the student is to answer a problem correctly and how long he will take to generate this response To construct this model, we used traces from previous users of the tutor to train the machine learning agent This agent used information about the student, the current topic, the problem, and the student's efforts to solve this problem to make its predictions This model was very accurate at predicting the time students required to generate a response, and was somewhat accurate at predicting the likelihood the student's response was correct We present two methods for integrating such an agent into an intelligent tutor
130 citations
19 Jun 2000
TL;DR: It is literally impossible to speak of a learner model as a single distinct entity in distributed support environments that will be characteristic of tomorrow's ITSs, and "learner model" will be considered in its verb sense to be an action that is computed as needed during learning.
Abstract: It is common to think of a "learner model" as a global description of a student's understanding of domain content. We propose a notion of learner model where the emphasis is on the modelling process rather than the global description. In this re-formulation there is no one single learner model in the traditional sense, but a virtual infinity of potential models, computed "just in time" about one or more individuals by a particular computational agent to the breadth and depth needed for a specific purpose. Learner models are thus fragmented, relativized, local, and often shallow. Moreover, social aspects of the learner are perhaps as important as content knowledge. We explore the implications of fragmented learner models, drawing examples from two collaborative learning systems. The main argument is that in distributed support environments that will be characteristic of tomorrow's ITSs, it will be literally impossible to speak of a learner model as a single distinct entity. Rather "learner model" will be considered in its verb sense to be an action that is computed as needed during learning.
86 citations
TL;DR: Professor Freedman's research focuses on reactive planning and theories of discourse and dialog processing with the goal of building better intelligent tutoring systems.
81 citations
13 Nov 2000
TL;DR: Progress is described toward a prototype implementation of a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that student in a tutorial dialogue, enabling the student to generate appropriate corrections to the text.
Abstract: This paper describes progress toward a prototype implementation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) signers of American Sign Language (ASL). We envision a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that student in a tutorial dialogue, enabling the student to generate appropriate corrections to the text. A strong focus of this work is to develop a system which adapts this process to the knowledge level and learning strengths of the user and which has the flexibility to engage in multi-modal, multi-lingual tutorial instruction utilizing both English and the native language of the user.
75 citations
01 Jan 2000
TL;DR: One reason that ITS is such a large and varied field is that “intelligent tutoring system” is a broadterm, encompassing any computer program that contains some intelligence and can be used inlearning.
Abstract: One reason that ITS is such a large and varied field is that “intelligent tutoring system” is a broadterm, encompassing any computer program that contains some intelligence and can be used inlearning. ITS is an outgrowth of the earlier computer-aided instruction or CAI model, whichusually refers to a frame-based system with hard-coded links, i.e. hypertext with an instructionalpurpose.The traditional ITS model contains four components: the domain model, the student model, theteaching model, and a learning environment or user interface. ITS projects can vary tremendouslyaccording to the relative level of intelligence of the components. For example, a project focusingon intelligence in the domain model may generate solutions to complex and novel problems sothat students can always have new problems to practice on, but it might only have simplemethods for teaching those problems, while a system that concentrates on multiple or novel waysto teach a particular topic might find a less sophisticated representation of that content sufficient.When multiple components contain intelligence, homogeneous or heterogeneous representationscan be used.ITS can also be classified by their underlying algorithm. One well-known category is the model-tracing tutor, which tracks students’ progress and keeps them within a specified tolerance of anacceptable solution path.A theme underlying much of ITS research is domain independence, i.e. the degree to whichknowledge encoded in the teaching model can be reused in different domains. Although to theexternal observer, domain independence seems like an essential characteristic of intelligence,many experts believe that some of the essential pedagogical knowledge in every domain isfundamentally domain-dependent. For example, there are analogies used in teaching physics, andeven in teaching specific topics in physics, that have no equivalents in other domains.Task independence, or the degree to which the knowledge in the system can be used to support avariety of tasks on the part of the student, has not yet been addressed by most systems.
68 citations
01 Jan 2000
TL;DR: The design and development of a web-based Computerized Adaptive Testing system (CAT) that is still under development and will be one of the main components of the TREE project, which consists in the development of several web- based tools for the classification and identification of different European vegetable species.
Abstract: In this paper, we describe the design and development of a web-based Computerized Adaptive Testing system (CAT) that is still under development and will be one of the main components of the TREE project. The TREE project consists in the development of a several web-based tools for the classification and identification of different European vegetable species (an expert system, interfaces for creating and updating databases and an intelligent tutoring system). The test generation system will be used by the ITS diagnostic module, and has a complete set of tools that not only assists teachers in test development and design, but also supports student evaluations. Adaptive capabilities are provided by an IRT model. While the student is taking the test, the system creates (and updates) his/her temporary student model. In this way, the system can be used in two different ways: as an independent evaluation tool over the WWW (SIETTE system, already finished), or as a component of the diagnostic module in any ITS with a curriculum structured knowledge base as the TREE ITS.
Journal Article•
TL;DR: In this article, an intelligent tutoring system designed to help students solve physics problems of a qualitative nature is described, where the tutor uses a unique cognitive based approach to teaching physics, which presents innovations in three areas.
Abstract: This paper describes an intelligent tutoring system designed to help students solve physics problems of a qualitative nature. The tutor uses a unique cognitive based approach to teaching physics, which presents innovations in three areas. 1) The teaching strategy, which focuses on teaching links among the concepts of the domain that are essential for conceptual understanding yet are seldom learned by the students. 2) The manner in which the knowledge is taught, which is based on a combination of effective human tutoring techniques, successful pedagogical methods, and less cognitively demanding approaches. 3) The way in which misconceptions are handled. The tutor was implemented using the model-tracing paradigm and uses probabilistic assessment to guide the remediation. Some preliminary results of the evaluation of the system are also presented.
29 Apr 2000
TL;DR: This paper describes an application of APE (the Atlas Planning Engine), an integrated planning and execution system at the heart of the Atlas dialogue management system, and describes Atlas-Andes, an intelligent tutoring system built using APE with the Andes physics tutor as the host.
Abstract: This paper describes an application of APE (the Atlas Planning Engine), an integrated planning and execution system at the heart of the Atlas dialogue management system. APE controls a mixed-initiative dialogue between a human user and a host system, where turns in the 'conversation' may include graphical actions and/or written text. APE has full unification and can handle arbitrarily nested discourse constructs, making it more powerful than dialogue managers based on finitestate machines. We illustrate this work by describing Atlas-Andes, an intelligent tutoring system built using APE with the Andes physics tutor as the host.
04 Dec 2000
TL;DR: A new Intelligent Tutoring System that teaches the mechanical rules of English capitalisation and punctuation as a set of constraints specifying the correct patterns of punctuation and capitalisation, and feedback is given on violated constraints.
Abstract: We describe a new Intelligent Tutoring System (ITS) that teaches the mechanical rules of English capitalisation and punctuation. Students must interactively capitalise and punctuate short pieces of unpunctuated, lower case text (the completion exercise). The system represents the domain as a set of constraints specifying the correct patterns of punctuation and capitalisation, and feedback is given on violated constraints. The ITS was evaluated during several sessions in a classroom of 10-11 year old school children. The results show that the children effectively mastered the 25 rules represented in the system.
19 Jun 2000
TL;DR: It is argued that traditional sequencing technology developed in the field of intelligent tutoring systems could find an immediate place in large-scale Web-based education as a core technology for concept-based course maintenance.
Abstract: We argue that traditional sequencing technology developed in the field of intelligent tutoring systems could find an immediate place in large-scale Web-based education as a core technology for concept-based course maintenance. This paper describes a concept-based course maintenance system that we have developed for Carnegie Technology Education. The system can check the consistency and quality of a course at any moment of its life and also assist course developers in some routine operations. The core of this system is a refined approach to indexing the course material and a set of "scripts" for performing different operations.
Journal Article•
TL;DR: An intelligent multimedia tutoring system for the passive voice of the English grammar and the main focus of the tutor is on the student's error diagnosis process, which is performed by the student modelling component.
Abstract: This paper describes an intelligent multimedia tutoring system for the passive voice of the English grammar. The system may be used to present theoretical issues about the passive voice and to provide exercises that the student may solve. The main focus of the tutor is on the student's error diagnosis process, which is performed by the student modelling component. When the student types the solution to an exercise, the system examines the correctness of the answer. If the student's answer has been erroneous it attempts to diagnose the underlying misconception of the mistake. In order to provide individualised help, the system holds a profile for every student, the long term student model. The student’s progress and his/her usual mistakes are recorded to this long term student model. This kind of information is used for the individualised error diagnosis of the student in subsequent sessions. In addition, the information stored about the student can also be used for the resolution of an arising ambiguity, as to what the underlying cause of a student error has been.
01 Jan 2000
TL;DR: The motivation and design of the new software, which enables the tutor to recognize and classify a greater number of unexpected responses, and the results in using it are reported on.
Abstract: We have replaced the input understanding component of CIRCSIM-Tutor, an intelligent tutoring system that engages the student in Socratic dialogue. Students type free-text answers to the computer’s questions. Even though the questions can be answered very simply, the variety of student responses prompted us to make the understanding component more robust. The new software also enables the tutor to recognize and classify a greater number of unexpected responses. In this paper we report on the motivation and design of the new software and our results in using it.
TL;DR: In this paper, learning theories of the completion strategy are investigated and a template technique is employed to realize the strategy.
Abstract: The purpose of this research is to develop a programming learning system for beginners using the completion strategy The completion strategy uses well-designed programs to let students engage in completing, modifying, and extending their programs The completion strategy is a paradigm of learning by examples with learning enforcement In this paper, learning theories of the completion strategy are investigated A template technique is employed to realize the strategy An educational experiment was made to show the learning impact of the proposed system The experimental result shows that the completion strategy is benefit to the programming learning for beginners
Journal Article•
TL;DR: It is argued that Bayesian nets can offer much more to an ITS, and an example of how they can be used for selecting problems is given.
Abstract: Bayesian networks have been used in Intelligent Tutoring Systems (ITSs) for both short-term diagnosis of students' answers and for longer-term assessment of a student's knowledge. Bayesian networks have the advantage of a firm theoretical foundation, in contrast to many existing, ad-hoc approaches. In this paper we argue that Bayesian nets can offer much more to an ITS, and we give an example of how they can be used for selecting problems. Similar approaches may be taken to automating many kinds of decision in ITSs.
19 Jun 2000
Abstract: Intelligent tutoring systems often emphasize learner control: They let the students decide when and how to use the system's intelligent and unintelligent help facilities. This means that students must judge when help is needed and which form of help is appropriate. Data about students' use of the help facilities of the PACT Geometry Tutor, a cognitive tutor for high school geometry, suggest that students do not always have these metacognitive skills. Students rarely used the tutor's on-line Glossary of geometry knowledge. They tended to wait long before asking for hints, and tended to focus only on the most specific hints, ignoring the higher hint levels. This suggests that intelligent tutoring systems should support students in learning these skills, just as they support students in learning domain-specific skills and knowledge. Within the framework of cognitive tutors, this requires creating a cognitive model of the metacognitive help-seeking strategies, in the form of production rules. The tutor then can use the model to monitor students' metacognitive strategies and provide feedback.
19 Jun 2000
TL;DR: The Conceptual Helper was implemented as a model-tracing tutor which intervenes when students make errors and after completion of each problem, at which time the tutor scaffolds the students on post-problem reflection.
Abstract: This paper describes an intelligent tutoring system designed to help students solve physics problems of a qualitative nature. The tutor uses a unique cognitive based approach to teaching physics, which presents innovations in three areas. 1) The teaching strategy, which focuses on teaching links among the concepts of the domain that are essential for conceptual understanding yet are seldom learned by the students. 2) The manner in which the knowledge is taught, which is based on a combination of effective human tutoring techniques, successful pedagogical methods, and less cognitively demanding approaches. 3) The way in which misconceptions are handled. The tutor was implemented using the model-tracing paradigm and uses probabilistic assessment to guide the remediation. Some preliminary results of the evaluation of the system are also presented.
19 Jun 2000
TL;DR: A LCS for Binary Boolean Algebra has been developed to explore the hypothesis that a learning companion with less expertise than the human student would be beneficial for the student in her learning and suggested that learning companions might be confusing for students if they try to resemble human behaviour.
Abstract: This paper describes work carried out to explore the role of a learning companion as a student of the human student. A LCS for Binary Boolean Algebra has been developed to explore the hypothesis that a learning companion with less expertise than the human student would be beneficial for the student in her learning. The system implemented two companions with different expertise and two types of motivational conditions. Results from a empirical evaluation suggested that subjects interacting with a less capable companion (weak) have a trend of more improvement than subjects interacting with a more capable companion (strong). Finally, the experiment also suggested that learning companions might be confusing for students if they try to resemble human behaviour, i.e. if they do not perform as they are told.
19 Jun 2000
TL;DR: Bayesian networks have been used in Intelligent Tutoring Systems (ITSs) for both short-term diagnosis of students' answers and for longer-term assessment of a student's knowledge.
Abstract: Bayesian networks have been used in Intelligent Tutoring Systems (ITSs) for both short-term diagnosis of students' answers and for longer-term assessment of a student's knowledge Bayesian networks have the advantage of a firm theoretical foundation, in contrast to many existing, ad-hoc approaches In this paper we argue that Bayesian nets can offer much more to an ITS, and we give an example of how they can be used for selecting problems Similar approaches may be taken to automating many kinds of decision in ITSs
01 Jan 2000
TL;DR: The evaluation of the Conceptual Helper, an intelligent tutoring system that uses a unique cognitive approach to teaching qualitative physics, is described and the results are encouraging and suggest that the proposed methodology can be effective in performing its task.
Abstract: Evaluating the Effectiveness of a Cognitive Tutor for Fundamental Physics Concepts Patricia L. Albacete (albacete@isp.pitt.edu) Intelligent Systems Program; 607 Dixie Drive Pittsburgh, PA 15235 USA Kurt A. VanLehn (Vanlehn@cs.pitt.edu) Learning, Research and Development Center; University of Pittsburgh Pittsburgh, PA 15260 USA Abstract In this article we describe and analyze the evaluation of the Conceptual Helper, an intelligent tutoring system that uses a unique cognitive approach to teaching qualitative physics. The results of the evaluation are encouraging and suggest that the proposed methodology can be effective in performing its task. Introduction Several studies (e.g. Hake, 1998; Halloun & Hestenes, 1985a, 1985b) have revealed that solving physics problems of a qualitative nature, such as the one presented in figure 1, pose a great cognitive challenge for most students taking elementary mechanics classes. They uncover naive conceptions that are seldom removed or modified while completing their courses. Several attempts have been made to improve this situation though none has met with great success (Hake, 1998). Given that mechanics is a required course for most science majors, there is a clear need to improve its instruction. Toward this end we developed an intelligent tutoring system called the Conceptual Helper that follows a cognitive teaching strategy which is deployed emulating effective human tutoring techniques as well as successful pedagogical techniques and less cognitive demanding methods (Albacete, 1999; Albacete & VanLehn, 2000). In this article we describe the evaluation of the system and discuss its implications. Two steel balls, one of which weights twice as much as the other, roll off of a horizontal table with the same speeds. In this situation: a) both balls impact the floor at approximately the same horizontal distance from the base of the table. b)the heavier ball impacts the floor closer to the base of the table than does the lighter. c) the lighter ball impacts the floor closer to the base of the table than does the heavier. Figure 1. Example of a qualitative problem Brief description of the Conceptual Helper The Conceptual Helper is an intelligent tutoring system (ITS) designed to coach students through physics homework problem solving of a qualitative nature, i.e., those problems that do not require the use algebraic manipulation to be solved but so require the application of conceptual knowledge. The tutor is basically a model-tracing ITS enhanced by the use of probabilistic assessment to guide the remediation. As a model-tracing ITS it contains a cognitive model that is capable of correctly solving any problem assigned to the student. Model tracing consists of matching every problem-solving action taken by the student with the steps of the expert’s solution model of the problem being solved. This matching is used as the basis for providing immediate feedback to students as they progress through the problem. The system also has a student model which is represented by a Bayesian network. Each node in the network represents a piece of conceptual knowledge that the student is expected to learn or a misconception that the tutor can help remedy. Each node has a number attached to it that indicates the probability that the student will apply the piece of knowledge when it is applicable. As the student solves a problem, the probabilities are updated according to the actions taken by the student. The challenge for the tutor is to decide when to intervene and what to say when it does so. This task is particularly challenging in this domain because tutoring of qualitative knowledge usually takes the form of verbal discussions, which given the state of the art of natural language processing is not an option for the computer tutor. To take care of the issue of when to intervene, we emulated human tutors in two ways: first, by giving immediate feedback (red for incorrect; green for correct) on each student entry (Merrill et al., 1992) and second, by helping the student with post-problem reflection (Katz & Lesgold, 1994; Katz et al., 1996). However, most of our work went into the second issue—deciding what to say when intervening. Novel approaches were developed in three areas: 1) the teaching strategy, 2) the manner in which the knowledge is deployed, and 3) the way in which misconceptions are handled.
19 Jun 2000
TL;DR: An animated pedagogical agent, Adele, uses the causal knowledge, represented as a Bayesian network, to dynamically generate a diagnostic process that is consistent with the best practice approach to medical diagnosis.
Abstract: This paper presents an approach to intelligent tutoring for diagnostic problem solving that uses knowledge about causal relationships between symptoms and disease states to conduct a pedagogically useful dialogue with the student. An animated pedagogical agent, Adele, uses the causal knowledge, represented as a Bayesian network, to dynamically generate a diagnostic process that is consistent with the best practice approach to medical diagnosis. Using a combination of hints and other interactions based on multiple choice questions, Adele guides the student through a reasoning process that exposes her to the underlying knowledge, i.e., the patho-physiological processes, while being sensitive to the problem solving state and the student's current level of knowledge. Although the main focus of this paper is on tutoring medical diagnosis, the methods described here are applicable to tutoring diagnostic skills in any domain with uncertain knowledge.
19 Jun 2000
TL;DR: Additional results derived from a more comprehensive analysis of the experimental data are presented, providing a stronger indication of the system's effectiveness and suggesting general guidelines for effective support of self-explanation during example studying.
Abstract: We present further results on the educational effectiveness of an intelligent computer tutor that helps students learn effectively from examples by coaching self-explanation - the process of explaining to oneself an example worked-out solution. An earlier analysis of the results from a formative evaluation of the system provided suggestive evidence that it could improve students' learning. In this paper, we present additional results derived from a more comprehensive analysis of the experimental data. They provide a stronger indication of the system's effectiveness and suggest general guidelines for effective support of self-explanation during example studying.
Journal Article•
TL;DR: For separating waste from a fiber-and-waste mixture in a textile machine, the mixture is tangentially thrown from a rotating roll by centrifugal force, and the waste particles are removed by suction.
Abstract: For separating waste from a fiber-and-waste mixture in a textile machine, the mixture is tangentially thrown from a rotating roll by centrifugal force. An air flow is directed onto the traveling particles of the mixture such that fibers are returned to the roll, while the waste particles are allowed to continue their travel. Subsequently, the waste particles are removed by suction.
01 Jan 2000
TL;DR: This paper presents Ms. Lbzdquist, an Intelligent Tutoring System (ITS) designed to carry a more human-like interactive dialog to help students learn how to write algebra expressions given a word problem.
Abstract: Graesser et. al. believe "there is something about interactive discourse that is responsible for [student] learning gains." In this paper we present Ms. Lbzdquist, an Intelligent Tutoring System (ITS) designed to carry a more human-like interactive dialog to help students learn how to write algebra expressions given a word problem. Ms. Lindquist is able to carry on a running conversation, complete with probing questions, positive and negative feedback, follow-up questions in embedded sub-dialogs, and requests for explanation as to why something is correct. In order to build Ms. Lindquist we have expanded the traditional model-tracing paradigm so that Ms. Lindquist not only has a model of the student, but also has a model of tutorial reasoning. Ms. Lindquist has a separate tutorial model encoding pedagogical content knowledge in the form of different tutorial strategies that was partially developed by observing an experienced human tutor. We discuss aspects of this human tutor’s method that can be modeled well by Ms. Lindquist and share examples of what the software is able to do. Through testing, we plan to learn about the relative effectiveness of the different tutorial strategies Ms. Lindquist uses. Ms. Lindquist is available at www.AlgebraTutor.org.
07 Oct 2000
TL;DR: The goal of the Crisis Action Planning Tutored On-line Resource (CAPTOR) project is to design, develop, and implement a state-of-the-art, Internet-based course of instruction that utilizes Intelligent Tutoring System (ITS) technology.
Abstract: The goal of the Crisis Action Planning Tutored On-line Resource (CAPTOR) project is to design, develop, and implement a state-of-the-art, Internet-based course of instruction that utilizes Intelligent Tutoring System (ITS) technology. ITS technology is ideally suited to teach complex, cognitive tasks, such as those required for troubleshooting, problem-solving, and for resolving critical situations. As currently taught, Crisis Action Planning is part of an Armed Forces Staff College 12-week classroom course. It is projected that fully implementing CAPTOR as a distance learning ITS will dramatically reduce instructional training time.
19 Jun 2000
TL;DR: The focus is on the German Tutor, an Intelligent Language Tutoring System (ILTS) that implements generality, interactivity, and modularity as contrasted with more traditional computational constraints and architectures.
Abstract: This paper discusses the inherent goals and trade-offs involved in the design of an efficient, robust Web tutor within the context of a working, hypermedia framework. The focus is on the German Tutor, an Intelligent Language Tutoring System (ILTS) that implements generality, interactivity, and modularity as contrasted with more traditional computational constraints and architectures. Design and pedagogical goals of Web-based delivery, such as intelligence and efficiency, will also be addressed but again with an emphasis on the special requirements of efficient, adaptive hypermedia systems.