Inquiry experiences can provide valuable opportunities for students to improve their understanding of both science content and scientific practices. However, the implementation of inquiry learning in classrooms presents a number of significant challenges. We have been exploring these challenges through a program of research on the use of scientific visualization technologies to support inquiry-based learning in the geosciences. In this paper, we describe five significant challenges to implementing inquiry-based learning and present strategies for addressing them through the design of technology and curriculum. We present a design history covering four generations of software and curriculum to show how these challenges arise in classrooms and how the design strategies respond to them. Students at all grade levels and in every domain of science should have the opportunity to use scientific inquiry and develop the ability to think and act in ways associated with inquiry...(National Scie...

CATO is an intelligent learning environment designed to help beginning law students learn basic skills of making arguments with cases. Using CATO, students practice tasks of induction and analogical argumentation. They practice testing theories against a body of cases and making written arguments about a problem, comparing and contrasting it to past cases. CATO’s model addresses arguments in which two opponents analogize a problem to favorable cases, distinguish unfavorable cases, assess the significance of similarities and differences between cases in light of normative knowledge about the domain, and use that knowledge to organize multi-case arguments. CATO communicates the model to students by presenting dynamically-generated argumentation examples and by reifying argument structure based on the model. CATO also provides a case database and tools based on the model that help make students ’ tasks more manageable. CATO was evaluated in the context of an actual legal writing course, in a study involving 30 first-year law students. We found that instruction with CATO leads to statistically significant improvement in students ’ basic argumentation skills, comparable

28> The problem The Find-Me systems are designed to allow a user to navigate through a set of possible solutions or products that #t their needs. The class of problems addressed by the Find-Me systems is best explained through an example: You want to rent a video. In particular, you'd like something like Back to the Future whichyou've seen and liked. Howdoyou go about #nding something? Do you wanttoseeBack to the FutureII?Doyou want to see another Michael J. Fox movie? Do you want to see Crocodile Dundee, another movie about a person dropped 1 into an unfamiliar setting? Time After Time, another time travel #lm? Who Framed Roger Rabbit?, another movie by the same director? The goal of the Find-Me project is to develop systems that deal with this sort of search problem. These problems relate to domains with the following features:

The development of high performance computing and communications is creating new media, such as the WorldWide Web and virtual realities. In turn, these new media enable new types of messages and experiences; for example, interpersonal interactions across network channels lead to the formation of virtual communities. The innovative kinds of pedagogy empowered by these emerging media, messages, and experiences make possible an evolution of synchronous, group, presentation-centered forms of distance education—which replicate traditional “teaching by telling ” across barriers of distance and time—into an alternative instructional paradigm: distributed learning. In particular, advances in computer-supported collaborative learning, multimedia/hypermedia, and experiential simulation offer the potential to create shared “learning-through-doing environments ” available anyplace, any time, on demand. This article speculates about how emerging technologies may reshape both face-to-face and distance education. Its purpose is to delineate a three-part conceptual framework (knowledge webs, virtual communities, and shared synthetic environments) for understanding the new types of instructional messages that enable distributed learning. Although this study cites leading edge scholarship to reinforce its claims, it is a position/discussion piece rather than

This paper describes how a computer program can support learning by retrieving and presenting relevant stories drawn from a video case base. Although this is an information retrieval problem, it is not a problem that fits comfortably within the classical IR model (Salton & McGill, 1983). In the classical model the computer system is passive: it is assumed that the user will take the initiative to formulate retrieval requests. A teaching system, however, must be able to initiate retrieval and formulate retrieval requests automatically. We describe a system, called SPIEL, that performs this type of retrieval, and discuss theoretical challenges addressed in implementing such a system. These challenges include the development of a representation language for indexing the system's video library and the development of set of retrieval strategies and a knowledge base that together allow the system to locate educationally relevant stories. Page 1 1. Introduction In this paper we show how a ...

This paper shows that the use of adaptation knowledge in CBR systems is heavily dependent on certain task and system constraints. Furthermore, the type of adaptation knowledge used in particular systems performing specific tasks is quite regular and predictable. These conclusions are reached by reviewing forty-two CBR systems in the literature and classifying them according to specified taxonomies. Three taxonomies are produced in the course of the paper. First, an adaptation-relevant taxonomy of CBR systems is outlined using four dimensions: whether adaptation is performed or not, whether single or multiple cases are used, whether case solutions are atomic or compound, whether parts of a compound solution interact with one another or not. Second, a taxonomy of tasks is produced based on a similar taxonomy proposed by Clancey (Clancey 1985). Third, a taxonomy of adaptation knowledge that distinguishes target-elaboration, role- substitution, subgoaling and goal-interaction operators is described. We then show how different systems cluster with respect to interactions between these three taxonomies. The conclusions of this paper could be applied as guidelines for system designers or support the comparative evaluation of systems

Progress in the area of interactive training applications has led to the formulation of methodologies that have been successfully transitioned out of research labs and into the practices of commercial developers. This paper reviews the academic origins of a methodology for developing training applications that incorporate branching storylines to engage users in a firstperson learn-by-doing experience, originally referred to as Outcome-Driven Simulations. Innovations and modifications to this methodology from the commercial sector are then reviewed, and the steps in this methodology are described, as implemented in current best practices. Finally, new research efforts based on this methodology are examined, including the introduction of natural language processing technology to enable human-computer conversations and the integration of branching storylines into real-time virtual reality environments. A prototype application to support leadership development within the U.S. Army that includes these advances is described.

It is a time-consuming and difficult task for an individual, a group, or an organization to systematically express and organize their expertise so it can be captured and reused. Yet the expertise of individuals within an organization is perhaps its most valuable resource. Q&A attempts to address this tension by providing an environment in which textual representations of expertise are captured as a byproduct of using the system as a semiautomatic questionanswering intermediary. Q&A mediates interactions between an expert and a question-asking user. It uses its experience referring questions to expert users to answer new questions by retrieving previously answered ones. If a user’s question is not found within the collection of previously answered questions, Q&A suggests the set of experts who are most likely to be able to answer the question. The system then gives the user the option of passing a question along to one or more of these experts. When an expert answers a user’s question, the resulting questionanswer pair is captured and indexed under a topic of the expert’s choice for later use, and the answer is sent to the user. Unlike previous work on question-answering systems of this sort, Q&A does not assume a fixed hierarchy of topics. Rather, experts build the hierarchy themselves, as their corpus of questions grows. One of the main contributions of this work is a set of techniques for managing the emerging organization of textual representations of expertise over time by mediating the negotiation of shared representations among multiple experts.

Case-based reasoning, inspired by people, was developed as a model for creating intelligent systems – systems that could reason by reference to their previous experiences. Such systems, we said, had the potential to behave more like real experts than could traditional expert systems. Reasoning based on experience would allow them to be more flexible and less brittle than rule-based systems, and, with learning from experience built into their architectures, they would become more capable over time (Kolodner & Simpson, 1989). Many experimental automated case-based reasoners have been created (see the lists, e.g., in Kolodner, 1993) and, indeed, CBR has proven to be quite a useful technology (refs). More interesting to us, however, are the implications case-based reasoning holds as a model of cognition – implications about what it means to be a learner and implications about learning and education. (Kolodner, 1993). While most traditional theories of cognition emphasize how generalpurpose abstract operators are formed and applied, case-based reasoning makes concrete cases, representing experience, primary. CBR suggests that we think in terms of cases —

We describe an approach to building integrated performance support systems by using model-based task tracking to link performance support tools to video-based organizational memory systems, enabling contextually appropriate help and advice as well as proactive critiquing.