Nine recommendations to make a computer supported situation work--论文代写范文精选

解决问题的水平和协作的水平本身,对于合作伙伴构建的模型,因为他们必须构建和协商共享来表示执行的任务。这是一个问题,也是一个学习的机会。信息交换在这样的情况下可以由计算机系统传输和管理沟通。下面的paper代写范文进行详述。

ABSTRACT
Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes?

INTRODUCTION
Modeling tasks have as a goal for a student to construct an explicit and runnable model of a domain. To obtain the data needed to construct the model, experimentation may be required. Modeling tasks carried out in collaboration seem to require communication and negotiation at the conceptual level (making explicit and elaborating concepts used), the level of problem solving (goal setting, planning, search for information, steps to take, partial solutions to evaluate), and the level of the collaboration itself (regulation and co-ordination). In this framework, partners build models of one another, because they have to construct and negotiate a shared task-representation (Erkens and Andriessen, 1994). This is a problem as well as an opportunity for learning. The exchange of information in such tasks can be mediated by computer systems that transmit and manage communicative actions.

The computer interface affords tools and utilities for the exchange of information (chat, email) as well as for problem solving (calculators, simulations, etc.). The study of such affordances is important for understanding facilitation of learning in interactive situations. The collaborative situation that we describe here permits protagonists engaged in a modeling task to exchange information via a network. In this situation (short) texts (messages) are produced with different functions, depending on the subtask that is being carried out. This communicative situation involves, for a given individual, alternating between message comprehension and production phases; learning may result from both the production and the comprehension of text, or from a series of texts. Messages concern the problem at stake, the problem situation, procedures and strategies, partial solutions and goals, conceptual issues, and the collaboration process itself.

The purpose of this chapter is a double one. In the first place we aim to more precisely define the mechanisms and constraints involved in this passing on of information by participants during Computer Supported Collaborative Learning (CSCL), from the viewpoint of psycholinguistic models of written language comprehension and production. In the second place, and as a result of this psycholinguistic analysis, we propose recommendations for improving computer support for modeling activities, by operating on parameters of the exchange of written messages during this process. This is not an empirical paper, but it is meant to serve as the starting point of issues to research. Our exploration is based on the assumption that the communication of knowledge, or more precisely, the activity of passing on knowledge as textual information (i.e.: the production of a structured message, comprising at least a written phrase), in an interactive situation, could by itself be important for learning. In part 2 of this chapter we discuss how such learning can be explained.

At least, learning by using language for communicating knowledge has two sources: comprehending other people’s messages and producing ones own messages. We will briefly discuss the current views held about the mechanisms involved in comprehending written information. More important for this chapter, we subsequently proceed to discussing the epistemic effect of text production, that is, the hypothesis that the activity of producing written code can bring about learning by the producer of the message. This ‘auto acquisition’ of knowledge through writing is a consequence of using written code, and more specifically, the result of the interaction between knowledge and the linguistic processing involved during writing. We discuss two possible explanations of this phenomenon.

The first explanation (the classical position) supposes that it is the act of writing for somebody, and the involvement in meeting specific communicative constraints, which modifies domain knowledge. The second explanation (the romantic position) supposes that, in contrast, free (written) expression of thought on the basis of some theme, without the requirement to meet constraints such as dealing with audience, permits the writer to constitute new domain knowledge. In the third and final part of this chapter we draw some consequences of this line of thinking to computer-mediated discussion. What general interface characteristics would be required for full text power to be used for conceptual reasoning and problem solving in computer supported collaborative learning situations (CSCL)? This chapter should be taken as an attempt to bring CSCL design experience to bear on issues about how writing affects learning.

The notion of collaborative modeling
We define a collaborative learning situation as one in which two or more students work together to fulfill an assigned task within a particular domain of learning to achieve a joint product. In ideal cooperation, the collaborating partners (two, at least) must have a common interest in solving the problem at hand. In addition, we suppose that in a learning situation, knowledge of either participant is insufficient to solve the problem. Furthermore, participants should be mutually dependent on the information and cooperation of the other to reach their (shared) goals. Only when the participants have abilities or information that are complementary, can co-operation be fruitful and will it be looked for (Erkens, 1998). As a result, not only may participants share some of each other’s knowledge, but also, in principle, new knowledge may be constructed during the activities.

We consider modeling as a problem-solving task, during which (incomplete) knowledge of concepts (called declarative or semantic knowledge) and knowledge for the application of concepts (often called procedural knowledge) are needed for solving the problem. In most educational situations during which modeling tasks are employed, the attainment of more of such knowledge is the main goal. In the collaborative situation the learning goal is supposed to be served by individuals communicating information. For this communication both protagonists apply linguistic knowledge for production and decoding of written messages, and pragmatic knowledge, seen here as knowledge of how to adapt the content and the form of a message to the goals of the situation and its participants. For the regulation of activities, we suppose the existence of metacognitive knowledge, which allows the protagonists to evaluate the implications of different knowledge activities.

Problem solving in individual modeling tasks entails dynamic application of domain knowledge, based on the protagonists’ knowledge. Learning is the result of active exploration, applying this knowledge to the problem solving process. Consequently, learning to model is based on appropriate experiences and reflections by the learner. The learning environment should be specifically tailored to meet these ends. The learner should not only be supported during problem solving, but also, and more importantly, supported in engaging in abstract reflections leading to (declarative) knowledge that would permit new problems to be solved independently of the supporting environment (Salomon, 1993). Many researchers claim this requires extensive use of specific forms of language (Ohlsson, 1993; Andriessen & Sandberg, 1999; Baker, 1999). A modeling task carried out as joint problem solving with exchange of written messages represents a rich environment. Knowledge acquisition does not merely involve learning by doing, as in the individual case, on, since at least two other modes of learning are possible: (1) learning by observation of the partner’s actions and (2) learning by understanding and producing language through which information is exchanged in relation to the problem solving situation, between (two) protagonists. Many functions may be served by using language; as stated above, we focus on the conceptual aspects - that is language referring to knowledge of the learning domain.(paper代写)

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