Interacting with a mediator agent in collaborative learning environments

Symbiosis of Human and Artifact Y. Anzai, K. Ogawa and H. Mori (Editors) © 1995 Elsevier Science B.V. All rights reserved.

895

I n t e r a c t i n g w i t h a M e d i a t o r A g e n t in C o l l a b o r a t i v e L e a r n i n g Environments GerardoAyala

and Yoneo Yano

Dept. Information Science and Intelligent Systems, The University of Tokushima, 2 - 1 Minami Josanjima-Cho, Tokushima, Japan 770. e-mail: [email protected] In this paper we present the basic issues in the modelling of a mediator agent in a computer-supported collaborative learning environment. The mediator agent is an intelligent software agent that has been designed based on ideas from agent modelling in distributed artificial intelligence [1] and social learning [2]. Mediator agents in collaborative environments support user interaction based on the communication of the learners' capabilities, goals and commitments. Mediator agents cooperate in the search of learning possibilities for the learners, in order to enhance the relevant collaboration and progress of a networked community of practice.

1. INTRODUCTION Interface agents are changing the style of human-computer interaction, from direct manipulation interfaces to working environments which enhance the cooperation between users and the transfer of information and know-how [3]. In this paper we present our approach in the development of software agents that support the interaction and relevant collaboration between users in collaborative learning environments. Collaborative learning environments are electronic environments that support and mediate the cooperative work and learning in a network [4]. GRACILE, our computer-supported collaborative learning environment, consists of software agents embedded in a context of social interaction, enhancing learning and facilitating the interaction of the learners in a community of practice. In this educational environment, learners work on real tasks within a group of cooperating individuals, using software agents that enhance their collaboration and capabilities. HCI (Human-Computer Interaction) "seeks to produce user interfaces that facilitate and enrich human motivation, action and experience" [5]. In collaborative learning environments learners are active agents, motivated by playing the role of teachers and learners and exchanging experiences in the application of knowledge in real situations.

896 In our organization of software agents for collaborative learning environments we have defined mediator agents (facilitators) and domain agents (personal assistants in the domain). Mediator agents support the collaboration of users, while domain agents provide assistance concerning the appropriate application of knowledge in the domain. In this paper we will discuss the characteristics of the mediator agent implemented in Project GRACILE, a multi-agent Japanese GRAmmar Collaborative Learning Environment* [6] implemented on a network of Macintosh computers. Working in GRACILE, learners are active collaborators interacting with the mediator and the domain agents in the creative use of Japanese language, constructing a dialogue together, assisting each other and negotiating the appropriate application of language patterns in different situations. 2. AN INTELLIGENT AGENT FOR COLLABORATIVE LEARNING An intelligent software agent is considered able to make its own goals and give suggestions to the user, performing without being instructed explicitly [7]. Mediator agents are software agents able to interact among themselves via an agent communication language, making requests, commitments and exchanging the necessary information for the relevant collaboration of the learners. Figure 1 presents the interaction of the mediator agent in the learning environment. Learners communicate and collaborate with each other via the mediator agents, and are supported by domain agents also accessible through the network. Domain agents are distributed in the network, and can be updated independently. In GRACILE we have implemented a set of domain agents able to assist the learner with the application of Japanese language patterns, expressions, and vocabulary [6]. Domain agents also make an analysis of the constructions produced by the learner, providing feedback about the proper application of domain knowledge.

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897 We have constructed a mediator agent in terms of its capabilities, commitments and beliefs [1], [8]. The mediator agent adapts to the learner by constructing a learner model (user model), which is considered a set of beliefs about the learner's capabilities, learning goals and commitments. The mediator agent creates its commitments according to its beliefs about the learner and the messages it receives from other mediator agents and the learner. The capabilities of the mediator agent are performed when a commitment is executed.

2.1. Interaction with the mediator agent Interacting with the learner and other mediator agents in the network, the mediator agent continuously executes the following steps: s t e p 1 . Read messages from the learner or other mediator agents in the environment. step2. Make commitments based on the received messages and its beliefs about the learner. step3. Execute the commitments, applying its capabilities. The commitments of the mediator agent are executed in order of appearance, invoking its capabilities, which are: a) Updating the learning group's virtual common workspace b) Determining the learning and collaboration possibilities of the learner in the group. c) Negotiating the learning activities with the learner, based on h e r / h i s learning and collaboration possibilities, until s/he makes a commitment to perform a task. d) Informing its learner of who is able to assist her/him. e) Sending a request of assistance from its learner to the domain agents or other learners. f) Providing information about its beliefs concerning its learner's goals, commitments and capabilities. g) Informing its learner about the arrived requests and messages from other learners. step4. Update its learner model, if necessary. step5. Return to step1. 2.2. The mediator creates its commitments The mediator agent decides to create its commitments by reasoning with a set of commitment rules [1]. The conditions of these commitment rules refer to the received messages from other mediator agents and the mediator agent's beliefs about the learner's capabilities, commitments and goals (learner model). 2.3. Modeling the learner The learner model is represented in terms of the capabilities, commitments and goals of the learner within the learning community. For our mediator agent we have designed the learner model [8] according to Vygotsky's social learning

898 theory [2]. The mediator agent maintains a representation of the learner's potential development level as the knowledge used by the learner with the assistance from other learners or from the domain agents. The learner's actual development level is the knowledge the mediator agent believes can be applied by the learner without any assistance.

3. SUPPORTING COLLABORATION We have designed the mediator agent in order to provide expectations of assistance and collaboration consistent to the reality of the learning group. Figure 2 presents the mediator agent's architecture, together with the structures and procedures that allow it to support the collaboration among learners. 3.1. Learners locate their level and collaboration possibilities in the network The communication of learning goals and capabilities is a key aspect in computer-supported collaborative learning, allowing learners to reflect on their level in the group and on the planning of~ domain knowledge application in the common problem. Learner models are accessible in the network by the learners at any time, via the mediator agents.

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Figure 2. Architecture of the mediator agent

3.2. Cooperative construction of the user's learning possibilities The learner's prior knowledge and her/his motivation to learn are considered significant aspects of human learning [5]. In collaborative learning we must consider also the knowledge already acquired by the rest of the learning group members. In order to represent the learning possibilities of the learner in the

899 group, the mediator agent constructs the representation of the learner's groupbased knowledge frontier which consists of the union of two sets: a) The complex knowledge elements that include basic internalized elements. This set represents the learner's possible progress from an individual point of view, based on the structural relations of knowledge elements. b) The knowledge elements internalized by other learners in the group. This set represents the learner's possible progress from the social point of view, considering the knowledge already acquired by other members of the group. In this way the mediator agent, with the cooperation of other mediator agents, determines the possibilities for knowledge development of the learner, considering the structural and social aspects of knowledge in the actual community of practice. The mediator agents cooperate exchanging data from their group based knowledge frontiers. They also cooperate with each other indirectly by proposing to their respective learners a list of learning tasks (application of knowledge elements) which are relevant to them and the other learners in the group. Based on this representation mediator agents work in the behalf of the learners, searching for learning and assistance possibilities.

3.3. Negotiating the learning activities with the learner Users must have a feeling of control while interacting with a software agent [7]. Learners are free to commit to perform tasks for the group's common problem. The mediator agent presents first a list of knowledge elements and the tasks where they are applied, based on the knowledge-based frontier elements. Then it incrementally relaxes its negotiating position including more elements, according to the learner's requests and her/his potential development level. 3.4. Supporting collaboration among learners The mediator agent updates its representation of the virtual common workspace (in GRACILE it is a dialogue in Japanese constructed by the group) and then informs other mediator agents of the constructions produced by its learner, so they can continuously update their representations. When learners need help from their colleagues they may request assistance in the following ways: a) Directly requesting a specific learner for help in a given task. b) Asking the mediator agent to send requests to all learners in the network. c) Accepting a suggestion from her/his mediator agent to ask for assistance from a given learner considered capable to help in the task. Learners engaged in a collaborative process may have their own valid viewpoints on knowledge application. When presenting a different viewpoint, it has to be supported with an example which must be considered valid by the group or the domain agents.

900 3.5. Supporting collaboration from domain agents In GRACILE, domain agents are distributed in the network, located in the computers where the mediator agents are also active. Learners may ask a domain agent for help. In the case when the domain agent can not respond to a request, the mediator agent requests assistance from other domain agents in the network.

4. CONCLUSIONS Software agents are changing the style of human computer interaction. In computer-supported collaborative learning environments we have developed a mediator agent which enhances the relevant collaboration between learners, allowing the communication of learners' goals and capabilities, and adapting their negotiating position to the capabilities of the learners in the group, considering both the structural and the social aspects of knowledge progress. Based on information from other learner models in the network, the mediator agent generates a representation of the learning possibilities of the learner in a networked community of practice.

REFERENCES

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