An Experiment using Software Agents for Dialogue

An Experiment using Software Agents for Dialogue Analysis In Collaborative Distance Learning Patricia Augustin Jaques [email protected]

PPGC-UFRGS1

Flavio Moreira de Oliveira [email protected] PPGCC - PUCRS2

Rosa Maria Vicari [email protected] PPGC-UFRGS

ABSTRACT Trends in distance education show a growing emphasis in collaborative learning, stimulating students to exchange ideas and information. A collaborative environment, however, will demand a higher effort from the teacher, who will have to supervise all the discussions among the learners, so that they do not deviate from the intended topic for the lesson. Moreover, the information proceeding from the interactions among the students will provide to the teacher features that allow a individual evaluation of the students and his course. In this way, this paper describes a first experience using Multi-agent architecture able to monitor the communication tools in a distance learning group. This system analyzes the discussions taking place in these tools (discussion list, chat and newsgroups), showing to the teacher statistical information (percentile of participation and number of changed messages) and identifying possible associations in the interactions, such as, topics and subtopics that interest the students, groups of learners that interact intensively, etc.

KEYWORDS CSCL, Educational Dialogue Interaction Analysis, Software Agents. INTRODUCTION The arrival of the computer networks, specially the Internet, made the Distance Learning (DL) become greater in quality. From this moment on, new technologies started being used, offering, thus, a differentiated means of communication for students and teachers. It was observed, though, that for students to feel involved in their study he/she must interact with other students of the course. As a classroom, students must work in groups, exchange ideas and help each other. This collaborative view of teaching became a new trend in DL, which is now looking for tools and methodologies (for instance, in the groupware) that are appropriate for the teaching and learning process (Maçada and Tijiboy, 1998). Yet, a collaborative teaching environment is going to give the teacher a greater number of activities. This is due to the fact that all communication processes among students have to be supervised by the teacher. This monitoring activity has a distributed nature. Due to all these facts, a Multi-Agent architecture is proposed. It monitors the main communication tool within a classroom, among which are discussion list, newsgroups and virtual conferences (chat). This agent collects data from the ongoing discussions; it analyzes them and gives such information to the teacher. The analysis shows to the teacher an overview of what is going on in the group and with the students, as well as important information such as student participation in class, subjects that are of main interest for the group and for each student individually, etc.

1 PPGC-UFRGS - Programa de Pós-Graduação em Computação – Instituto de Informática - Universidade Federal do Rio Grande do Sul - Bloco IV, Campus do Vale, Av. Bento Gonçalves 9500, Porto Alegre, RS, Brazil. 2 PPGCC - PUCRS - Programa de Pós-Graduação em Ciência da Computação. – Instituto de Informática - Pontifícia Universidade Católica do Rio Grande do Sul. Prédio 16 - Av. Ipiranga, 6618, Porto Alegre, RS, Brazil

The paper is organized as follows: section 2 presents a brief description of multi-agent systems and software agents; concepts used for modeling the proposed system; section 3 presents the motivation; section 4 describes the architecture of the proposed Multi-agent system and the implemented prototype; section 5 presents the validation of the prototype and conclusions; section 6 describes the perspectives for future work and, finally, section 7 lists the bibliography used in this paper.

MULTI-AGENT SYSTEMS Starting from the decade of 1970, with the arrival of the Intelligent Tutoring Systems, the researchers in Computer Science in Education observed the need to use techniques of Artificial Intelligence to turn education systems most flexible and adapted to users needs. Now, the collaborative focus of the distance learning brings a great number of problems and activities in which the agents technology can be very well used, such as monitoring the students as well as providing information to the teacher. When a system has more than one agent, it is known as Multi-agent system (MAS). An agent is an autonomous entity that has knowledge of its own existence and of the others agents' existence and, therefore, they collaborate with each other to reach a common goal in an environment (Sichman et al., 1992). In a MAS, agents must have some specific capacities in order to interact in the same environment. This is the reason why the agents must recognize their existence and the existence of other agents. They must be able to communicate, and for doing so, they need their own language. Each agent must have knowledge and skills to perform a specific task and must cooperate in order to achieve a global purpose. According to (Sichman et al., 1992), the different agents capacities for resolution of problems allow classifying them in two main categories: reactive agents and cognitive agents. Now, even so, we can observe that reactive agents and cognitive agents are the begin and the end of a classification line where new denominations appeared as, for example, software agents. The agents of this paper are considered software agents. A software agent is a piece of software that executes a certain task using incoming information of its environment. The software can be able to adapt itself based on the changes that are happening in its environment, generating, in this way, the wanted result. Software agents are computer systems to which one can delegate tasks. They differ from conventional software in that they are long-lived, semi-autonomous, proactive, and adaptive (MIT Groups, 2000) (Hermans, 1996) (Koda, 1996). Although it is still a very current area, there are some examples of applications using software agents (Hermans, 1996): a) b) c) d) e) f)

Manage nets and systems; Filter e-mails (Malone et al., 1997), (Maes, 1997); Look for information in the web (Knoblock and Ambite, 1997); To mark meetings in a collaborative group (Maes, 1997); In the e-commerce, aiding an user to search products in the web or managing sales; As interface agent, where the agent will monitor the user's actions, to develop models of the user's abilities and automatically help the user when appears problems (Laurel, 1997).

MOTIVATION The motivation for the development of the agent’s architecture to monitor the collaboration resulted of some interactions with educators of the area of distance learning (We had the assistance of the distance learning teachers of PUCRS). When asked on possible tools to help teachers and students in the aspect of collaboration, it was observed that there weren't tools to aid teacher to monitor the interactions among students. According to the educators, collaborative classes generate a great number of interactions, which is difficult to the teacher to monitor and it resulting in little time to accomplish other important works in the class. So, we decided to implement a multiagent system to monitor and analyze collaboration that provide to the teacher information that will help him/her in the evaluation of students and of its course. With that, the teachers would have larger free time to do other activities in the course.

2

SYSTEM SPECIFICATION The multi-agent system proposed is composed of four agents. Three of them, witch we call collecting agents are responsible for collecting data based on the messages of the Internet communication mechanisms, that are: discussion list, newsgroup and chat. The fourth agent is the teacher’s agent. The teacher's agent, when asked by the teacher, requests analysis made by other agents and shows them to the teacher. In this system there is an agent for each one of the Internet communication mechanisms available (discussion list, chat and newsgroup). This single agent is responsible for collect all the information in that tool, and it happens periodically. For instance, there is an agent responsible for the discussion list. This agent is put into operation from time to time by the system and then it brings every new e-mail message that might arrive at the list. In an alike way, there is an agent responsible for the newsgroup and other one for the chat log file. Each agent has its own local database, which stores the collecting data. After this data collecting it does the analysis. When the teacher decides to see the analysis, he will ask to the teacher's agent. The teacher's agent will request to the others agents that will send the name and the address of the file containing the analysis. The teacher's agent will do a local copy of the file and will show the analysis to the teacher. The collecting agents are located at the teacher’s local folder, where the e-mail and the newsgroup’s messages are stored. The teacher’s agent will be installed in the machine chosen by the teacher. The general architecture of the multi-agent system can be seen in [Figure 1].

3

Request analysis

4

Read messages

Analysis Request

1 CHAT

Show analysis

7

Teacher 6 Agent

URL of analysis file

5

Chat Agent Write analysis

2

Write collected data

Collected Data

Figure 1: Trace of the Agents Execution of the Analysis. Information Collection While they are reading new messages, the collecting agents look for data that will be used for posterior analysis. These information are stored in a database that has the following fields: ID

FROM

REPLY

SUBJECTS

SUB-SUBJECTS

DATE

TIME

COMMUNICATION MECHANISM

Where: a) ID: Message identifier. In the chat it identifies each sentence formulated by a user. b) From: Student sender of the e-mail or newsgroup message or of the sentence in the chat. c) Reply: It is used just for news. It identifies when a news article is sent as an answer or as an argument to an existent message (thread). d) Subject: This field contains a list of subjects. e) Sub-Subject: As the students keep on discussing a certain subject, there will be much more specific comments to be sent. It may create a new sub-subject. f) Date: Sending date of the e-mail, newsgroup or the chat-meeting message. g) Time: Sending time of the e-mail, newsgroup or the chat-meeting message. h) Communication Mechanism: Tool in which the agent collects data. It can be: discussions list, newsgroup and chat.

3

Identification of the Subjects and Sub-Subjects in the Messages The subjects and sub-subjects are identified in the messages subject (just in e-mails or news) or by keywords in the message content. The agents consider keywords all the nouns found in the text. In this case, adverbs, prepositions, numeric values and verbs are not considerate. It is because a noun has richer semantic value than the other words (Salton and McGill, 1983), (Baeza-Yates and Ribeiro-Neto, 1999). In the first analysis made by agents, they considered keywords all the nouns and verbs. But, the results of the first analysis show that verbs didn't represent so much the messages subjects. In order to check the syntactic and morphological meaning of these words we are using a lexical-morphological dictionary of the Lexis project (Lima et al., 1997) and a thesaurus that is supplied by the system. The use of the lexical dictionary facilitates to identify the nouns in the text, while Thesaurus is used to identify synonyms and hierarchy relationships among the words (sub-subjects). Messages are in Portuguese. Data Analysis Each agent has its own local database, which stores the data, as explained before. After this data collecting it does the analysis, based on data existent in its database. The period of analysis can be default (analyzes all messages sent after the last analysis), or teacher can ask interaction analysis that happened in a certain period of time, with some determined initial and final dates. Basically, there are three kinds of associations that can be identified by the agent in the interaction analysis: a) Student-student: It identifies which students interact more with each other. b) Student-subject: The agent gets information about which subjects each student discusses more. This kind of analysis allows identifying which topics are more interesting for each student. c) Student-student-subject: The agent identifies which subjects are of greater interest for a specific group of students. Besides all associations mentioned above, it is possible to have access to some statistical analysis based on data gathered in the messages: the number of messages exchanged, participation percentage, etc. This statistical information helps the teacher to evaluate the student’s participation in the interactions, checking, thus, if the collaboration is happening indeed. Information originated from the analysis is, at a first moment, exhibited as table charts. For all the analysis, they are shown the name of the tool (discussion list, newsgroup or chat), the date period (date of the most recent and older message) and the total number of messages exchanged in that period. Student-Student-Subject Analysis This kind of analysis identifies the groups of students formed when the discussion of a determined subject is started. In order to do so, the new messages that refer to a certain subject are highlighted, thus, generating a new group. So, each table refers to a single group that treats about a single subject. This subject is, however, formed by several students who sent messages related to that specific subject. The content of each field of the table is explained below:

Group ID Students

Subjects Sub-subjects

Total number of messages Individual number of messages

Where we have: a) Group ID: Sequential number that identifies a group of students. b) Students: The name of all students that make part of a group. Each student may belong to various groups. c) Subject: The association is identified according to the subject which the students are discussing. In the preanalysis the collecting agents store all subjects and sub-subjects of a message. When working out the associations for the analysis, the agent seek for the most specific subject (probably a sub-subject) that may be common to a larger number of students.

4

d) Sub-subject: In the case of a message that is specific, treating about a new topic that belongs to the subject in discussion, this topic is detailed at the sub-subject field. e) Individual number of messages: The number of messages sent by a student about a determined subject. Student-Student Analysis The reply field of the message allows the identification of the student-student association. In this way, when a student sends a message as answer to another message, it is created a new group in the student-student analysis. All the students that send a message in reply to these messages are part of this same group. This association is just exhibited for the newsgroup, because in the discussion list the messages are sent as reply to all messages of the list. Group ID Students

Total number of messages Individual number of messages

Each table shows the information about one group. It is shown a group ID, the total number of sent messages in the group, the name of the students and the number of messages sent by each one. Student-Subject Association In order to help the teacher to have an individual evaluation of each student’s participation and specific interest, another kind of analysis is performed. It contains specific information referring to each student in the class. This information allows the teacher to follow the student’s individual participation, as well as their subjects of interest. The teacher may be able to send motivation messages to make the student interact with the whole group. Each table shows information about an only student. The following information are exhibited: Student Subjects

Total number of messages Sub-subjects

Number of messages

Percentage of participation (%)

Where we have: a) Students: Student’s names. b) Subject: List of subjects discussed by the student. c) Sub-subject: For each subject discussed by the student, there is a list of topics associated to that subject commented by the student. d) Individual number of messages: Number of messages sent by student about each subject. e) Student’s percentage of participation: The student’s percentage of participation for each subject commented among the students. f) Total number of messages

Implemented Prototype For the implementation of the multi-agent system, we used the Java Agent Template framework (JAT) version 0.3 (Frost, 1998). JAT supplies a group of classes, written in Java language, that allows the construction of software agents that communicate peer-to-peer in a community of agents distributed in the Internet. JAT supplies a KQML interface for communication among the agents (Finin et al., 1997), not being necessary to think in the communication in lower level, using sockets and Internet addresses. For so much, this environment has an names server agent which stores the name and IP address of all agents in the society. It facilitates the communication because agents just need to know the name of the agent to which it needs to send the message. All the functionalities of the agents were implemented using the Java language (Sun, 1998).

5

VALIDATION The proposed system was used for analysis of chat interactions in a virtual class of the Global Campus Laboratory in the Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS) (Ferreira and Campos, 1998). In PUCRS, the students that are going to take the discipline of Introduction to the Computer science may choose the presence teaching, as it usually happens, or the virtual classes. The discipline of Introduction to the Computer science presents basic notions of hardware and concepts of logic programming. In the virtual modality, the first class is in the university where the teacher present and explain the classes and software that will be used. The other classes are virtual and they happen in two weekly meetings of chat in the normal time of class. For validation of the prototype, the system analyzed the chat meetings logs of a virtual group. For example, let us see one of the classes analyzed by the system. In that class, the students, mediated by the teacher, discussed the following subjects: - Valves and Transistors; - Input and output devices; - Operating systems; - Programming (algorithms and languages). The student’s individual analysis (student-subject) shows the students names, total number of messages of each student, the subjects and sub-subjects, as well as the number of messages on each subject. The table 1 shows, for example, the individual analysis of one of the students: STUDENT: Ingrid SUBJECTS

MESSAGES NUMBER: 17 SUB-SUBJECTS

Assunto Aula Computador Dia Encontro Figura Hardware Linguagem Memória Mesa Mudança Opção Dispositivo Pesquisa Programação Silício Software

Terminal, software, hardware,

Dispositivo, vídeo, memória, c, ram,

teclado, saída, mouse, impressora, Linguagem, Programação, sistema,

NUMBER

%

4 1 11 1 1 3 7 1 1 1 1 1 3 1 1 1 2

0% 0% 2% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Table 1: An Example of an Individual Analysis of the Students We can observe through the students individual analysis that the words that appear in more than one message, generally, are the ones that really are the subjects of the messages (see black words in the table). In this way, later, the agent can just select words that appear in more than one message. At the moment, we choose to show all words because the students interact so little and, in this way, keywords appear in few messages. The teacher can observe the student participation by the number of sent messages, as well as for subjects that appears in the analysis. Another analysis accomplished by the agents searches to identify students groups that discuss on a certain subject (student-student-subject), showing the sub-subjects discussed by each student. The agent just considers as groups, those subjects discussed by three or more students. To illustrate, let us see the example in the table 2.

6

SUBJECT= Dispositivo

MESSAGES NUMBER = 79

STUDENTS

SUB-SUBJECTS

Nr

%

Andréa Giraffa Ingrid Leonardo Marcelo Paulo Roberto Rodrigo

Modem, impressora, Camera, pen, saída, driver, entrada, mouse, impressora, Teclado, saída, mouse, impressora, monitor, pen, driver, impressora, scanner, impressora, Caneta, joystick, saída, entrada, modem, caneta-dicionário monitor, teclado, modem, microfone, impressora, camera, monitor, caneta, teclado, saída, cd-r, driver, entrada, mouse, microfone, impressora Monitor, modem, impressora, Caneta, saída, fita, impressão, driver, entrada, modem, impressora, Caneta, joystick, impressão, modem, mouse, impressora,

1 22 3 5 2 4 7 14

1% 27% 3% 6% 2% 5% 8% 17%

2 13 6

2% 16% 7%

Victor Xactor Zingano

Table 2: An Example of a Subjects Group Analysis This analysis shows the students names that discussed a certain subject - in the example, dispositivos (devices) - as well as the related sub-subjects. As the system has a thesaurus, the agents can identify sub-subjects and group them in groups of subjects. In the example above, Andrea discusses video, Victor monitor and Marcelo scanner. These students are contained in a same group, because the commented components are computer devices. Therefore, video, monitor and scanner are sub-subjects of devices. The students' names will also be in the subject groups: monitor, video and scanner, in case the teacher wants information of the smaller groups. In general, the analyses of the group of subjects show to the teacher subjects of the messages. This can be observed by the number of sent messages that contain these words, although this information is a little dispersed with words that don't express the content of the messages so straightforward. The students individual analysis, even so, clearly showed in the analysis, which students really participated in the classes and which students were dispersed in subjects not related to the class. In general, the other analysis showed similar results.

CONCLUSION The analysis of the interactions of a virtual group (see section 5) allowed us to observe that the proposed architecture and the types of analysis showed are appropriate for the wanted objective of supplying information to the teacher that aided him/her to monitor student's interaction. It doesn't fit to the system to be the only evaluation mechanism of the collaboration among the students. The teacher's final evaluation and accompaniment are indispensable, however, the tool can be used as a aid resource to the teacher. We observed, even so, that the analysis would be more precise to the related subjects if some deeper method of semantic analysis was used (Natural Language Processing), for example, discourse analysis (Grosz and Sidner, 1986), instead of keyword search. For limitations of time, we worked in larger depth in the multi-agent architecture that was our main objective. Besides, the validation allowed us to observe that new aspects should be considered for larger efficiency of the system, which we mention above: a) Expressions: Some subjects are formed for more than one word and they lose the meaning when it is just considered one of the words. For example: programming language. b) Orthographic Mistakes: The language used by the students in the interaction tools is quite informal. Besides, in the hurry in exposing their ideas (mainly in the case of synchronous tools as chat), the students make many orthographic mistakes and they use abbreviations that are not considerate by the agents. A possible solution is to create an agent that performs orthographic correction of the messages before they be analyzed. c) Improvement of the interface of the Teacher's Agent: the teacher’s agent shows the results of the analysis in text, in a simple way. To provide better visualization of the results for the teacher, the interface can be improved inserting hypertext mechanisms that would allow to the teacher to link among the available information. In this way, for example, the teacher could, after observing the analysis of a group of students, to select a student to visualize individual information about him. d) On-line Prototype: Currently, the agent accomplishes the analysis of the log files of chat meeting. Another way would be the agent to work on-line, where he/she is connected to a virtual conference (chat meeting) and,

7

during the section, it does the analysis and it show the results in the moment in that the conference is happening. Due to limit of time for development of the prototype, the on-line part of the system was not implemented, what can be did as future work. For so much, the proposal architecture can be well used, just taking care some relative implementation details, such as, which software for conference will be used, as how the agent should be connect to the conference, etc.

FUTURE WORK (IN PROGRESS) The limitations observed in this work (see section Conclusion), help us to model a new online system that will analyze the student's messages and assist and guide the students in communication collaborative tools. This system is modeled as an animated pedagogical agent and it is part of the multi-agent architecture of the project “A Computational Model of Distance Learning Based in the Socio-Cultural Approaches” Piaget (1976, 1978, 1979, 1983), Freire (1980, 1995, 1996), Lévy (1999), Morin (1990) and Vygotsky (1998). In this project, two PhD theses and two master dissertation works are under development (Andrade et al., 2001). This animated pedagogical agent, that we call collaboration agent, assist the interaction among students in a collaborative tool, in real time or not, motivating them, correcting wrong concepts and providing new knowledge. This guide agent will consider not only cognitive capabilities of students, but also social and affective ones, which becomes a more qualitative mechanism of collaboration between students and learning. Five types of artificial agents are part of the architecture of the system – Diagnostic Agent, Mediating Agent, Collaboration Agent, Social Agent and Semiotic Agent – and human agents (learners and teachers). The Mediating Agent is an interface agent that is responsible for presenting pedagogical contents to the student. All user actions will be collected by the Mediating Agent and sent to the Diagnostic Agent. The Diagnostic Agent updates the information in the student model and verifies, according to received data, if it is necessary to use a new educational tactic and it sends this tactic to the Mediating Agent. If this tactic is, for example, the presentation of an instructional content, the Mediating Agent makes a request to the Semiotic Agent. The Semiotic Agent searches the requested sign or instrument in the knowledge base (for example: contents, animations, videos, chat) and sends them to the Mediating Agent to be shown to the student. When the Mediating Agent verifies a deficiency in the student’s learning and considers it would be interesting to perform an activity in group, it will make a request to the Social Agent. The Social Agent will create a Collaboration Agent and form a study group of students which the Collaboration Agent will mediate/monitor the interaction between them. There is a Diagnostic Agent and a Mediating Agent for each student, a Semiotic and Collaboration Agent for all the society and a Collaboration Agent for each group of students formed. More information about this agent can be found in (Jaques et al.; 2002).

REFERENCES Andrade, A. F.; Jaques, P. A.; Vicari, R. M.; Bordini, R.; Jung, J. (2001) A Computational Model of Distance Learning Based on Vygotsky's Socio-Cultural Approach. Proceedings of the MABLE Workshop (San Antonio, Texas, May 19-23). X International Conference on Artificial Intelligence on Education. Baeza-yates, R.; Ribeiro-Neto, B. (1999) Modern Information Retrieval. Usa: ACM Press, 1999. Bercht, M.; Moissa, H.; Viccari, R.M. (1999) Identificação de fatores motivacionais e afetivos em um ambiente de ensino e aprendizagem. Proceddings of SBIE’99 (Curitiba, November 23-25) (Poster). Boone, K. (2000) A Human-computer Dialogue Authoring System for Web Interaction. URL: http://citeseer.nj.nec.com/390875.html. Bradshaw, J . (1998) Introduction. In: Bradshaw, Jeffrey (Ed.). Software Agents. Menlo Park: AAAI Press/The MIT Press, 1997. p.3-48. Ferreira, S. N.; Campos, M.B. (1998) CBP 2001: Uma experiência prática de sala de aula virtual nos cursos de graduação da PUCRS. Latin-Iberoamerican Congress On Operations Research, 9, 1998, Buenos Aires. Finin, T.; Labrou, Y., Mayfield, J. (1997) KQML as an agent communication language. In: BRADSHAW, Jeffrey (Ed.). Software Agents. Menlo Park: AAAI Press/The MIT Press, 1997. p.291-316. Foner, L. (1997). Entertaining Agents: A Sociological Case Study. Proceedings of the First International Conference on Autonomous Agents (AA '97),( Marina del Rey, California). Franklin, S.; Graesser, A. (1996) Is it an Agent, or just a Program? A Taxonomy for Autonomous Agents.

8

Proceedings of International Workshop on Agent Theories, Architectures and Languages, 3, 1996, SpringVerlag. Freire, P.; Fagundes, A. (1995) Por uma Pedagogia da Pergunta. Ed. Paz e Terra, Rio de Janeiro. Freire, P.; Fagundes, A. (1996) Pedagogia da Autonomia; Saberes Necessário A Prática Educativa. Ed. Paz E Terra, São Paulo. Freire, P.; Fagundes, A. (1980) Conscientização: Teoria e Prática da Libertação: uma Introdução ao Pensamento De Paulo Freire. Editora Moraes, São Paulo. Frost, Robert. (1998) Java Agent Template (JAT). URL: http://java.stanford.edu/java-agent/html. Grosz, B.; Sidner, C. (1986) Attention, Intentions, and the Structure of Discourse. Computational Linguistics, Cambridge, v.12, n.3, p.175-204, set. 1986. Hermans, B. (1996) Intelligent Software Agents on the Internet: an Inventory offered funcionality of (near-) future developments. Neverthelands, 1996. Thesis (PhD. in Computer Science) - Faculty of Computer Science, Tilburg University, 1996. URL: http://www.hermans.org/agents. Jaques, P.; Andrade, A.; Jung, Joao; Bordini, Rafael; Vicari, Rosa. (2002) Using Pedagogical Agents to Support Collaborative Distance Learning. To appear in this same conference. Knoblock, C.; Ambite, J. L. (1996) Agents for Information Gathering. In: BRADSHAW, Jeffrey (Ed.). Software Agents. Menlo Park: AAAI Press/The MIT Press, 1997. p.347-374. Koda, T. (1996) Agents with Faces: A Study on the Effects of Personification of Software Agents. (Dissertation Master of Media Arts and Sciences) at the Massachusetts Institute of Technology. September, 1996. Laurel, B.. (1997) Interface Agents: Methaphors whith Character. In: BRADSHAW, Jeffrey (Ed.). Software Agents. Menlo Park: AAAI/MIT Press, 1997. p.67-78. Levy, P (1999). Inteligência Coletiva. Por uma antropologia do ciberespaço. Edições Loyola, São Paulo. Lima, V. L. S. de; Abrahão, P. R.; Paraboni, I. (1997) Approaching the dictionary in the implementation of a natural language processing system: toward a distributed structure. In: SOUTH AMERICAN WORKSHOP ON STRING PROCESSING, 4, 1997. BAEZA-YATES, Ricardo (Ed.) Anais... Valparaiso: 1997. Maçada, D.; Tijiboy, A. (1998) Aprendizagem Cooperativa em Ambientes Telemáticos. Proceedings of IV RIBIE. Brasília, 1998. Maes, P. (1997) Agents that Reduce Work and Information Overload. In: BRADSHAW, Jeffrey (Ed.). Software Agents. Menlo Park: AAAI Press/The MIT Press, 1997. p.145-164. Malone, T.; Lai, K.; Grant, K. (1997) Agents for Information Sharing and Coordenation: A History and Some Reflections. In: BRADSHAW, Jeffrey (Ed.). Software Agents. Menlo Park: AAAI Press/The MIT Press, 1997. p.109-143. MIT Media Laboratory, Software Agents group of the (2000). http://tomoko.www.media.mit.edu/groups/agents/. July, 2000. Morin, E. (1990) Introdução ao Pensamento Complexo. Lisboa: Publicações Instituto Piaget. Ogata, H. and Yano, Y. (2000). Combining knowledge awareness and information filtering in an open-ended collaborative learning environment, 11, 33-46. http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_11/ogata.html. Piaget, J. (1976) A Equilibração das Estruturas Cognitivas - Problema Central do Desenvolvimento. Zahar Editores, Rio de Janeiro. Piaget, J. (1978) A Tomada de Consciência. Edusp/Melhoramentos, São Paulo. Piaget, J. (1979) O Nascimento da Inteligência na Criança. Ed. Zahar, Rio de Janeiro. Piaget, J. (1983) A Epistemologia Genética /Sabedoria e ilusões da Filosofia; Problemas de Psicologia Genética; 2. Ed. – São Paulo: Abril Cultural. Pilkington, R. (2001). Analysing Educational Dialogue Interaction: Towards Models that Support Learning. Intelligence Journal of Artificial Intelligence in Education, 12. Porayska-Pomsta, K.; Mellish, C. and Pain, H. (2000) Aspects of speech act categorisation: towards generating teachers' language, 11, 254-272. http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_11/porayska-pomsta.html. Rourke, L.; Anderson, T.; Garrison, D.; Archer, W.. (2000) Methodological Issues in the Content Analysis of Computer Conference Transcripts. International Journal of Artificial Intelligence Education. Vol 12, 2000. http://cbl.leeds.ac.uk/ijaied/abstracts/Vol_12/rourke.html Salton; G. and Mcgill, M. (1983) Introduction to Modern Information Retrieval. USA: MacGraw-Hill, 1983. Sichman, J. S.; Demazeu, Y.; Boissier, O. (1982) How can knowledge-based systems be called agents? Proceedings of Simpósio Brasileiro De Inteligência Artificial, 9, Rio de Janeiro: Pontifícia Universidade Católica do Rio de Janeiro. p.173-185. Shoam, Y. (1997) An Overview of Agent-Oriented Programming. In: BRADSHAW, Jeffrey (Ed.). Software Agents. Menlo Park: AAAI Press/The MIT Press, 1997. p.271-290.

9

Soldato, T. & Boulay,B. (1995) Implementation of Motivational Tactics in Tutoring Systems. Journal of Artificial Intelligence in Education. vol(6), n.4, p.337-378. Soller, A. (2001) Supporting Social Interaction in an Intelligent Collaborative Learning System. Intelligence Journal of Artificial Intelligence in Education, 11. Sun Systems (1998) The Java Tutorial. URL: http://java.sun.com. (1998, October). Vicente, A. & Pain, H. (1998) Motivation Diagnosis in Intelligent Tutoring Systems. Proceedings 4th International Conference ITS’98. San Antonio: Springer-Verlag, pags.86-95. Vygotsky, L. S. (1998) A Formação Social da Mente: o Desenvolvimento dos Processos Psicológicos Superiores. Editora Martins Fontes, São Paulo. Wooldridge, M.; Jennings, N.. (1997) Intelligent Agents: Theory and Practice. URL: http://www.doc.mmu.ac.uk/ STAFF/mike/ker95/ker95-html.html. (1997, May).

10