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Half day tutorial at ICCE 2003, Hong Kong, December 2, 2003. Pedagogical Agent Design for Distributed Collaborative Learning. Weiqin Chen and Anders Mørch* Dept. of Information Science, University of Bergen *InterMedia, University of Oslo. Outline. Software Agents Agents in education

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Pedagogical Agent Design for Distributed Collaborative Learning


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    1. Half day tutorial at ICCE 2003, Hong Kong, December 2, 2003 Pedagogical Agent Design for Distributed Collaborative Learning Weiqin Chen and Anders Mørch* Dept. of Information Science, University of Bergen *InterMedia, University of Oslo

    2. Outline • Software Agents • Agents in education • Pedagogical agents in distributed collaborative learning • A case study with demo • Lessons learned

    3. Software Agents

    4. Outline • Definition • Classification • Brief history • Research Questions • Tools and Platforms • Agent Applications

    5. What is an Agent? (1) • An over-used term autonomous agents, software agents, intelligent agents, interface agents, virtual agents, information agents, mobile agents • No commonly accepted notion

    6. What is an Agent (2) • ”computer programs that simulate a human relationship by doing something that another person could do for you”.T. Selker (1994) • ”persistent software entity dedicated to a specific purpose”.D. smith, et al. (1994) • ”situated in some environment, that is capable of flexible autonomous action in order to meet its design objectives”. N. Jennings & M. Wooldridge (1998) • Other definitions in S.Franklin & A. Graesser (1997) http://www.msci.memphis.edu/~franklin/AgentProg.html

    7. What is an Agent? (3) • Weak Notion of Agency: • Autonomy • Social ability • Reactivity • Pro-activeness • Strong Notion of Agency: • Belief, desire, intention

    8. What is an Agent? (4) • Two extreme views: • Agents as essentially conscious, cognitive entities that have feelings, perceptions, and emotions just like human • Agents as automata and behave as they are designed and programmed

    9. Agent Taxonomy Franklin and Graesser’s (1997) agent taxonomy

    10. Agent Typology Typology based on Nwana’s (Nwana, 1996) primary attribute dimension

    11. Multiagent Systems (MAS) • A loosely coupled network of problem solvers that work together to solve problems that are beyond the individual capabilities or knowledge of each problem solver. • Characteristics: • Limited viewpoint • No global system control • Decentralized data • Asynchronous computation

    12. Origin of Software Agents The idea of an agent originated with John McCarthy in the mid-1950’s and the term was coined by Oliver G. Selfridge a few years later, when they were both at the Massachusetts Institute of Technology. They had in view a system that, when given a goal, could carry out the details of the appropriate computer operations and could ask for and receive advice, offered in human terms, when it was stuck. An agent would be a ”soft robot” living and doing its business within the computer’s world. -Alan Kay Computer Software, 1984

    13. Brief History(1) • 1994 • CACM special issue on agents • P. Maes, ”Agents that reduce work and information overload” • D. Norman, et al. ”How might people interact with agents” • ATAL (Workshop on Agent Theories, Architectures, and Languages) • 1995 • ”Intelligent agents: Theory and Practice” by M. Wooldridge & N. Jennings • ICMAS (Int. Conf. on Multi-Agent Systems)

    14. Brief History (2) • 1996 • PAAM (Int. Conf. on Practical Application of Intelligent Agents and Multi-Agent Technology) • FIPA (Foundation for Intelligent Physical Agents) • 1997 • J. M. Bradshaw, ”Software agents” • AA (Int. Conf. on Autonomous Agents) • 1998 • JAMAS (Journal of Autonomous Multi-Agent Systems) • 2002 • AAMAS (joint of ICMAS, AA & ATAL)

    15. Agent-Enabled System Architecture (Bradshaw, 1997)

    16. Design Issues (1) • Agent—User • Control -who takes control? • Understanding -how to make agents understandable /trustworthy? • Personification -how to present agent? • Distraction -how to minimize distraction? • User modelling -how to model emotion, intention, social behaviours, etc? • Privacy -how to protect privacy?

    17. Design Issues (2) • Agent--Other Agents • How to find other agents? • How to model other agents? • How to communicate with other agents (language, ontology)? • KQML (Finn & Labrou, 1997) & KIF (Genesereth & Fikes, 1992), FIPA ACL & FIPA SL • Ontology (Gruber, 1993) • How to cooperate/negotiate with other agents? • AI techniques (inference, planning, logic, constraint satisfaction)

    18. Design Issues (3) • Agent—Legacy software Three possible solutions (Genesereth & Ketchpel, 1994) • Rewriting the software • Transducer (interpreter) • Wrapper Another suggestion: Web Services • Scalability, stability and performance

    19. Tools and Platforms • OOA (Open Agent Architecture) by SRI International’s AI Center http://www.ai.sri.com/~oaa/ • JATLite (Java Agent Template, Lite) by Center for Design Research, Stanford Univ. http://cdr.stanford.edu/ABE/JavaAgent.html • ZEUS by British Telecommunications (BT) http://more.btexact.com/projects/agents/zeus/index.htm • JADE (Java Agent Development Framework) by Telecom Lab, Italia (TILAB) http://sharon.cselt.it/projects/jade/

    20. Workflow management Network management Air-traffic control Business process engineering Entertainment Personal assistants E-mail filtering Information management Data mining E-commerce Education Agent Applications

    21. Agents in Education

    22. Outline • Roles of agents from CAI to CSCL • Spectrum of educational systems • Positioning the agent component • Some examples • Intelligent tutoring systems • Domain-oriented design environments • Collaborative learning environments

    23. Computer Supported Collaborative Learning On-Line Resources (CD-ROM, WWW) Instructors On-Line Facilitators Super- users Learning on Demand Problem-Based Intelligent Tutoring Systems Learning Network of Instructors Colleagues Critiquing Systems (Automated) Coaching Micro-worlds Roles for agents: CAI to CSCL Computer-Assisted Instruction

    24. Educational systems paradigms • Computer Aided Instruction (CAI) • Intelligent Tutoring Systems (ITS) • Microworlds (MW) • Guided Discovery and Critiquing (GDC) • Knowledge Building Environments (CSCL) • Note: the paradigms are continually evolving and mutually influencing each other

    25. Spectrum system-user control • Positioning educational systems along a line of increasing order of their enabling user control, or alternatively allowing predefined instructional sequences • On the left: Instructional systems and ITS • One the right: open (”constructionist” and “constructivist”) learning environments • In between: Guided discovery and critiquing

    26. Distribution of (human-computer) control in educational systems CAI ITS GDC MW CSCL On left: Systems supporting well-defined instruction On right: Systems allowing user-defined interaction Note ! Comparison leaves out important variables ..

    27. Omission 1: number of users • First and second generation systems (CAI; ITS; GDC; MW) were primarily built for single users • The field of CSCW had not yet matured • Third generation systems (CSCL) are multiuser, since the focus now is on how to support collaborative learning

    28. Omission 2: type of artefact • First generation systems (CAI, ITS) tended to favour behavioural and mental aspects of learning (psychology) • Second generation systems (MW, GDC) put more emphasis on the physical aspects of learning (”learning by doing”) • Third generation systems (CSCL) tend to favour conceptual aspects of learning (learning to reason)

    29. Positioning the agent component • Agents can support part of the “system functionality” of a learning environment • Agents can also support part of the “user work” in a learning environment • Agents are positioned somewhere in-between hard coded (programmed) functionality and informal rules to guide user interaction and social conduct

    30. 1st Gen.: Tutors and Coaches • “Expert systems” for teaching and learning • Works best in well-defined domains (e.g. physics, computer programming) • Instructional planner • High-level goals and strategies • Individual student model • Many opportunities for agent (coach) interaction • Few opportunities for “learning by discovery”

    31. Grace: A Tutor for Cobol

    32. Andes: ITS with coaching

    33. Microworlds • Microworlds are not directly associated with agents, but have been important inspiration and platform for agent integration • Microworlds define domain-specific “worlds” users can freely explore to build artefacts of the own creation and learn as a “by-product” • Microworlds support constructionist learning or “learning by doing” (Harel & Papert, 1991)

    34. 2nd gen.: Guided discovery • Combining open learning environments with teacher guidance • Conceptual models and well-defined tasks to be discovered • Learners construct knowledge themselves by being engaged; philosophy is “just try it” • Teacher as facilitator “standing behind the shoulder” to encourage, challenge, and direct • Goal of teacher: stimulate students’ critical thinking skills

    35. 2nd gen.: Critiquing systems • Computational approach to guided discovery by integrating Microworlds with ITS rule-bases • Conceptual foundation in Donald Schön’s theory of expert everyday knowledge building: • Learning by doing (“physical” activity) • Learning by reflection (mental/conceptual aspect) • Application domains • Learning-on-demand • Design (architecture, network design, lunar habitat) • The computer-based critic systems are referred to as Integrated Design Environments

    36. Janus: The “doing” face

    37. Janus: The argumentation face

    38. Janus: Critics • Critics are intelligent interface agents • Linking “doing” and conceptualization, and correspondingly construction and argumentation • “Breakdowns” in construction may create new (unanticipated) learning opportunities • Making students pause and reflect • Abstract concepts are presented to students in a context when it is meaningful for them

    39. Comparing Tutors & Critics

    40. CoPAS: Simulating CSCL Agents

    41. Pedagogical Agents in Distributed Collaborative Learning Environments

    42. General definition • Pedagogical agent definition adopted from Johnson et al., (1999): “Pedagogical agents can be autonomous and/or interface agents that support human learning in the context of an interactive learning environment.”

    43. 3rd Gen.: Agents for CSCL • Agents as online facilitators in CSCW and CSCL environments • Pedagogical agents operate in the context of collaboration systems, such as groupware • Facilitating communication and coordination among collaborating peers • In our case: facilitating knowledge building and progressive inquiry (to be discussed)

    44. CSCW • Computer Supported Cooperative Work • CS-part focus on groupware, knowledge management & other collaboration systems • Technical issues include: distribution, document sharing, coordination, awareness • CW-part address social aspects of using the systems by empirical (usually field) studies • Also important are conceptual approaches, such as coordination theory and languages

    45. CSCL • Computer Supported Collaborative Learning • Educational CSCW applications for teaching and learning (school or workplace) • Broad and multifaceted conceptual foundation, which includes : • Socio-cultural perspectives • Situated learning • Distributed cognition

    46. Knowledge Building • A technique for collaborative learning • Students learn by “talking” (reasoning aloud) for the purpose of developing explanations • Formulating research questions, answering them independently, and finding support • Structured as a discussion with message categories modelled after scientific discourse • Computer supported by environments such as • CSILE and Knowledge Forum • Fle3

    47. Agents as facilitators • Monitor participation in KB discussion and provide advice to the participants • Encourage non-active students to be more active • Suggest what messages to reply to and who should be doing so • Suggest what category to choose for the next message to be posted • Suggest when messages do not follow the scientific method of knowledge building, etc.

    48. Conceptual design • Agents for CSCL can be designed from different perspectives: • Technological-based design • Theory-based design • Empirical-based design • These perspectives can be combined

    49. Technological-based design • Build agent technology from • Scratch (e.g. java, python) • Existing agent development environments, such as JADE, ZEUS and Microsoft Agents • Build agent systems by integrating them with existing educational systems • Open source systems (e.g. Fle3) • Other systems (e.g. Teamwave Workplace) • Combinations of the above is possible

    50. Theory-based design • Coordination theory • Conceptual models of collaboration • Patterns of collaborative interaction, such as “genuine interdependence”: • sharing information • mindful engagement • joint construction of ideas