Agent-oriented Knowledge Management in Learning Environments: A Peer-to-Peer Helpdesk Case Study Renata S. S. Guizzardi 1 Lora Aroyo 1 Gerd Wagner 2 1 University of Twente 2 Eindhoven University of Technology

Introduction Shift in the business model from a static to a dynamic model, built on top of continuously changing and knowledge-based organizations. This generates the need for creative and flexible professionals, who have the ability to cooperate and work in teams. The hierarchical educational model is not appropriate to educate these professionals. Collaborative methods can lead to better results.

Hierarchical vs. Collaborative Educational Model Hierarchical Model teachers detain knowledge while students consume knowledge instructional methods based on memorization of information and individual study Collaborative Model students and teachers are, at the same time, producers and consumers of knowledge instructional methods based on teams of students working together toward a common goal

Theories and Empirical Studies Supporting Collaborative Learning Collaborative Learning is based on constructivist theories. Piaget: peer collaboration leads to the development of logical thinking; Vygotsky: collaboration among individuals from different performance levels is essential for learning. Gokhale (1995): empirical study developed at the Western Illinois University. Journal of Technology Education. Vol 7, n. 1, Fall 1995.

Benefits of KM for Collaborative Learning KM motivates students to be active in the creation of knowledge, trough externalization of knowledge in terms of concrete artifacts. In learning environments, knowledge resources are naturally distributed. Therefore, a KM infrastructure is needed to manage all these resources.

Benefits of P2P for Collaborative Learning Requirements for a Collaborative Environment: Cooperative posture Cooperative-based environment structure Non-hierarchical method P2P reflects the need for a non-hierarchical structure among teachers and learners. There is no central authority. All peers are equals. While sharing knowledge, peers feel valued. This motivates collaboration.

Help&Learn H&L is a peer-to-peer architecture to support KM in collaborative learning environments. It provides an environment where teachers, students, and external participants can share knowledge. Knowledge is exchanged in the form of help items. E.g: what-is or how-to-do explanation, electronic documents, bibliographic reference, etc. H&L is based on a helpdesk metaphor: somebody asks for help (the helpee) and somebody provides help (the helper).

Help&Learn Agents Resource Manager Peer Assistant Broker Directory Server SIG Assistant HelpItem search on peer’s behalf HelpItem search on peer’s behalf Integrates existing knowledge repositories Registration of helpers and Explanation Case Helper indication SIG members can be helpers

P2P is… Scenario of use Anna’s Peer Assistant Request explanation Directory Server Check for existing explanation No explanation available what is p2p? Anna Broker Find best peer Best peer = Mark Mark’s Peer Assistant Request explanation Provide explanation

Agent-Object-Relationship Modeling Language An entity can be either an agent, an event, an action, a claim, a commitment or an object. Agents are the active entities while objects are the passive entities of the domain. Agents can be either artificial, human, or institutional. There are two types of AORML models: external and internal models.

Strengths of AORML It models agents and objects. It considers organizations and human actors of the domain as agents. It explicitly considers ‘mentalistic’ concepts such as commitments and claims, rights and obligations. It captures behavior of agents with the help of reactive rules. It is based on UML.

H&L Modeling: AORML Agent Diagram

Interaction Sequence Diagram – Helper Indication

Interaction Sequence Diagram – Provide Help

Interaction Pattern Diagram

Conclusions Help&Learn is a peer-to-peer agent-based architecture, aimed at creating a rich environment for knowledge sharing in learning contexts. Developing H&L we aim at contributing to the advance in the state of the art of collaborative learning environments. AORML has proven to be an effective modeling language, guiding us during the system’s analysis and design phases. During the experiments, we gathered feedback on how AORML can be extended.

Future Work We aim at investigating: the structuring and retrieval of the questions and answers present in the Explanation Case; the organization of the personal knowledge assets owned by each peer; the management of resource items by the Resource Manager; incorporating ontologies into the architecture, aiming at making knowledge explicit, supporting interaction among the system peers.

Renata – Lora – Gerd – AORML site –