Experiencias de coordinación con otros colectivos

The previous sections described some of the available group oriented technologies for helping team working in business organisations and in higher education. In this section a brief history of intelligent tutoring and online support for student team working is outlined, some of which is being developed with AI (Artificial Intelligence) principles, finally, the concept of software agent technology is introduced, which is based on AI principles.

Artificial intelligence has been the Holy Grail of computing for several decades, the possibility of automating activities that require some human intelligence, so that results are more consistent and we can spread expertise wider. Except within a few small domains, such as Eliza or geological surveys, the emergence of practical applications of the resulting Knowledge-Based Systems (KBS) or Expert Systems has been disappointing. These systems have been successful, but have cost a lot to

produce and only operate within a small area of expertise. However, there has been some success with Intelligent Tutoring systems, which are capable of learning something about the user, in order to provide material in an appropriate format to be best suited for an individual learner (Farr and Psotka 1992; Hwang 2003; Negoita and Pritchard 2004).

Intelligent tutoring systems (ITS) aim to provide user specific instruction to learners, based on their preferences and past experience and performance. They have developed from computer aided instruction/learning systems (CAI or CAL), which were programmed learning systems leading learners through a series of pre- determined learning activities (Farr and Psotka 1992). Expert systems can add some adaptation to learners, by analysing learner experience, ability and preference, and selecting appropriate format of teaching material. Such a system learns from the user about their previous knowledge and preferences, using a rule-based system, applies this knowledge to a search for appropriately formatted material to present next, sometimes also known as programmed learning, e.g. (Hatzilygeroudis and Prentzas 2004), or WITNeSS, which applies fuzzy logic to reason with the uncertain data characteristic of the complex nature of student support (Negoita and Pritchard 2004).

These systems have been designed for individual learning, and are useful for providing online tutorials for on demand learning. However, learning through collaboration within small teams of learners, building up knowledge between them as they work on projects, with advice and help from a tutor, require a different design of intelligent tutoring system, which would be more complex (Strijbos et al. 2004). In the next section software agent technology, and multiple agent systems are considered, as a possible support tool for students in teams.

4.4.1. Intelligent software agents for learning

The emergence of intelligent software agents was proposed to be an “acceptable” form of AI that would more closely mirror the ways in which humans work, and would be more usable. The name “agent” was chosen for its definition as:

“something that acts for or on behalf of someone by their authority”.

“An agent is a self-contained, concurrently executing software process, which encapsulates the current state in terms of knowledge, and is able to communicate with other agents through message passing” (Wooldridge 1995).

The concept of an agent originates from human agents that provide services, such as estate agents and travel agents. These agents have specialist skills, access to relevant information, contacts for obtaining information and are focused on a particular task. In the same way software agents are autonomous systems that work on behalf of a user. They exhibit the ability to recognise what the user needs to accomplish and reacts to the user’s input.

Over the past decade there has been considerable debate over what a software agent actually is (Franklin and Graesser 1996). A working outline is that an agent should be:

• autonomous, so that it operates without much human intervention,

• social, in that they interact with other agents and humans (who may also be called agents),

• reactive, and able to react to a stimulus from the environment and respond to changes,

• proactive, not only responding but also taking the initiative (Knapik and Johnson 1998).

These qualities certainly denote an agent as having intelligence in certain areas, and using it appropriately. However, the present state of research is such that there is no way that agents can be developed to operate in broad domain areas, they would be much too big, and would probably grow out of hand, as they learn new information (Bradshaw 1997). Agent systems are likely to remain within narrow domains for the foreseeable future, but in certain circumstances can be designed to be more reactive than a rule based expert system (O'Leary 1998). Some notable applications of agent technology are in knowledge management (Ferneley and Berney 1999) and Internet searchbots (Lieberman 1997), e.g. Phibot (Henninger 2002) or MySpiders (Pant and Menczer 2002), which all facilitate knowledge sharing and searching.

The influence of robotics in software agent technology is evident as developers choose to include some form of character to the agent, similar to the animated paperclip of Microsoft word, also known as an avatar, as a means of personalising the agent to the user. Examples can be found in a workshop proceedings edited by Aylett (2001).

A software agent may operate in isolation, working on behalf of an individual, but their power derives from an ability to communicate with other agents to fulfil tasks they would be unable to complete alone (Ferber 1999). Several agents linked together, all playing their part in a particular task are called multi-agent systems. These multi- agent systems are the main thrust of much recent research, and have become possible because of the massive global infrastructure of networks now available, embodied in the Internet (Aldea et al. 2004).

Lesser (1999) suggests that the power of several agents is greater than the power of each individual agent, so that each agent could be a local knowledge based system with a specific narrow field of expertise, and by combining several agents together, each field of narrow expertise is combined to solve more complex problems. The power of their action lies in their ability to communicate between individual agents, by broadcasting messages or specifying recipients of messages (Soller and Busetta 2003).

Given the working outline of an agent, provided above, it would suggest that a software agent system could well be applied to the area of online learning, as observer, information processor or proposer (Boy 1997). There have been some developments of software agent systems for learning, such as enabling students to navigate through virtual environments (Nijholt 2001), EduAgents (Hietala and Niemirepo 1996), and I-Help to form a network of students who are willing to help each other (Vassileva and Deters 2001). Research is ongoing into the benefits of using software agents for learners, such as the ADE Project, which has combined course management on the server side with intelligent tutoring on the client side to support individual learners by helping them to connect with tutors and other students (Johnson and Shaw 1997). None of these are aimed at team project working, but demonstrate the potential of software agents for providing tailored help to students.

If an agent can support an individual learner, then several software agents linked together as a multi-agent system, can model the types of connections that exist between several learners linked together, as in a team. A multi-agent system could enable sharing between the individual team members that each agent is working on behalf of, e.g. in learning about community care (Beer et al. 2003). At a conference keynote, Corkill (2003) outlined the future potential for multi-agent systems in learning, supporting the possible benefit of this research to unifying a team of learners (Whatley et al. 2001). One example of a multi-agent system for supporting team working is I-Minds (Soh 2004), which works on assessing team working.

Although there has been considerable development of technology to provide support for learning and for team working, most solutions support the task roles of team projects, and there are no applications specifically for support of student team projects. Software agent technology has much potential for personalised individual learning, but also in combination as a multi-agent system, for supporting teams of learners. Providing technology tools to help communication is a growth area, but as will be apparent from the next section, users can vary in their acceptance of tools, either through a reluctance to learn how to use a tool, through an overload of tools, or simply not finding the tool as useful as developers intended. A recent study suggests that there is greater acceptance of a text only agent system over agents with an avatar, because it is more important that the system is well designed so that it does not detract from the learning (Hershet Dirkin et al. 2005). The affordance of technology tools for learning is discussed next.