Permutations of Control: Cognitive Considerations for Agent-Based Learning Environments

By Baylor, Amy L. | Journal of Interactive Learning Research, Winter 2001 | Go to article overview

Permutations of Control: Cognitive Considerations for Agent-Based Learning Environments


Baylor, Amy L., Journal of Interactive Learning Research


While there has been a significant amount of research on technical issues regarding the development of agent-based learning environments (e.g., see the special issue of Journal of Interactive Learning Research, 10(3/4)), there is less information regarding cognitive foundations for these environments. The management of control is a prime issue with agent-based computer environments given the relative independence and autonomy of the agent from other system components. This article presents four dimensions of control that should be considered in designing agent-based learning environments, with the Multiple Intelligent Mentors Instructing Collaboratively (MIMIC) system as an example. The first dimension of control involves instantiating the instructional purpose of the environment on a constructivist (high learner control) to instructivist (high program/agent control) continuum. The second dimension entails managing feedback involving issues of type, timing, amount, explicitness, and potential for learner choo ice. Third, agent versus learner control is further defined through the desired relationship of the learner to agent(s) (e.g., agent as learning companion, agent as mentor, multiple pedagogical agents or agent as personal assistant). Fourth, to be instructionally effective, the agent(s) must assert enough control so that the learner develops confidence in the agent(s) in terms of believability, competence, and trust. Overall, an array of possible permutations of system versus learner control must be carefully considered.

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The distinguishing characteristics of intelligent agents may be in their capacity to independently manage cooperation among distributed programs and/or other agents, to provide intelligent assistance to learners when traditional interfaces are insufficient, and to enable more humanlike interaction (Bradshaw, 1997); however, from an educational vantage point, a better description might be that intelligent agents are computer programs that simulate a human relationship by doing something that another person could otherwise do for you (Seiker, 1994). As described in a recent special issue of the Journal of Interactive Learning Research, 10(3/4)), intelligent agent technology provides a range of possibilities for computer-based learning environments. However, as the special issue illustrates, research in intelligent agent-based learning environments tends to focus on system development principles rather than on examining the cognitive foundations of such environments. Specifically, articles were included in three areas as described by the editors (Arroyo & Kommers, 1999, p. 235): (a) historical development of intelligent tutoring and support systems; (b) agent paradigms and agent-based user support systems; and (c) tendencies in agent development and application, including agents as guides, information assistants, architectural solutions, help systems, and as simulation agents in virtual and interactive learning environments. To add to the mix of agent research, there are a variety of programs that are called "agents," as listed by Bradshaw (1997):

1. those that can be scheduled in advance to perform tasks on a remote machine,

2. accomplishing low-level computing tasks while being instructed in a higher-level of programming language or script,

3. abstracting out or encapsulating the details of differences between information sources or computing services,

4. implementing a primitive or aggregate "cognitive function,"

5. manifesting characteristics of distributed intelligence,

6. serving as a mediating role among people and programs,

7. performing the role of an "intelligent assistant,"

8. migrating in a self-directed way from computer to computer,

9. presenting themselves to users as believable characters,

10. speaking an agent communication language, and

11. they are viewed by users as manifesting intentionality and other aspects of "mental state. …

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