Teachable agents: Difference between revisions
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{{under construction}} | |||
==Definition== | ==Definition== | ||
[[ | ''Intelligent agents'' within computer-based environments are {{quotation | autonomous entities that can exist in complex, dynamic, and open environments}} (Maes, 1997). Agents can possess distinct behaviours and goals that can be defined and adapted by the system or the user as the agent acts within its environment. | ||
'''Teachable agents''' in education are pedagogical agents and intelligent agents at once that through programming can be 'taught' to perform certain tasks within simulation-based environments to explore and solve problems. It is believed that by allowing students to program computer agents students will benefit from the effects of [[learning by teaching]], [[discovery learning]] and more generally, [[project-oriented learning]]. | |||
{{quotationbox| | |||
Programming intelligent agents requires several important processes: | |||
# defining what the agent needs to know | |||
# defining a representation of this knowledge | |||
# and programming this knowledge into the agent. | |||
This process of evaluating the needs of the agent to perform a task can lead to deeper understanding of the domain the agent needs to know about.}} (Brophy et al. 1998). | |||
See also [[logo]], [[microworlds]], [[simulations]]. | |||
Swartz & Blair describe four features of teachable agents that enable students to take advantage of the inherent [[learning by teaching]] scenario: | |||
# explicit well-structured shared visual representations, | |||
# independent performance of the agent | |||
# the agent’s ability to model productive learner behavior | |||
# embedding the agent in environments that support teaching. | |||
==Use in ID== | ==Use in ID== | ||
A designer must evaluate how an agent acts and how it interacts with other agents (dependencies) in the simulation environment. The designer must translate their hypothesis into an agent's actions/interactions using a visual programming environment to program sets of propositions defining the behavior of the agents. To create these propositions, users identify conditions when specific actions should be taken by agents, and they can specify actions using simple click and drag techniques. Then the designer can evaluate the design by "running" the simulation to evaluate whether they correctly modelled the desired outcome. Used as a learning environment the designers are learners trying to model the dynamics of a complex situations. This inquiry process of evaluation, implement and test provides an excellent learning opportunity that requires very little programming experience. | A designer must evaluate how an agent acts and how it interacts with other agents (dependencies) in the simulation environment. The designer must translate their hypothesis into an agent's actions/interactions using a visual programming environment to program sets of propositions defining the behavior of the agents. To create these propositions, users identify conditions when specific actions should be taken by agents, and they can specify actions using simple click and drag techniques. Then the designer can evaluate the design by "running" the simulation to evaluate whether they correctly modelled the desired outcome. Used as a learning environment the designers are learners trying to model the dynamics of a complex situations. This inquiry process of evaluation, implement and test provides an excellent learning opportunity that requires very little programming experience. | ||
==Examples== | |||
[http://www.teachableagents.org/betty.php BETTY] | |||
==References== | |||
* Blair, K., Schwartz, D., Biswas, G. & Leelawong, K. (2006). Pedagogical Agents for Learning by Teaching: Teachable Agents, Educational Technology & Society, Special Issue on Pedagogical Agents.[http://www.teachableagents.org/papers/Final-edtechTA.pdf PDF] | |||
* Brophy S., Schwartz, D., Biswas, G., & Bransford, J. (1998, August). Learning Through Programmable Agents. Presented at Workshop on Pedagogical Agents, ITS '98, San Antonio, TX, August 1998. | |||
Revision as of 12:41, 22 December 2006
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Definition
Intelligent agents within computer-based environments are “autonomous entities that can exist in complex, dynamic, and open environments” (Maes, 1997). Agents can possess distinct behaviours and goals that can be defined and adapted by the system or the user as the agent acts within its environment.
Teachable agents in education are pedagogical agents and intelligent agents at once that through programming can be 'taught' to perform certain tasks within simulation-based environments to explore and solve problems. It is believed that by allowing students to program computer agents students will benefit from the effects of learning by teaching, discovery learning and more generally, project-oriented learning.
Programming intelligent agents requires several important processes:
- defining what the agent needs to know
- defining a representation of this knowledge
- and programming this knowledge into the agent.
(Brophy et al. 1998).
See also logo, microworlds, simulations.
Swartz & Blair describe four features of teachable agents that enable students to take advantage of the inherent learning by teaching scenario:
- explicit well-structured shared visual representations,
- independent performance of the agent
- the agent’s ability to model productive learner behavior
- embedding the agent in environments that support teaching.
Use in ID
A designer must evaluate how an agent acts and how it interacts with other agents (dependencies) in the simulation environment. The designer must translate their hypothesis into an agent's actions/interactions using a visual programming environment to program sets of propositions defining the behavior of the agents. To create these propositions, users identify conditions when specific actions should be taken by agents, and they can specify actions using simple click and drag techniques. Then the designer can evaluate the design by "running" the simulation to evaluate whether they correctly modelled the desired outcome. Used as a learning environment the designers are learners trying to model the dynamics of a complex situations. This inquiry process of evaluation, implement and test provides an excellent learning opportunity that requires very little programming experience.
Examples
References
- Blair, K., Schwartz, D., Biswas, G. & Leelawong, K. (2006). Pedagogical Agents for Learning by Teaching: Teachable Agents, Educational Technology & Society, Special Issue on Pedagogical Agents.PDF
- Brophy S., Schwartz, D., Biswas, G., & Bransford, J. (1998, August). Learning Through Programmable Agents. Presented at Workshop on Pedagogical Agents, ITS '98, San Antonio, TX, August 1998.