Аннотация
The idea that teaching others is a powerful way to learn is intuitively
compelling and supported in the research literature. We have developed
computer-based, domain-independent Teachable Agents that students
can teach using a visual representation. The students query their
agent to monitor their learning and problem solving behavior. This
motivates the students to learn more so they can teach their agent
to perform better. This paper presents a teachable agent called Betty's
Brain that combines learning by teaching with self-regulated learning
feedback to promote deep learning and understanding in science domains.
A study conducted in a 5th grade science classroom compared three
versions of the system: a version where the students were taught
by an agent, a baseline learning by teaching version, and a learning
by teaching version where students received feedback on self-regulated
learning strategies and some domain content. In the other two systems,
students received feedback primarily on domain content. Our results
indicate that all three groups showed learning gains during a main
study where students learnt about river ecosystems, but the two learning
by teaching groups performed better than the group that was taught.
These differences persisted in the transfer study, but the gap between
the baseline learning by teaching and self-regulated learning group
decreased. However, there are indications that self-regulated learning
feedback better prepared students to learn in new domains, even when
they no longer had access to the self-regulation environment.
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