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preview, skimming the content, evaluating the content vis-à-vis the current sub-
goal, and then determining whether to pursue or abandon the content) versus a
fine-grained scaffolding that is time-sensitive and fosters qualitative changes in
conceptual understanding. This approach fits with extensive research on human
and computerized tutoring (Azevedo et al., 2007, 2008; Chi et al., 2004; Graesser
et al., 2008). One of the challenges for the adaptive MetaTutor will be to design
graduated scaffolding methods that fluctuate from ERL (i.e., a student observes
as the agent assumes instructional control and models a particular strategy or
metacognitive process to demonstrate its effectiveness) to fading all support once
the student has demonstrated mastery of the content. Our current data on SRL pro-
cesses show that learners are making FOK, JOL, and content evaluation (CE) more
often than any other metacognitive judgment. However, they are deploying these
processes very infrequently. Thus, agents could be designed to prompt students
to explicitly engage in these key metacognitive processes more frequently during
learning. Another level of scaffolding would involve coupling particular metacog-
nitive processes with optimal learning strategies. For example, if students articulate
that they do not understand a certain paragraph (i.e., JOL-), then a prompt to re-
read is ideal. In contrast, students who report that they understand a paragraph
(i.e., JOL+) should be prompted to continue reading the subsequent paragraph or
inspect the corresponding diagram.
Feedback module . Feedback is a critical component in learning (Koedinger &
Corbett, 2006; VanLehn et al., 2007). The issues around feedback include the tim-
ing and type of feedback. Timing is important because feedback should be provided
soon after one makes an incorrect inference or incorrectly summarizes text or a
diagram. The type of feedback is related to whether the agents provide knowledge
of results after a correct answer, inference, etc., or elaborative feedback, which is
difficult to create because it requires knowing the student's learning history and
therefore relies heavily on an accurate student model. For example, a key objective
of this project is to determine which and how many learner variables must be traced
for the system to accurately infer the students' needs for different types of feed-
back. We emphasize that feedback will also be provided for content understanding
and use of SRL processes. The data may show that a student is using ineffective
strategies, and therefore the agent may provide feedback by alerting the student to a
better learning strategy (e.g., summarizing a complex biological pathway instead of
copying it verbatim).
Summary
Learning with MetaCognitive tools involves the deployment of key SRL pro-
cesses. We have articulated and explicitly described the metaphor of computers as
MetaCognitive tools. We provided an overview of SRL and provided a description
of the importance of using SRL as a framework to understand the complex nature
of learning with MetaCognitive tools. We provided a synthesis of our previous work
and how it was used to design the MetaTutor system. We then provided preliminary
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