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these design decisions should also be based on the successes of current adaptive
computer-based learning environments for well-structured tasks (e.g., Koedinger &
Corbett, 2006; Graesser, Jeon, & Dufty, 2008; VanLehn et al., 2007; Woolf, 2009),
technological limitations in assessing learning of challenging and conceptually rich,
ill-structured topics (e.g., Brusilovsky, 2001; Jacobson, 2008; Azevedo, 2008), and
conceptual issues regarding what, when, and how to model certain key self-regulated
learning processes in hypermedia environments (Azevedo, 2002). Current computa-
tional methods from AI and educational data mining (e.g., Leelawong & Biswas,
2008; Schwartz et al., 2009) need to be explored and tested to build a system
designed to detect, trace, and model learners' deployment of self-regulated pro-
cesses. Other challenges associated with having the system detect the qualitative
shifts in students' mental models of the topic must be circumvented by using a com-
bination of embedded testing, frequent quizzing about sections of the content, and
probing for comprehension.
As for SRL processes, our data show that learners are using mainly ineffective
strategies and they tend to use up to 45 min of the 60 min session using these pro-
cesses. In contrast, the same data show learners use key metacognitive processes
but they may last a short period of time (up to 9 seconds). The challenge for an
adaptive MetaTutor is for it to be sensitive enough to detect the deployment of
these processes and to accurately classify them. Aggregate data from state-transition
matrices are also key in forming the subsequent instructional decision made by the
system. All this information would then have to be fed to the system's students and
instructional modules in order to make decisions regarding macro- and microlevel
scaffolding and tailor feedback messages to the learner. Associated concerns include
keeping a running model of the deployment of SRL processes (including the level of
granularity, frequency, and valence; e.g., monitoring, JOL, and JOL-) and evolving
understanding of the content and other learning measures. This history would be
necessary to make inferences about the quality of students' evolving mental models
and the quality of the SRL processes.
To be most effective in fostering SRL, adaptive hypermedia learning environ-
ments must have the capacity to both scaffold effective SRL and provide timely
and appropriate feedback. In this section we focus on two specific and important
modules for an adaptive MetaTutor that provide these critical components.
Scaffolding module
. Scaffolding is an important step in facilitating students' con-
ceptual understanding of a topic and the deployment of SRL processes (Azevedo &
Hadwin, 2005; Pea, 2004; Puntabmbekar & Hubscher, 2005). Critical aspects
include the agents' ability to provide different types of scaffolding depending on
the students' current level of conceptual understanding in relation to the amount
of time left in a learning session, and also their navigation paths and whether they
have skipped relevant pages and diagrams related to either their current subgoal
or the overall learning goal for the session. In addition, we need to figure in how
much scaffolding students may have already received and whether it was effec-
tive in facilitating their mastery of the content. The proposed adaptive MetaTutor
may start by providing generic scaffolding that binds specific content to specific
SRL processes (e.g., intro to any section of content is prompted by scaffolding to
