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The SRL processes deployed were handwritten on their navigational paths and
presented in Fig. 11.4c and d. In Fig. 11.4c and d, the
x
-axis represents time (in
minutes) within the learning session and the
y
-axis represents pages of content
(and their corresponding titles). There are several key observations to highlight
in terms of keeping with our goal of extracting information for the design of the
adaptive MetaTutor. First, there is more complexity in the navigational paths and
deployment of SRL processes as seen in the number of processes handwritten in
the figures. Second, 70% of the low performer's SRL moves were coded as taking
notes while the high performer only used 39% of his processes for taking notes.
Third, one can infer (from the “space” between moves) that the low performer spent
more time acquiring knowledge (reading the science content) from the environment
while the high performer spent less time reading throughout the session. Fourth, the
high performer used a wider variety of SRL processes compared to the low per-
former. A related issue is the nonstrategic move by the low performer to create a
new subgoal near the end of the learning session. However, the high performer is
more strategic in his self-regulatory behavior throughout the learning session. For
example, he engages in what can best be characterized as “time-dependent SRL
cycles.” These cycles involve creating subgoals, previewing the content, acquiring
knowledge from the multiple representations, taking notes, reading notes, evaluat-
ing content, activating prior knowledge, and periodically monitoring understanding
of the topic.
Overall, these data show the complex nature of the SRL processes during learn-
ing with MetaTutor. We have used quantitative and qualitative methods to converge
process and product data to understand the nature of learning outcomes and the
deployment of SRL processes. The data will be used to design an adaptive version
of MetaTutor that is capable of providing the adaptive scaffolding necessary to foster
students' learning and use of key SRL processes. It is extremely challenging to think
about how to build an adaptive MetaTutor system designed to detect, trace, model,
and foster SRL about complex and challenging science topics. The next section will
address these challenges in turn.
Implications for the Design of an Adaptive MetaTutor
In the next section, we highlight some general and specific design challenges that
need to be addressed in order to build an adaptive MetaTutor system.
General challenges.
Our results have implications for the design of the adap-
tive MetaTutor hypermedia environments intended to foster students' learning of
complex and challenging science topics. Given the effectiveness of adaptive scaf-
folding conditions in fostering students' mental model shifts, it would make sense
for a MetaCognitive tool such as MetaTutor to emulate the regulatory behaviors of
the human tutors. In order to facilitate students' understanding of challenging sci-
ence topics, the system would ideally need to dynamically modify its scaffolding
methods to foster the students' self-regulatory behavior during learning. However,
