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making FOK, JOL, and content evaluation more often than any other metacognitive
judgment. These judgments tend to last an average of 3-9 s. Such data demonstrate
the need for an adaptive MetaTutor, and will be useful in developing new modules.
The
log-file data
have also been mined to investigate the navigational paths and
explore the behavioral signatures of cognitive and metacognitive processing while
students use MetaTutor (Witherspoon, Azevedo, & Cai, 2009). It should be noted
that MetaTutor traces learners' behavior within the environment and logs every
learner interaction into a log file. These trace data are critical in identifying the
role and deployment of SRL processes during learners' navigation through the sci-
ence content. We conducted a cluster analysis using five navigational variables:
(1) percentage of 'linear forward' transitions (e.g., p. 1 to p. 2); (2) percentage of
'linear backward' transitions (e.g., p. 2 to p. 1); (3) percentage of 'nonlinear' tran-
sitions (e.g., p. 3 to p. 7); (4) percentage of category shifts (from one subheading to
another); and (5) percentage of image openings.
From this quantitative analysis, we found four major profiles of learners. One
group tended to remain on a linear path within the learning environment, pro-
gressing from one page to the next throughout the session (average of 90% linear
navigations). Another group demonstrated a large amount of nonlinear navigation
(average of 36% of the time), while a third group opened the image a majority of
the time (78% on average). The fourth group of learners was more balanced in their
navigation, navigating nonlinearly on average 18% of the time, and opening the
image accompanying the page of content 39% of the time. Further analysis revealed
that learners in this fourth, “balanced” group scored significantly higher on com-
posite learning outcome measures. An adaptive MetaTutor system should scaffold
balanced navigational behavior.
A qualitative analysis of MetaTutor's traces of learners' navigational paths dur-
ing each learning session was also performed (see Witherspoon et al., 2009 for a
complete analysis). The work here is emphasizing the need to examine how vari-
ous types of navigational paths are indicative (or not) of strategic behavior expected
from self-regulating learners (Winne, 2005). Figures 11.4a and b illustrate the nav-
igational paths of two learners from our dataset while they use MetaTutor to learn
about the circulatory system. In Fig. 11.4a and b, the
x
-axis represents move
x
and
the
y
-axis represents
x+
1. Figure 11.4a shows the path of a low performer (i.e.,
small pretest-posttest learning gains) while Fig. 11.4b illustrates the path of a high
performer. These figures highlight the qualitative differences between a low and
high performer in terms of the linear vs. complex navigational paths and reading
times. For example, the low performer tended to progress linearly through the con-
tent until he got to a key page (e.g., p. 16 on blood vessels) and decided to return to
a previous page. In contrast, the high performer's path is more complicated and is
more consistent with a strategic, self-regulated learner by the complexity shown in
Fig. 11.4b. This learner progresses linearly, at times makes strategic choices about
returning to previously visited pages, and deploys twice as many SRL processes
as the low-performing learner (i.e., 212 moves vs. 102 moves, respectively). This is
symbolically illustrated in the figures by the difference between the “space” between
the dots—i.e., more space between dots
=
longer reading times.
