Information Technology Reference
In-Depth Information
only when all skills in a section are mastered to a criterion. Thus it takes on the
burden of judicious item selection and self-testing, which learners may not opti-
mize on their own. Classroom experiments have found evidence that Cognitive
Tutor enhances student learning compared to traditional teaching and study methods
(Ritter, Anderson, Koedinger, & Corbett, 2007).
Training Metacognitive Control
A potential consequence of the approaches outlined above is to promote learning at
the cost of developing improved metacognitive control skills. Such skills can be cru-
cial for self-regulated lifelong learning beyond the structured learning environment.
Note, however, that whether control skills really do need to be learned depends
on one's goals and contexts; some control tasks may be best relinquished to the
environment. Nevertheless, another approach is for information technology to guide
learners toward improved metacognitive control: that is, to help learners learn how
to learn.
The Cognitive Tutor program has been adapted to model, and thus also to tutor,
certain metacognitive control behaviors (Koedinger, Aleven, Roll, & Baker, 2009).
For example, Roll, Aleven, McLaren, and Koedinger (2007) sought to improve
strategic help-seeking behavior of learners when using the built-in help functions
of the Geometry Cognitive Tutor. Learners using this program had been observed to
engage in maladaptive behaviors such as not seeking help at all (even after making
multiple errors on the same type of problem) or quickly using the help functions
to retrieve a complete answer to the current problem rather than only seeking help
when they made errors or got stuck. A cognitive model of help-seeking was built,
which encompassed both maladaptive and adaptive behaviors, and this was used to
give learners immediate feedback when they used the help functions in suboptimal
ways. There was some improvement in help-seeking under such tutelage; how-
ever, it is unclear whether learners truly developed improved skills or were merely
complying with the metacognitive advice provided.
Winne and Nesbit (2009) outline important characteristics of software-enabled
attempts to scaffold improved metacognition. They point out that, in addition
to suggesting normatively optimal learning behaviors, educational software that
logs learners' interactions (e.g., their program, gStudy) can be adapted to also
present graphical representations of the strategies that learners have used and how
those strategies have influenced performance. This would enable, and perhaps
even motivate, learners to assess for themselves the effectiveness of their control
processes—an important step in improving metacognitive control, as suggested by
the data from deWinstanley and Bjork (2004).
Development of software to foster improved metacognitive control still has a long
way to go (cf. Azevedo, 2007). But given that so much learning takes place outside
of structured learning environments, there is much to be gained from leveraging
technology to increase our self-regulated learning skills.
