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Interplay Between Metacognitive Monitoring and Control
In this section, we review evidence on the relationship between monitoring and
control. The effective control of learning behavior requires accurate assessments of
current states of knowledge (Benjamin, Bjork, & Schwartz, 1998; Benjamin, 2005;
Konopka & Benjamin, 2009), the current rate of learning (Metcalfe & Kornell,
2005), the effects of various stimulus factors on learning (Benjamin, 2003), the
effectiveness of competing strategies in promoting additional learning (Benjamin
& Bird, 2006; Son, 2004), the nature and payoff structure of the upcoming test
(Benjamin, 2003; Dunlosky & Thiede, 1998), and the exact form of the learning
function for the particular study material (Son & Sethi, 2006).
Metacognitive monitoring is theorized to directly impact control of learning
behavior. According to the “monitoring affects control” hypothesis (Nelson &
Leonesio, 1988), objective item difficulty influences a person's beliefs about item
difficulty, which in turn influence control processes such as study time allocation,
item selection, retrieval strategies, and output decisions.
Monitoring of Ongoing Learning
The most prominent example of monitoring affecting control comes from research
on self-pacing (i.e., allocation of study time). The
discrepancy reduction
model of
self-pacing (Thiede & Dunlosky, 1999) suggests that learners set a desired state
of learning, continuously monitor their current state of learning while studying, and
only stop studying when their current state meets or exceeds the desired state. As this
model predicts, learners usually do allocate more study time to material judged as
more difficult, across a wide range of circumstances—from children to older adults
(Dufresne &Kobasigawa, 1988, 1989; Kobasigawa &Metcalf-Haggert, 1993), from
recognition to free recall tasks (Belmont & Butterfield, 1971; Le Ny, Denheire, &
Taillanter, 1972; Zacks, 1969), and from simple to complex study materials (Baker
& Anderson, 1982; Maki & Serra, 1992; Son & Metcalfe, 2000).
Another position on how learners choose to self-pace learning of differentially
challenging materials is the
region of proximal learning
hypothesis, which posits
that learners preferentially allocate study time not to items that are furthest from
their current grasp (as specified by the discrepancy reduction model) but rather to
items that are just slightly beyond their current grasp. According to this hypoth-
esis, learners monitor their current
rate
of learning and continue to study items
until that rate drops below a pre-determined threshold. This contrasts with the
discrepancy reduction model, in which learners study until the item reaches a pre-
determined
level
of learning. Research on the influence of domain expertise and on
the influence of learning goals supports aspects of the region of proximal learning
hypothesis: experts allocate their study time to more difficult items than do novices,
and conditions inducing low performance goals (e.g., time pressure or penalties for
remembering too many items) lead learners to spend more time on easy items, aban-
doning the more difficult items (Thiede & Dunlosky, 1999; Son & Metcalfe, 2000).
