A Region of Proximal Learning model of study time allocation☆
Section snippets
The Region of Proximal Learning model
In the Region of Proximal Learning model, there are two separable components to human study time allocation: choice and perseverance. It is important to distinguish between them insofar as they are guided by different metacognitive markers. In the choice stage people decide which items they will study, and in which order. In the perseverance stage the question is: how long, once they have started studying a particular item should they continue before switching to another item? Notice that the
Do people choose to decline study for the items that they know they know?
A number of studies, several going back three decades (Atkinson, 1972, Masur et al., 1973), affirm people’s capability to decline study of already-known items. We have conducted several demonstration experiments focusing on this topic that will be presented below. Masur et al. (1973) found convincing support for a choice strategy in which the already-known items are spurned. In their study with children of various ages, even very young children showed a tendency to choose the items they did not
Perseverance
This final section of the paper addresses the issue of how long people persist in studying. The Discrepancy Reduction model’s answer to that question, as given by Dunlosky and Thiede (1998), is:
If the perceived degree of learning has not reached the norm of study, more study time will be allocated to the item. Put differently, this discrepancy-reduction model of self-paced study is based on a negative feedback loop in which study of an item is stopped when the error between the perceived state
Conclusion
A strong negative correlation between JOLs and study time has often been thought to indicate that people are using their metacognitions to allocate their study time appropriately. The sometimes rather low values observed have been taken as an indication of poor metacognition or of poor control. In contrast, the Region of Proximal Learning model, presented here, suggests that a strong negative correlation should not be taken as an index of the goodness of metacognitively guided control. One
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This research was supported by National Institute of Mental Health Grant MH60637. We thank Lisa Son and Bridgid Finn for their help and comments.