Metacognitively Guided Study in the Region of Proximal Learning
- Cite this paper as:
- Metcalfe J. (2011) Metacognitively Guided Study in the Region of Proximal Learning. In: Biswas G., Bull S., Kay J., Mitrovic A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, vol 6738. Springer, Berlin, Heidelberg
Empirical data on people’s metacognitively guided study time allocation– data that resulted in the proposal that metacognitively astute people attempt to study in their own Region of Proximal Learning (RPL)– will be reviewed. First, the most straightforward study-choice strategy that metacognitively sophisticated learners can use is to decline to study items that they know they already know. If an item has already been mastered, then further study is unnecessary. All theories, including the RPL model, agree on this strategy, and many, but not all, people use it. Its effective use depends on refined metaknowledge concerning the boundary between what is known and what is not known, as well as the implementation of a rule to decline study of items for which judgments of learning are very high. There are many situations in which people are overconfident, and if they are, they may miss studying items that are almost, but not quite, mastered. These items would yield excellent learning results with just a small amount of study, and so this failure to study almost-learned items has detrimental results. Data will be presented showing that young middle childhood children (7 to 9 year olds) tend not only to be overconfident-thinking they know things when they do not-but also to have an implementation deficit in using metacognitively-based item-choice strategies. One result is that many children at this age fail to use even this most obvious study strategy, even though it will be shown that it would benefit their learning. When the computer implements this learning strategy for the children their later performance improves. Second, with already-learned item eliminated, metacognitively sophisticated learners selectively study the items that are closest to being learned first, before turning to more distal items that will require more time and effort. This, as well as studying the materials that are within their cognitive reach, rather than items that are too difficult, is a strategy that conforms to the so-called “Goldilocks principle”-not too easy and not too difficult but just right. As will be detailed, while college-aged learners use this strategy, older middle childhood children (aged 9-11) do not. Children at this age are not without strategies, however. They do use the strategy of declining the easiest items (including the already-learned items). However, the older middle childhood children overgeneralize this strategy to selectively prefer the most difficult items. While their learning is negatively impacted by this, it is improved if the computer implements the Goldilocks principle on their behalf. Third, people use a stop rule that depends upon a dynamic metacognitive assessment of their own rate of learning. They discontinue study when they perceive that continued efforts are yielding little learning return. This stop rule predicts that people will stop studying easy items when they are fully learned them (and the learning rate has reached an asymptote on ceiling). Study will also stop, however, if the item is too difficult to allow noticeable learning. This strategy keeps people from being trapped in laboring on very difficult items in vain. Finally, the value that each item is assigned on a criterion test, if known during study, influences which items metacognitively sophisticated people choose to study and for how long they continue to study them. Items worth many points on a test will be studied sooner, longer and more often, than items worth few points. But not all learners use these strategies to their advantage. To effectively use the strategies that the Region of Proximal Learning framework indicates are effective, the learners must both have adequate metacognitive knowledge and also exhibit good implementation skills. Both metaknowledge and implementation skills vary across people. Age differences, motivational style differences, and metacognitive expertise differences can result in strategies that vary considerably, and which can result in sizable differences in the effectiveness with which the individual is able obtain his or her learning goals.