While the benefits of self-directed learning are widely acknowledged, the reasons why a sense of control leads to better acquisition of material are poorly understood.
Some researchers have highlighted the motivational component of self-directed learning, arguing that this kind of learning is effective because it makes students more willing and more motivated to learn. But few researchers have examined how self-directed learning might influence cognitive processes, such as those involved in attention and memory.
But we're not always optimal self-directed learners. The many cognitive biases and heuristics that we rely on to help us make decisions can also influence what information we pay attention to and, ultimately, learn.
Gureckis and Markant note that computational models commonly used in machine learning research can provide a framework for studying how people evaluate different sources of information and decide about the information they seek out and attend to. Work in machine learning can also help identify the benefits -- and weaknesses -- of independent exploration and the situations in which such exploration will confer the greatest benefit for learners.
Drawing together research from cognitive and computational perspectives will provide researchers with a better understanding of the processes that underlie self-directed learning and can help bridge the gap between basic cognitive research and applied educational research. Gureckis and Markant hope that this integration will help researchers to develop assistive training methods that can be used to tailor learning experiences that account for the specific demands of the situation and characteristics of the individual learner.
The above story is reprinted from materials provided by Association for Psychological Science.
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T. M. Gureckis, D. B. Markant. Self-Directed Learning: A Cognitive and Computational Perspective. Perspectives on Psychological Science, 2012; 7 (5): 464 DOI: 10.1177/1745691612454304
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