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Why is self-study so effective?
Last reviewed: 01.07.2025
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In recent years, educators have begun to pay more attention to practical classes, laboratory experiments, and student research. This is explained by the fact that students learn material much better if they have the opportunity to control the intensity of their knowledge acquisition independently.
Self-directed learning has proven itself to be a positive phenomenon, but the reasons for this phenomenon are poorly understood.
Some scientists suggest that self-directed learning is effective due to a person's motivation to learn. However, experts do not have enough data to identify the relationship between self-directed learning and cognitive processes, in particular memory and attention processes.
The reasons for the effectiveness of this particular process of studying the material were attempted to be investigated by scientists from New York University, Douglas Markant and Todd Gurekis. They approached the study of this type of learning from a computational and cognitive point of view.
Experts put forward several hypotheses regarding why self-directed learning has advantages over other types of learning.
Self-directed and independent learning helps a person optimize their experience and focus on learning materials that we have not yet mastered. In addition, the nature of self-directed learning allows for the retention of learned information over a long period of time.
However, this type of learning is not always effective. A person can make mistakes in making decisions about the information he or she is going to study. The reason for this may be cognitive errors.
The researchers note that computational models commonly used in machine learning research could be used to study how people evaluate different sources of information and evaluate the data they seek.
Analysis using machine learning methods can help identify the negative and positive aspects of self-directed learning.
A combined study that includes an assessment of this type of learning from both cognitive and computational processes will help experts understand the processes that underlie independent, self-directed learning.
Scientists also hope that by understanding these processes, it will be possible to develop auxiliary methods for independent study of the material.