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Meta-learning is a branch of metacognition concerned with learning about one's own learning and learning processes. The term comes from the meta prefix's modern meaning of an abstract recursion , or "X about X", similar to its use in metaknowledge , metamemory , and meta-emotion .
This self-awareness of memory has important implications for how people learn and use memories. When studying, for example, students make judgments of whether they have successfully learned the assigned material and use these decisions, known as "judgments of learning", to allocate study time. [2]
The theory of metacognition plays a critical role in successful learning, and it's important for both students and teachers to demonstrate understanding of it. Students who underwent metacognitive training including pretesting, self evaluation, and creating study plans performed better on exams. [ 28 ]
The journal was formerly titled the Journal of Experimental Psychology: Human Learning and Memory.In 1980, the editor of Human Learning and Memory, Richard M. Shiffrin, announced that he intended to "broaden the scope of the journal to include a more general set of topics in human cognition", and that the journal would be renamed Learning, Memory, and Cognition. [4]
the article about bibliographic databases for information about databases giving bibliographic information about finding books and journal articles. Note that "free" or "subscription" can refer both to the availability of the database or of the journal articles included. This has been indicated as precisely as possible in the lists below.
The process of learning to read depends heavily on analysed knowledge on the functions and features of reading, [4] control over the knowledge required [5] and control over the formal aspects of the language to extract its meaning. [6] Various research has exhibited that weaknesses in any one of these aspects reflects poorer literacy.
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
There is evidence to support the theory that the IOED is a contributing factor to increased political polarization in the United States. [11] A 2018 study with participants recruited in the context of the 2016 United States presidential election found that higher levels of IOED about political topics is associated with increased support in conspiracy theories.