Harman Patil (Editor)

Meta learning

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Meta learning is the study of disciplines.



Meta learning is originally described by Donald B. Maudsley (1979) as "the process by which learners become aware of and increasingly in control of habits of perception, inquiry, learning, and growth that they have internalized". Maudsely sets the conceptual basis of his theory as synthesized under headings of assumptions, structures, change process, and facilitation. Five principles were enunciated to facilitate meta-learning. Learners must:

(a) have a theory, however primitive; (b) work in a safe supportive social and physical environment; (c) discover their rules and assumptions; (d) reconnect with reality-information from the environment; and (e) reorganize themselves by changing their rules/assumptions.

The idea of meta learning was later used by John Biggs (1985) to describe the state of "being aware of and taking control of one’s own learning". You can define meta learning as an awareness and understanding of the phenomenon of learning itself as opposed to subject knowledge. Implicit in this definition is the learner’s perception of the learning context, which includes knowing what the expectations of the discipline are and, more narrowly, the demands of a given learning task.

Within this context, meta learning depends on the learner’s conceptions of learning, epistemological beliefs, learning processes and academic skills, summarized here as a learning approach. A student who has a high level of meta learning awareness is able to assess the effectiveness of her/his learning approach and regulate it according to the demands of the learning task. Conversely, a student who is low in meta learning awareness will not be able to reflect on her/his learning approach or the nature of the learning task set. In consequence, s/he will be unable to adapt successfully when studying becomes more difficult and demanding.

Meta learning model for teams and relationships

Marcial Losada and other researchers have attempted to create a meta learning model to analyze teams and relationships. A 2013 paper provided a strong critique of this attempt, arguing that it was based on misapplication of complex mathematical modelling. This led to its abandonment by at least one former proponent.

The meta learning model proposed by Losada is identical to the Lorenz system, which was originally proposed as a simplified mathematical model for atmospheric convection. It comprises one control parameter and three state variables, which in this case have been mapped to "connectivity," "inquiry-advocacy," "positivity-negativity," and "other-self" (external-internal focus) respectively. The state variables are linked by a set of nonlinear differential equations. This has been criticized as a poorly defined, poorly justified, and invalid application of differential equations.

Losada and colleagues claim to have arrived at the meta-learning model from thousands of time series data generated at two human interaction laboratories in Ann Arbor, Michigan, and Cambridge, Massachusetts, although the details of the collection of this data, and the connection between the time series data and the model is unclear. These time series portrayed the interaction dynamics of business teams doing typical business tasks such as strategic planning. These teams were classified into three performing categories: high, medium and low. Performance was evaluated by the profitability of the teams, the level of satisfaction of their clients, and 360-degree evaluations.

One proposed result of this theory is that there is a ratio of positivity-to-negativity of at least 2.9 (called the Losada line), which separates high from low performance teams as well as flourishing from languishing in individuals and relationships. Brown and colleagues pointed out that even if the proposed meta-learning model were valid, this ratio results from a completely arbitrary choice of model parameters—carried over from the literature on modeling atmospheric convection by Lorenz and others, without any justification.


Meta learning Wikipedia