Meta learning
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. Overview. 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". Maudsley 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: 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". Meta learning can be defined 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 their 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 their learning approach or the nature of the learning task set. In consequence, the student will be unable to adapt successfully when studying becomes more difficult and demanding. Conceptually, meta  learning is a mixture of understanding, processes, and attitudes. It includes the self-knowledge about how one learns, particularly an awareness of the learning strategies and behaviors applicable to a learning context (Jackson 2004; Boström and Lassen 2006). It also includes 'knowledge of completion', in which learners develop an appreciation of the knowledge that they have gained and an understanding of how to use this knowledge (Boström and Lassen 2006). Meta learning also relates to learners' attitudes, such as their belief that the way they self-regulate is the best way for them, and that they have the capacities and  skills to apply their knowledge (Jackson 2004). Based on this perspective, meta learning is an active, internal process in which a learner's point of view regarding themselves and their surroundings will change and be regulated (Boström and Lassen 2006; Winters 2011). Engaging effectively in meta learning has been shown to improve academic performance (Biggs 1985). It has been suggested that aiding the metacognition of learning can help students to become more effective learners, as they become more aware of self-regulatory behaviours and begin to recognise the effectiveness of various strategies they use (Jackson 2004). Meta learning can also  be a very effective tool to assist students in becoming independently self-reflective (Biggs 1985; Winters 2011). As meta learning relates to students' self-awareness regarding their learning processes, it is closely aligned to the self-regulation of learning (Zimmerman 2002; Winne 2010); that is, the thoughts, feelings and actions that students use to help them attain their academic goals (Zimmerman 2000). 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 performance 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. Ideas for implementation and goals. Meta learning can also be a very effective  tool to assist students in becoming independently self-reflective. Students will require feedback in order to reflect on their learning, strengths, and weaknesses. Meta learning tasks will help students be more proactive and effective learners by focusing on developing self-awareness. Meta learning tasks would provide students with the opportunity to better understand their thinking processes in order to devise custom learning strategies. The goal is to find a set of parameters that work well across different tasks so that learners start with a bias that allows them to perform well despite receiving only a small amount of task-specific  data. Tim Ferriss's DiSSS System. Tim Ferriss created a four stem system which he argues can be used to learn anything.