According to Zimmerman's (1998) cyclic model, within a social cognitive perspective, self-regulation is a self-directed learning process that occurs in three successive phases.
In this sense, these pragmatic idealizations function as "standards for a self-correcting learning process" (2005b, 91).
Such a stability of S_an and VQ_an stems from the nature of the self-organizing learning process.
Local windows are built over the image through a self-adjustable learning process and are used to focus the feature extraction step.
The third class proposes that grid cells are formed by a self-organised learning process that borrows elements from both former classes [16 18].
Subsequently, their initial impression, which is dependent on how successful they can achieve simple tasks during their self-directed learning process, plays an important role in their intention to adopt the technology.
Science, technology, engineering, math and medicine are the most obvious examples, as new insights and discoveries are built upon the successes of the past in a progressive and self-transcending learning process.
Instead, it is the result of millions of scientists, engineers, historians, archeologists and others, who have worked diligently in diverse disciplines to discover the details of the really real over many generations in a progressive and self-transcending learning process.
The first steps in the self-directed learning process are the definition of learning needs and the articulation of learning goals.
Because the whole self-learning process includes simultaneous learning among multiple regions, the time required to complete self-learning within a certain range (denoted as ( {D}_{mathrm{LEA}}^{mathrm{TOT}} )) is the maximum value of the self-learning time between the regions.
In the self-adaptive algorithm, the convergent speed of Newton's method is faster than that of the steepest descent method because Newton's method utilizes the self-relevant messages of receiving data in the self-adaptive learning processes.
