Methods described in this paper use these inputs in order to extract specific information about the learner (outputs).
This may trigger a new input for another process of the ICLT model as learners' output is a process rather than a product of their ICC learning.
The main idea behind this optimization method is to profit from the influence of a teacher on the learners' output in a class [47].
Learners' outputs also serve to highlight their source of learning difficulty so that the teacher can intervene to provide remedial programs.
In addition, the audiolingual aspect of shadowing practice can also lead to learners' anxiety, such as speech apprehension, test anxiety, and fear of negative evaluation, because shadowing requires learners to output speech in exactly the same manner as in the model audio, in front of an audience in class (Horwitz et al. 1986).
When the learners talk with each other, teacher feedback often becomes an additional source of input in the target language and the learners' original output may be 'filtered' for them by the teacher (Andrews, 2007).
Collectively, we can refer to these as "generative" cognitive work because both involve learners generating outputs beyond what has been presented to them (Table 1).
For example, phylogenetic profiles, one of the most useful attributes, are based on an unsupervised learning approach, where no prior information is given to the learner regarding the output or class label.
While dynamic learning stresses the idea of joint interactions between learners, it fails to account for the pauses of silence where learners produced no output.
Therefore, on day one, the effort was focused on affording a suitable context in which enough input could be presented to the learners; however, on the next lesson, it was attempted to make learners produce an output which required the use of previously acquired target forms.
