Sentence examples for across learners from inspiring English sources

Secondly, Sampson (2012) reports that "[a]nything learners had written beyond 150 words was not corrected by the teacher, to ensure equality of sampling across learners' texts" (pg. 498).

In this commentary, through reexamination of Sampson's research, we argue that (1) correction of errors that appeared on previous drafts should not be equated with the ability to produce correct forms in future writings; (2) equality of sampling across learners' texts should have been more systematic; and (3) error types deserve a more systematic classification scheme.

In this paper we argue that (1) correction of errors that appeared on previous drafts should not be equated with the ability to produce correct forms in future writings; (2) equality of sampling across learners' texts should have been more systematic; and (3) error types deserve a more systematic classification scheme.

Sampson states "…it was necessary to give feedback on all the errors in each piece of work" (p. 498) but previously in the paper had stated "Anything learners had written beyond 150 words was not corrected by the teacher, to ensure equality of sampling across learners' texts" (p. 498).

Validation across learner characteristics (e.g. students vs. clinicians, nurses vs. physicians, users vs. doers) is most useful for broad adoption within EBP education, but as a minimum, tools should identify the type(s) of learner(s) for which they are validated.

Results are provided in Tables 7 and 8. Mean columns represent the average of performance metrics across all learners (clustering and classification) and data sets.

With these adaptive learning technologies, learning platform reacts to individual learner data and adapts instructional resource accordingly based on cloud computing and learning analytics, and it can leverage aggregated data across mass learners for insights into the design and adaptation of curricula based on big data (NMC 2015).

For the purposes of analysis, the mean 'learning-trial' across trained learners was assigned to all pseudo-conditioned mice.

A comparable characterization of K-Channel expression in brain areas associated with speech and language (e.g. Broca's, orofacial and laryngeal motor cortex, etc). is lacking in humans, thus we cannot at present assess the degree of brain gene expression conservation across vocal learners.

(* indicates within condition [same time-tuning v/s random cell-pairs], # indicates across conditions [trace learners v/s pseudo-conditioned] and @ indicates comparisons across stages of learning [early v/s middle v/s late], p<0.01; n.s. indicates not significant).

This seems to suggest that the impact of AIBL may not be uniform across all learner profiles.