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The findings of studies on language learner e-readiness are of a context-bound nature, highly contingent upon factors such as technological infrastructure of an institution, demographic features of learners (e.g., nationality), and their attitude toward e-learning.
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We investigate the blended learning design features of learner interactions, face-to-face support, learning management system tools and technology quality while the outcomes considered include satisfaction, performance, intrinsic motivation and knowledge construction.
Moreover this two layers system is able to provide various features of a learner model for instance resistance against perturbations, modulation of trajectories amplitude and frequency.
It became evident that one-dimensional focus on the meaning-based features of language hinders learners from attaining linguistic accuracy.
The SRL features of the skillful learners in each phase support learning processes by helping teachers predict learning styles and learning performance.
Nevertheless, as we now have a more reliable estimate of the correlation lengths we can take advantage of a different feature of the learner.
Even though cross-validation was used internally for combinations of features and learners to evaluate predictive accuracies, when these results are subsequently used to make decisions (such as which features to use) it compromises any conclusions from further analysis of the same training and testing data.
Some works have compared the degree of usage of grammatical features by learners with native writers, in an attempt to identify over- and under-use of features by the learner, and thus to take corrective measures.
This VM allows students and faculty to collaboratively create content, annotate slides with markers, and it is enhanced with social networking features to give the community of learners more control over the system.
In addition, studies have used the intelligent tutoring system (ITS) to evaluate structural features of the knowledge that learners possess.
The questions that were asked during this semi-structured interview were about what specific features of the task the learners liked or disliked.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com