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Smith and Ushioda (2009) stated that the capacity of learners should be considered when learner autonomy (learner-centeredness) is focused.
In Chang and colleagues' research, they provided jewels as reward when learner accomplishes one quest in the mobile game in the museum (Chang et al. 2008).
When learner model visualisations are complex, such as the Next-TELL and LEA's Box OLM competency networks in Fig. 4, another way to initiate negotiation than that proposed above will be required.
Display via context: When learner A is at the supermarket in the daytime, the system will provide him with knowledge about "tofu" (a food made by curdling soymilk) and "edamame" (a preparation of immature soybeans in the pod, found in the cuisines of China, Japan, Korea, and Hawaii) related to "supermarket" and "daytime".
When learner feedback was incorporated into curricular revision, learner satisfaction with the instructive value of the curriculum (1 = not instructive; 5 = highly instructive) increased from 3.8 to 4.1 (p < 0.001), and knowledge gains (i.e., post test scores minus pretest scores) increased from 17.0 % to 20.2%% (p < 0.001).
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For instance, when learners train their weak LS.
An assignment is completed when learners pose the problem correctly.
The results indicate that comprehension improves when learners are exposed to a text in several modalities.
When learners register in the LMS through a registration form, the Learner Model is initialized.
When learners are asked to solve a word problem, the system will instruct the learners what to do.
That is, noticing of errors occurs when learners realise their language limitations evident through their output.
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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