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Note that we only consider game performance during the two MRI scans in this paper, since the main focus of the paper is on predicting learning success from imaging data.
(2014) found evidence for the importance of early growth or catch-up growth in predicting learning speed, whereas early growth was one of the less important predictors in our study.
Variables for predicting learning outcomes.
However, the formula is interesting, and Poehner and Lantolf (2013) employed it in computerized dynamic assessment to further test its feasibility in predicting learning potential.
If the learners of high LPS outperform those of lower LPS, LPS is valid in predicting learning potential; otherwise, its validity is questioned.
Several studies have been done to process these logs to elicit the following: Students' learning and interaction Variables for predicting learning outcomes Clustering students into groups based on their interaction.
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Although the original manuscript never claimed that learning and coherence changes are causally related, we have eliminated expressions like "learning-induced" or "changes in coherence predict learning" from the revised manuscript.
However, in the non-embedded condition, it was expected that spatial skill would predict learning.
The study presented here assessed which variables predicted learning gains in a 1-h lesson.
As in Experiment 1, qualities of the explanations predicted learning outcomes.
The study assessed which variables predicted learning gains after a 1-h lesson on the electric field.
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