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In general, these approaches use deterministic inference systems for detecting students' behavioural patterns.
With an aim of detecting students learning process in the out-of-classroom settings, the study borrowed part of project data (i.e., learning artifacts created by mobile tools).
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Given this, an important question is how effective lexical analysis will be at detecting student ideas across isomorphic items designed to test for the same idea (e.g., natural selection).
Several approaches to detect students' perception style according to Felder's model have been proposed.
This could be used to detect students with difficulties ahead of time through early intervention.
This approach aims to automatically detect students' LS based on GA techniques [12, 17].
"Examination systems should be set up in such a way to detect students who purchase their thesis".
It is therefore suggested that an instrument be designed to detect students' STEM competencies for future research.
An early performance indicator is needed to detect students with mathematical problems at an early stage so that relevant actions can be undertaken to mitigate the problem.
One advantage of our approach is to directly detect students' LS based on their performance, as depicted later in this paper.
As a consequence, we isolated the around complexity, in order to come up with an efficient model to automatically detect students' LS.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com