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Self-assessment is a key component of "assessment as learning," [ 7] where students apply self-regulatory processes in their learning such as setting goals, selecting learning strategies, assessing learning progress, evaluating information from feedback, and then making improvements in their learning processes for the next time.
Reif et al. (2012b) investigate increasing the accuracy and decreasing runtime of a genetic algorithm for selecting learning parameters for a Support Vector Machine and a Random Forests classifier.
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For each experiment, first, system-centric adaptations rules are applied to select learning resources that can be played on the used device.
Instead, we asked them to select learning strategies for exam 2 (planning) using insights from their exam 1 experiences (evaluating).
This type of approach could help students in the "struggling" category gain metacognitive knowledge, which would help them select learning strategies that better align with their studying needs.
These students recognized the benefit of adjusting their study plans and could select learning strategies for exam 2 that appropriately addressed issues they had in preparing for exam 1.
This suggests that subjects who showed greater (though opposite) responses to LV and DV in the vmPFC/OFC showed an increased bias toward selecting learned-value options.
Additionally, we hypothesized that between-subject variability in responses to learned and described value would predict the degree to which individuals displayed choice behavior biased toward selecting learned-value options.
Strategic control includes modifying, selecting, or learning specific movement plans that are appropriate to the task at hand.
From Table 2, we can see that if we use the top components, about 60 80% components are selected into learning machines, while if we use feature selection to select useful components, about 30% components are selected on average.
The selected learning schemes are applied to eight datasets extracted from the ISBSG R12 Dataset.
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