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In this study, the extraneous cognitive load is minimized by avoiding temporal split attention with regard to supportive information (i.e., conceptual models or 'theory') and the germane cognitive load is optimized by using schematic representations of this information to direct learner's attention to concepts relevant for learning.
Improvements are expressed in the percentage of increase over the baseline performance as demonstrated by the DNN_PCM covered in "Temporal split partition" section (temporal split).
These settings were validated on the temporal split because it represents a more realistic and more challenging scenario as shown in "Temporal split partition" section.
Hence, this make temporal split validation a better way to validate computational models.
This was done for both the random split set and temporal split set.
To accurately compare the methods, 4 z-scores were calculated for each method and metric within the experiments (random split MCC, random split BEDROC, temporal split MCC, and temporal split BEDROC, Table 1 and Fig. 4).
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In particular I will discuss the strategies we are evaluating to efficiently time-step the incompressible Navier-Stokes equations with particular emphasis on temporal splitting, optimizing the advection steps, and preconditioning the elliptic solves.
It has been suggested in literature that also temporal splitting is not ideal, but it still provides a more challenging form of validation and better than leaving out chemical clusters [27].
The split-attention principle focuses on replacing multiple sources of information, distributed either in space (spatial split-attention) or time (temporal split-attention), with one integrated source of information [ 17].
The relative arrangement of these appendages dictates the temporal splitting between the peaks, which we interpret using two models.
Thus, temporal splitting pattern supports antiparallel orientations for the two S1-S2a/S2Ca sthatds thosthosthehe near-infrared cluster.
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