Sentence examples for performance on problems from inspiring English sources

The a posteriori-driven mesh refinement is shown to significantly enhance the performance on problems featuring singular solutions, allowing to fully exploit the high-order of approximation.

processor is architected for multimedia applications with regular processing requirements, we are interested in its performance on problems with non-uniform memory access patterns.

We followed the procedure of Ollinger et al. [31] who demonstrated that repeatedly solving problems requiring one kind of insight (e.g. changing an X to a V as shown in Type 1 of figure 1) impairs subsequent performance on problems requiring a different kind of insight (e.g. changing a + sign to an  =  sign as shown in Type 2 of figure 1).

However, they did not completely ignore the presence of the support, as there was a significant difference between their performance on problems S7 and NR3/f.

Finally, our results suggest that there are several variables which could potentially mediate the link between maths anxiety and performance on problems with a numerical content (including general mathematical knowledge, self-confidence, and time spent on solving the problems).

To determine whether the distance of the large distracter food affected performance, performance on problems Lf−1 and Lf−2 in Experiment 1 and problems Lf−3 and Lf−4 in Experiment 2 was compared using t tests, but neither of the differences was significant (Lf−1 vs. Lf−3: t3 = 0.27, P = 0.804; Lf−2 vs. Lf−4: t3 = 2.35, P = 0.134).

The result of this synergy is, therefore, a formal approach that allows carrying out assessments of performance on problem solving tasks.

While descriptive, written feedback can enhance student performance on problem-solving tasks; reaping those benefits requires students to read, understand, and use the feedback.

However the results did suggest that baseline performance on problem list management may be negatively correlated post-training improvements, although the relationship was not strong enough to be significant in our small sample (p < 0.1).

CC shows better performance on separable problems, but deteriorated on non-separable problems, because the interacting variables could not be grouped in one subcomponent.

The animals displayed excellent generalisation performance on untrained problems, and the effect size of generalisation was larger when they were first trained with "connected problems" (i.e., problems in which there was a gap in the unreinforced cloth) than when they were first trained with "on problems" (i.e., in which the food items were either on or not on the cloth).