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Effective time management is associated with greater academic performance and lower levels of anxiety in students; however many students find it hard to find a balance between their studies and their day-to-day lives. This article examines the self-reported time management behaviors of undergraduate engineering students using the Time Management Behavior Scale. Correlation analysis, regression analysis, and model reduction are used to attempt to determine which aspects of time management the students practiced, which time management behaviors were more strongly associated with higher grades within the program, and whether or not those students who self-identified with specific time management behaviors achieved better grades in the program. It was found that students’ perceived control of time was the factor that correlated significantly with cumulative grade point average. On average, it was found that time management behaviors were not significantly different across gender, age, entry qualification, and time already spent in the program.

This paper develops an abstract framework for Infinitesimal Perturbation Analysis (IPA) in the setting of stochastic flow models, and it applies it to several problems arising in the study of flow control in single-server fluid-flow queues. The framework is based on a switched-mode hybrid-system paradigm, and especially on the interplay between its discrete-event dynamics and continuous-time dynamics. It is quite general, and most of the formulas obtained to-date for IPA on single-server queues can be derived from it as simple corollaries. Additional new results can be derived as well, and the paper demonstrates it by considering a queue with loss-rate-based flow control. The main contribution of the paper is in the proposed framework and its apparent broad scope. Its possible extension to a general class of fluid-flow queueing networks appears likely, and will be pointed out as a direction for future research.Index Terms-Fluid-flow queues, infinitesimal perturbation analysis (IPA), stochastic hybrid systems.

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