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The results suggest that computer-assisted learning methods will be of greater help to students who do not find the traditional methods effective. Explorations of the factors behind this are a matter for future research.

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Bio-inspired ad hoc routing is an active area of research. The designers of these algorithms predominantly evaluate the performance of their protocols with the help of simulation studies. Such studies are mostly scenario and simulator specific and their results cannot be generalized to other scenarios and simulators. Therefore, we argue that mathematical tools should be utilized to develop a consistent, provable and compatible formal framework in order to provide an unbiased evaluation of Bio-inspired ad hoc routing protocols. Motivated by this requirement, in this paper, we develop a probabilistic performance evaluation framework that can be used to model the following key performance metrics of an ad hoc routing algorithm: (1) routing overhead, (2) route optimality, and (3) energy consumption. We utilize this framework to model a well known Bee-inspired routing protocol for ad hoc sensor networks, BeeSensor. We also show that the proposed framework is generic enough and can easily be adapted to even model a classical routing protocol, Ad Hoc on Demand Distance Vector (AODV). The modeled metrics of the two algorithms not only allow unbiased performance comparison but also provide interesting insights into the parameters governing the behavior of these routing protocols.