This paper addresses a challenging problem concerned basically with realistic optimal computer-based educational simulations. More specifically, it searches for an optimally designed computational tool(s), such as software learning package(s), applied to teaching a specified curriculum in classroom. Herein, quantitative evaluation as well as statistical analysis of the learning environment nature have been considered for optimal learning systems' performance. Recently, this learning issue has gained significance due to the integration of information technology into educational/instructional practical operations. Accordingly, two learning parameters -that are candidates to measure effectiveness and efficiency of such packages- are elected to support the optimal selection of a relevant software learning package SWLP. These parameters -after establishing how to measure results and performance of the learning process- are: the output learning level, which evaluates obtained educational achievement, and time response considered in fulfillment of a pre-assigned educational achievement/learning level. Artificial Neural Networks (ANNs) modeling is adopted for the simulation of a realistic practical learning processes’ performance, as well as software learning packages' evaluation and testing. Additionally, herein an Artificial Neural Network (ANN) model is presented. Which is based on guided-error correction learning (learning with a teacher). Therefore, it is used as a realistic simulating tool aiming at quantitative/statistical evaluation of the learning process under investigation. Consequently, presented ANN model considered both students' individual differences as well as SWLPs employed as virtual teacher in a computer course curriculum. Two learning parameters are considered during the running of the presented model. Namely, learning convergence (response) time, and secondly, the achievement (output) learning level (amplitude) response. It is worthy to note that obtained simulation results were well supported by the case study results that have been recently published.