Evaluating Agile Neural Educational System for Effective Resource Management

Abstract

The major challenge of most basic schools is inadequate educational resources despite a conscious effort to constantly provide. This is a result of inaccurate data management leading to inappropriate predictions for effective planning. The actual efficiency of a system is determined by its ability to predict real-life data with speed and accuracy. In this work, the neural educational expert system (ES) is evaluated using mathematical models for predicting the availability of resources for the growing school-aged population using a criteria-based formative evaluation to know resource life and its effect on availability. This will help in the decision to add more resources by knowing when and how the resources should be added. Technical mathematical model generation through differential equations is used to fuse the factors affecting the availability of educational resources. The real-life data is used in prediction regarding the actual enrollment of learners and the availability of resources. The model is evaluated and critically analyzed to know the degree of accuracy and the steady state. The findings revealed that the resources decay and attrite at an exponential rate in the long run and the constant number of resources provided cannot cater for the rate of decay, resulting in inadequacy. A proposed algorithm for managing the resources is presented.