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Using average uniform algorithm to model educational data

Curve fitting is widely used in different fields to model various types of continuous data. The resultant model is then used to predict one or more output variables based on different values for input variables. Optimization is a very important technique for different fields of research. In most cases, researchers in the field of smart learning have to deal with large amounts of data, which usually have to be analyzed and modeled to assess the learning process and to come up with new models for prediction. In this paper, we propose an approach that will be used for modeling educational data based on a curve fitting method. In order to optimize the parameters for the model, the Average Uniform Algorithm (AUA) is used. The idea behind the algorithm is based on a mathematical approach unlike other meta-heuristic algorithms that are inspired by nature such as Genetic Algorithm (GA), Simulated Annealing (SA), and Ant Colony (ACO). The algorithm is principally constructed using uniform distribution to generate random solutions, and then averaging the best solutions to obtain the optimal value for the objective function.