Artificial Neural Network Applications in Selecting the Next Item for Multidimensional Computer-Adaptive Testing
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Abstract
Selection for the next item using an artificial neural network has five important steps: 1. Dataset; 2. Data Preparation, comprising 2 methods—2.1 Formatting, followed by Data Transformation, and 2.2 Data Cleaning; 3. Modeling with Algorithms—Neural Net, Data mining by dividing the dataset into two parts: Train set—80%; Test set—20%. Adjust the parameter of the Neural Network to obtain the highest efficiency in the Training Cycle, Learning rate, and Momentum; 4 Test the performance of the model with the 10-fold cross-validation method and evaluate the performance of the model (Evaluation) with accuracy, precision, recall, and f-measure; and 5. Deployment Stage by developing the model in the form of a web application MCAT, using PHP language, scripting various commands, using the Prediction API (Application Programming Interface) to connect the algorithms Neural Net with the MCAT application in the item selection algorithm stage, and using a MySQL database for storing each attribute on cloud hosting.
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