Estimating of student success with artificial neural networks
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Abstract
Purpose of the study is classifying students’ success of Information and Communication Technology-ICT courses by using Artificial-Neural-Networks-ANNs models. To classify success of 161 students, a three-layer-feed-forward-ANNs model is used. 3 parameters which contain demographic data and 27 parameters which contain ICT-Usage data captured by a questionnaire chosen as input layer parameters. Hidden nodes are determined experimentally. Logic-0 and Logic-1 are the output level values which define success of the students in ICT courses. The Back-Propagation algorithm is used for training of ANN. The mean squares of the errors are used as a performance (error) function with its goal set to zero. In conclusion, the application done with success ratio of 96%. If same variables are used, realistic estimations will be reached. It is recommended that a research with same parameters would be better results with higher participation.
Keywords: Artificial Neural network, backpropagation, student success, classification, ICT.
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