||Artificial intelligence has developed rapidly in recent years. TensorFlow is one of the important deep learning frameworks, users can easily use the deep learning algorithms in TensorFlow and start up to build model easily. It make deep learning easy to get started. Nowadays, use deep learning to analyze big data has become a trend. Moreover, Convolution Neural Network’s performance in image recognition is excellent. Nowadays, deep learning has been widely used. The application of this technology in the school system will certainly be very helpful. This article mainly introduces how to apply CNN technology to the analysis of student score analysis data and to build a model that can predict whether student will be dropped out in the next semester.|
After we obtain student score analysis data, we observed two reasons for the dropouts in the "QUIT_REASON" column, "two cumulative 1/2 credit failures" and "two cumulative 2/3 credit failures", which are directly related to grades. This article use R language to analysis all required data. Focused on all college students’ data, list the results of each semester of these college students and convert them into pictures. Every picture has a label to show whether they will be dropped out in next semester. First of all, using Keras to build CNN model in R language. Second, adjust the parameters in the model. Third, trying one by one to find the most suitable model for processing "student score analysis data.
After trying various models with different parameters, we found that there is no certain trend in parameter setting. Such as filters or epochs, the parameter value increases but the performance of the model is not necessarily better. However, CNN model needs to make various attempts through experience to find the best model.
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