Prediction model for students' future development by deep learning and tensorflow artificial intelligence engine

Classification and prediction of students' performance in examination are the typical challenges for educators. Various traditional data mining methods such as decision tree and association rules were used to perform classification. In recent years, the rapid development of artificial intelligence and deep learning algorithm provided another approach for intelligent classification and result prediction. In this paper, a research on how to use Tensorflow artificial intelligence engine for classifying students' performance and forecasting their future universities degree program is studied. An appropriate and accurate forecast is important for providing prompt advice to student on program and university selection. For a more comprehensive consideration of an all rounded factors, the deep learning model analysed not only the traditional academic performance including Mathematic, Chinese, English, Physics, Chemistry, Biology and History, but also non-academic performance such as service, Conduct, Sport and Art. A few parameters in Tensorflow engine including the number of intermediate nodes and number of deep learning layers are adjusted and compared. With a data set of two thousands students, 75% of these data are used as the training data and 25% are used as the testing data, the accuracy ranged from 80% to 91%. The optimal configuration of the Tensorflow deep learning model that achieves highest prediction accuracy is determined. This study determined the factors affecting the accuracy of the prediction model.