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While online education keeps expanding, web-based institutions face high dropout rate, pushing costs up and
making a negative social impact. Based on the analysis of existing research, personal characteristics and learning behavior
were selected as input variables to train a dropout prediction model using neural network algorithm. The outcomes of prediction
model were analyzed by calculating the rates of accuracy, precision, and precision. The results suggest this method
is effective in identifying potential dropouts, and can help the online education institutions prevent dropout.