Predicting dropout from online education based on neural networks

Abstract

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.