Data Collection and Pre-processing for Machine Learning-Based Student Dropout Prediction

In this era of big data, a large amount of data is generated from various educational environments that will help integrate machine learning techniques to find valuable insights from the data. Student dropout prediction is one of the application areas in education where machine learning algorithms can be applied to predict the students who are likely to dropout of their studies by extracting valuable patterns and key parameters from educational data. However, the performance of the algorithms is determined by the quality of the data we feed into them. The educational data are usually noisy that reduces the performance of the machine learning algorithms and requires accurate data pre-processing. This work presents an overview of data collection and pre-processing steps that consist of data augmentation, data labeling, enhancing data/model, data cleaning, data transformation, feature extraction, feature transformation, feature selection, etc. for analysis of statistical evidence from the source data for student dropout prediction. We also present some of the data pre-processing techniques which are suitable for machine-learning-based student dropout prediction in this study.