Denoising autoencoder-based imputation of Harvard-MITx person-course dataset

Abstract

With the rapid development of smart education around the world, online education is becoming the new normal in the education industry. While online education is booming, a huge amount of data is generated. Online education datasets contain a large number of missing values for various reasons. The existence of missing values hinders educational research, especially educational mining and learning analysis for datasets. Based on Harvard-MITx Person-Course Dataset (HMPC) online education dataset, a batch denoising autoencoder imputation method is proposed. In order to facilitate performance evaluation, the experiment cleans the HMPC dataset, extracts some data that are not missing, and then manually introduces missing data. The experimental results show that the imputation values are similar to the results of the initial data, and the proposed algorithm better preserves the classification performance of the dataset.