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

Lina Cao; Xiaoyun Zhou

doi:10.1117/12.2641453

10 November 2022 Denoising autoencoder-based imputation of Harvard-MITx person-course dataset

Lina Cao, Xiaoyun Zhou

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481X (2022) https://doi.org/10.1117/12.2641453
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

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.

Citation Download Citation

Lina Cao and Xiaoyun Zhou "Denoising autoencoder-based imputation of Harvard-MITx person-course dataset", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481X (10 November 2022); https://doi.org/10.1117/12.2641453

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