Student learning performance prediction based on online behavior: an empirical study during the COVID-19 pandemic.

ABSTRACT: In the context of the COVID-19 global pandemic, highly intense and frequent online teaching has leapt to be one of the dominant learning patterns and become an ordinary situation in university teaching practices. In recent years, progress in feature engineering and machine learning has made it possible for more effective educational data mining, which in turn has enhanced the performance of intelligent learning models. However, the potential impact of increasing and varying features on online instruction in this new situation makes it unclear whether the existing related findings and results are practical for teachers. In this article, we use various state-of-the-art machine learning techniques to predict students' performance. Based on the validation of the rationality of the built models

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10703107 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature