Automated machine learning (AutoML) is the process of automating part of the trivial, time-consuming tasks in building machine learning pipelines [1]. The tasks that can be performed by AutoML include preprocessing the data, selecting suitable models, optimizing the model hyperparameters, and providing the predictions with the best model found [2]. In educational data mining (EDM) scenarios, when a data scientist is unavailable, or in countries where data scientists are scarce [3], AutoML can be a feasible solution for non-expert users to create reusable models for their prediction tasks effectively. Typical educational data mining tasks educators have utilized AutoML include predicting dropouts [3] and course grades [4].

Recommended Citation: Griep K., Song Y., Ricanek K., Modaresnezhad M., (2023). Ensuring Ethical, Transparent, and Auditable Use of Education Data And Algorithms on Automl. UNCW MS CSIS Proceedings. V. 17 , N. 19 .