As universities increasingly adopt digital tools and automated analytics systems, attention often centers on these tools’ gains in accuracy and efficiency. Far less visible, however, is another critical dimension: the additional work students must do to produce, organize, and interpret their own data within these systems.
Algorithmic grading in class: What a study shows about extra student workload and privacy
