  Educational content labeled with proper knowledge components (KCs) are
particularly useful to teachers or content organizers. However, manually
labeling educational content is labor intensive and error-prone. To address
this challenge, prior research proposed machine learning based solutions to
auto-label educational content with limited success. In this work, we
significantly improve prior research by (1) expanding the input types to
include KC descriptions, instructional video titles, and problem descriptions
(i.e., three types of prediction task), (2) doubling the granularity of the
prediction from 198 to 385 KC labels (i.e., more practical setting but much
harder multinomial classification problem), (3) improving the prediction
accuracies by 0.5-2.3% using Task-adaptive Pre-trained BERT, outperforming six
baselines, and (4) proposing a simple evaluation measure by which we can
recover 56-73% of mispredicted KC labels. All codes and data sets in the
experiments are available at:https://github.com/tbs17/TAPT-BERT
