Towards Blooms Taxonomy Classification Without Labels

Zichao Wang, Kyle Manning, Debshila Basu Mallick, Richard G. Baraniuk

Abstract: In this work, we explore weakly supervised machine learning for classifying questions into distinct Bloom’s Taxonomy levels. Bloom’s levels provide important information that guides teachers and adaptive learning algorithms in selecting appropriate questions for their students. However, manually providing Bloom labels is expensive and labor-intensive, which motivates a machine learning approach. Current automated Bloom’s level classification methods employ supervised learning that relies on large labeled datasets that are difficult and costly to construct. In this paper, we propose a weakly supervised learning method that performs binary Bloom’s level labeling without any a priori known Bloom’s taxonomy labels. The key idea behind BLACBOARD (for Bloom’s Level clAssifiCation Based On weAkly supeRviseD learning) is to appropriately incorporate human domain knowledge into the modeling process to produce a weakly labeled dataset on which discriminative models can then be trained. We compare BLACBOARD to fully supervised learning methods and show that it achieves little to no performance compromise while using entirely unlabeled data.