Towards Blooms Taxonomy Classification Without Labels

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
EditorsIdo Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin, Vania Dimitrova
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030782917
StatePublished - 2021
Event22nd International Conference on Artificial Intelligence in Education, AIED 2021 - Virtual, Online
Duration: Jun 14 2021Jun 18 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12748 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Artificial Intelligence in Education, AIED 2021
CityVirtual, Online


  • Bloom’s level classification
  • Weakly supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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