TY - GEN
T1 - Towards Blooms Taxonomy Classification Without Labels
AU - Wang, Zichao
AU - Manning, Kyle
AU - Mallick, Debshila Basu
AU - Baraniuk, Richard G.
N1 - Funding Information:
Acknowledgements. This work was supported by NSF grants 1842378 and 1937134 and by ONR grant N0014-20-1-2534. We thank Prof. Colleen Countryman (Ithaca College), Prof. Lauren Rast (The University of Alabama at Birmingham), Joyce Spangler (Six Red Marbles), and Andrew Giannakakis (OpenStax) for helpful discussions on the Labeling Functions. Thanks to Fred Sala for insights on WSL. Thanks to anonymous reviewers and CJ Barberan for suggestions on the manuscript.
Funding Information:
This work was supported by NSF grants 1842378 and 1937134 and by ONR grant N0014-20-1-2534. We thank Prof. Colleen Countryman (Ithaca Col-lege), Prof. Lauren Rast (The University of Alabama at Birmingham), Joyce Spangler (Six Red Marbles), and Andrew Giannakakis (OpenStax) for helpful discussions on the Labeling Functions. Thanks to Fred Sala for insights on WSL. Thanks to anonymous reviewers and CJ Barberan for suggestions on the manuscript.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Bloom’s level classification
KW - Weakly supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85126479080&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126479080&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78292-4_35
DO - 10.1007/978-3-030-78292-4_35
M3 - Conference contribution
AN - SCOPUS:85126479080
SN - 9783030782917
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 433
EP - 445
BT - Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
A2 - Roll, Ido
A2 - McNamara, Danielle
A2 - Sosnovsky, Sergey
A2 - Luckin, Rose
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Artificial Intelligence in Education, AIED 2021
Y2 - 14 June 2021 through 18 June 2021
ER -