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Investigating the Impact of a Meaningful Gamification-Based Intervention on Novice Programmers’ Achievement Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Validating Revised Bloom’s Taxonomy Using Deep Knowledge Tracing

Authors : Amar Lalwani, Sweety Agrawal

Published in: Artificial Intelligence in Education

Publisher: Springer International Publishing

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Abstract

Revised Bloom’s Taxonomy is used for classifying educational objectives. The said taxonomy describes a hierarchical ordering of cognitive skills from simple to complex. The Revised Taxonomy relaxed the strict cumulative hierarchical assumptions of the Original Taxonomy allowing overlaps. We use a knowledge tracing model, Deep Knowledge Tracing (DKT), to investigate the hierarchical nature of the Revised Taxonomy and also study the overlapping behavior of the Taxonomy. The DKT model is trained on about 42 million problems attempted on funtoot by the students. funtoot is an adaptive learning platform where students learn by answering problems. We propose a novel way to interpret the model’s output to measure the effects of each learning objective on every other learning objectives. The results confirm the relaxed hierarchy of the skills from simple to complex. Moreover, the results also suggest overlaps even among the non-adjacent skills.

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Footnotes
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Metadata
Title
Validating Revised Bloom’s Taxonomy Using Deep Knowledge Tracing
Authors
Amar Lalwani
Sweety Agrawal
Copyright Year
2018
Book
Artificial Intelligence in Education

Print ISBN: 978-3-319-93842-4

Electronic ISBN: 978-3-319-93843-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93843-1_17

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