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.
Keywords
- Deep knowledge tracing
- Revised Bloom’s Taxonomy
- Cognitive skills
- Hierarchical taxonomy
- Deep learning
- Student modeling
- Domain knowledge
- funtoot
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Lalwani, A., Agrawal, S. (2018). Validating Revised Bloom’s Taxonomy Using Deep Knowledge Tracing. In: , et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_17
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DOI: https://doi.org/10.1007/978-3-319-93843-1_17
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