Comprehension Based Question Answering using Bloom’s Taxonomy

Abstract

Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom's Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.

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“…Bloom's taxonomy is used by educational institutions to design course learning outcomes (CLOs) and match assessment items to them in order to improve student learning (Shaikh, Daudpott & Imran, 2021). However, difficulties in using the taxonomy have been reported and some authors have suggested that there are inherent flaws in the classification system (Sahu et al, 2021). To address this issue, researchers have proposed deep learning models such as LSTM-based models to more accurately classify CLOs and assessment items into Bloom's levels (Waheed et al, 2021).…”

“…To address this issue, researchers have proposed deep learning models such as LSTM-based models to more accurately classify CLOs and assessment items into Bloom's levels (Waheed et al, 2021). In addition, Bloom's taxonomy was used to analyze and improve the comprehension skills of the pre-trained language models, resulting in improved performance on question answering tasks (Sahu et al, 2021).…”

“…Arlianty et al (2018) use the taxonomy to identify students' capacity to answer questions according to their cognitive level. Sahu et al (2021) use the taxonomy to improve the question answering comprehension capabilities of pre-trained language models. Overall, Anderson and Krathwohl's taxonomy is a useful tool for understanding and improving educational learning outcomes.…”