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ABSTRACT

Cognitive learning complexity identification of assessment questions is an essential task in the domain of education, as it helps both the teacher and the learner to discover the thinking process required to answer a given question. Bloom’s Taxonomy cognitive levels are considered as a benchmark standard for the classification of cognitive thinking (learning complexity) in an educational environment. However, it was observed that some of the action verbs of Bloom’s Taxonomy are overlapping in multiple levels of the hierarchy, causing ambiguity about the real sense of cognition required. The paper describes two methodologies to automatically identify the cognitive learning complexity of given questions. The first methodology uses labelled Latent Dirichlet Allocation (LDA) as a machine learning approach. The second methodology uses the BERT framework for multi-class text classification for deep learning. The experiments were performed on an ensemble of 3000+ educational questions, which were based on previously published datasets along with the TREC question corpus and AI2 Biology How/Why question corpus datasets. The labelled LDA reached an accuracy of 83% while BERT based approach reached 89% accuracy. An analysis of both the results is shown, evaluating the significant factors responsible for determining cognitive knowledge.

Keywords: multi-class text classification, labelled LDA, pre-trained BERT (Bidirectional Encoder Representations from Transformers), question classification