BloomNet: A Robust Transformer based model for Bloom's Learning Outcome Classification

Abstract

Bloom's taxonomy is a common paradigm for categorizing educational learning objectives into three learning levels: cognitive, affective, and psychomotor. For the optimization of educational programs, it is crucial to design course learning outcomes (CLOs) according to the different cognitive levels of Bloom's Taxonomy. Usually, administrators of the institutions manually complete the tedious work of mapping CLOs and examination questions to Bloom's taxonomy levels. To address this issue, we propose a transformer based model named BloomNet that captures linguistic as well semantic information to classify the course learning outcomes (CLOs) . We compare BloomNet with diverse set of basic as well as strong baselines and we observe that our model performs better than all the experimented baselines. Further, we also test the generalisation capability of BloomNet by evaluating it on different distributions which our model does not encounter during training and we observe that our model is less susceptible to distribution shift compared to the other considered models. We support our findings by performing extensive result analysis. In ablation study we observe that on explicitly encapsulating the linguistic information along with semantic information improves the model's IID (independent and identically distributed) performance as well as OOD (out-of-distribution) generalization capability.

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

“…However, the optimal variants of TF-IDF are not studied well and deeply in exam question classification. It is imperative to use the optimal variant of TF-IDF as a baseline term weighting in order to compare effectively with the improved term weighting schemes or advanced models such as word embedding and deep neural network [42,35]. This is to ensure results are more conclusive.…”