  Bloom 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
Taxonomy. Usually, administrators of the institutions manually complete the
tedious work of mapping CLOs and examination questions to Bloom 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 a 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 generalization
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 on IID (independent and
identically distributed) performance as well as OOD (out-of-distribution)
generalization capability.
