BloomNet：一个基于Transformer的鲁棒Bloom学习结果分类模型
BloomNet: A Robust Transformer based model for Bloom's Learning Outcome Classification
摘要：
布卢姆分类法是一种将教育学习目标分为认知、情感和心理三个层次的常用范式。根据Bloom分类法的不同认知水平设计课程学习结果对于优化教育方案至关重要。通常，机构的管理员手动完成将CLO和考试问题映射到Bloom分类级别的繁琐工作。为了解决这个问题，我们提出了一个名为BloomNet的基于转换器的模型，该模型捕获语言以及语义信息来对课程学习成果（CLO）进行分类。我们将BloomNet与一组不同的基本基线和强基线进行了比较，我们观察到我们的模型比所有实验基线都表现得更好。此外，我们还通过在我们的模型在训练过程中没有遇到的不同分布上对其进行评估来测试BloomNet的泛化能力，并且我们观察到，与其他考虑的模型相比，我们的模型对分布偏移不太敏感。我们通过进行广泛的结果分析来支持我们的发现。在消融研究中，我们观察到，显式封装语言信息以及语义信息，提高了模型的IID（独立同分布）性能以及OOD（分布外）泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
