At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners. On one hand, most learner models are susceptible to under-estimating ND ability since confounding contexts cannot be held accountable (eg consider dyslexia and text-heavy assessments), and on the other, few (if any) existing datasets are suited for appraising model and data bias in ND contexts. In this paper we attempt to model the relationships between context (delivery and response types) and performance of ND students with zero-inflated learner models. This approach facilitates simulation of several expected ND behavioural traits, provides equitable ability estimates across all student groups from generated datasets, increases interpretability confidence, and can significantly increase the quality of learning opportunities for ND students. Our approach consistently out-performs baselines in our experiments and can also be applied to many other learner modelling frameworks.

翻译：目前,教育数据采矿界缺乏确保对新潮(ND)学习者进行公平能力估计所需的许多工具,一方面,大多数学习者模型容易低估ND能力,因为无法追究混乱环境的责任(例如考虑阅读障碍和文本重评估),另一方面,现有数据集很少(如果有的话)适合在ND环境中评估模型和数据偏差。在本文件中,我们试图模拟环境(交付和反应类型)与零充气学习者模型的ND学生业绩之间的关系。这一方法有助于模拟若干预期的ND行为特征,从生成的数据集中为所有学生群体提供公平的能力估计,提高可解释性,并大大提高ND学生的学习机会质量。我们的方法始终超越了我们实验中的基线,还可以适用于许多其他学习者建模框架。