Bidirectional RNN with attention models real-time user knowledge from question-response sequences to predict correctness, outperforming baselines especially for new users on a large TOEIC mobile app dataset.
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Interpretability of the underlying AI representations is a key raison d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. Over thirty years of research in ITS (also known as AI in Education) produced important work, which informs about how AI can be used in Education to best effects and, through the OLM research, what are the necessary considerations to make it interpretable and explainable for the benefit of learning. We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess
Bidirectional RNN with attention models real-time user knowledge from question-response sequences to predict correctness, outperforming baselines especially for new users on a large TOEIC mobile app dataset.