Learning data teaching strategies via knowledge tracing

Teaching plays a fundamental role in human learning. Typically, a human teaching strategy involves assessing a student’s knowledge progress for tailoring the teaching materials to enhance the learning progress. A human teacher can achieve this by tracing a student’s knowledge over essential learning concepts in a task. Albeit, such a teaching strategy is not well exploited yet in machine learning as current machine teaching methods tend to directly assess the progress of individual training samples without paying attention to the underlying learning concepts in a learning task. In this paper, we propose a novel method, called Knowledge Augmented Data Teaching (KADT), which can optimize a data teaching strategy for a student model by tracing its knowledge progress over multiple learning concepts in a learning task. Specifically, the KADT method incorporates a knowledge tracing model to dynamically capture the knowledge progress of a student model in terms of latent learning concepts. We further develop an attention-pooling mechanism to distill knowledge representations of a student model with respect to class labels, which enables to develop a data teaching strategy on critical training samples. We have evaluated the performance of the KADT method on four different machine learning tasks, including knowledge tracing, sentiment analysis, movie recommendation, and image classification. The KADT method consistently outperforms the state-of-the-art methods on all these tasks.