HeTROPY: Explainable learning diagnostics via heterogeneous maximum-entropy and multi-spatial knowledge representation

Highlights：

• Autonomously diagnosing learning problems in e-learning systems can be challenging due to the lack of teacher resources (teachers) in e-learning. This work proposes to tackle the explainable learning diagnostics problem using attention-based explanation mechanisms by performing target-source relation prediction. The findings are adoptable to various types of e-learning systems to gain insights into their learner states and diagnose their learning problems.

• This paper identifies the importance to use the ‘close relatives of knowledge to generate prediction decisions in a knowledge tracing model. We propose a Heterogeneous Attention Relative Detector and a Maximum Entropy Regularizer to detect those relatives, i.e. knowledge relation discovery.

• We propose a multi-spatial representation of knowledge in expressing knowledge relations in finer-granularity and low-dimensionality, which can be readily generalized to other data-driven educational tasks.

• We provide a different perspective on knowledge graph completion and/or construction. In this perspective, we exploit the interaction data to uncover inner knowledge relations (links) where there is no existing relation data to learn. Our method is effective in settings where the knowledge space is relatively small while the interaction space is very large.