Autonomous learning diagnostics, where the students’ strengths and weaknesses are disclosed from their observed performance data, is a challenging task in e-learning systems. Current student knowledge models can alleviate some of the problems in learning (i.e. predicting student performance) but they neglect learning diagnostics, which is based on causal reasoning. To this end, we propose a novel heterogeneous attention interpreter with a maximum entropy regularizer on top of a student knowledge model to achieve explainable learning diagnostics. Our model segregates the impact of the homogeneous knowledge points, while promoting the heterogeneous relatives by maximizing their chance to contribute to the prediction. We also propose a multi-spatial knowledge representation that is readily generalizable to other data-driven educational tasks. Extensive experiments on real-world datasets reveal that the proposed method is able to enhance the model's explanatory power, hence increases the trustworthiness towards learning diagnostics. It also brings notable improvement in accuracy in the student performance prediction task. The findings in this paper are adoptable to various types of e-learning systems to assist teachers to gain insights into student learning states and diagnose learning problems.