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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. • 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. [ABSTRACT FROM AUTHOR]