  Textbook Question Answering (TQA) is a task that one should answer a
diagram/non-diagram question given a large multi-modal context consisting of
abundant essays and diagrams. We argue that the explainability of this task
should place students as a key aspect to be considered. To address this issue,
we devise a novel architecture towards span-level eXplanations of the TQA
(XTQA) based on our proposed coarse-to-fine grained algorithm, which can
provide not only the answers but also the span-level evidences to choose them
for students. This algorithm first coarsely chooses top $M$ paragraphs relevant
to questions using the TF-IDF method, and then chooses top $K$ evidence spans
finely from all candidate spans within these paragraphs by computing the
information gain of each span to questions. Experimental results shows that
XTQA significantly improves the state-of-the-art performance compared with
baselines. The source code is available at
https://github.com/keep-smile-001/opentqa
