  To reduce the repetitive and complex work of instructors, exam paper
generation (EPG) technique has become a salient topic in the intelligent
education field, which targets at generating high-quality exam paper
automatically according to instructor-specified assessment criteria. The
current advances utilize the ability of heuristic algorithms to optimize
several well-known objective constraints, such as difficulty degree, number of
questions, etc., for producing optimal solutions. However, in real scenarios,
considering other equally relevant objectives (e.g., distribution of exam
scores, skill coverage) is extremely important. Besides, how to develop an
automatic multi-objective solution that finds an optimal subset of questions
from a huge search space of large-sized question datasets and thus composes a
high-quality exam paper is urgent but non-trivial. To this end, we skillfully
design a reinforcement learning guided Multi-Objective Exam Paper Generation
framework, termed MOEPG, to simultaneously optimize three exam domain-specific
objectives including difficulty degree, distribution of exam scores, and skill
coverage. Specifically, to accurately measure the skill proficiency of the
examinee group, we first employ deep knowledge tracing to model the interaction
information between examinees and response logs. We then design the flexible
Exam Q-Network, a function approximator, which automatically selects the
appropriate question to update the exam paper composition process. Later, MOEPG
divides the decision space into multiple subspaces to better guide the updated
direction of the exam paper. Through extensive experiments on two real-world
datasets, we demonstrate that MOEPG is feasible in addressing the multiple
dilemmas of exam paper generation scenario.
