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To reduce the repetitive and complex work of instructors, exam papergeneration (EPG) technique has become a salient topic in the intelligenteducation field, which targets at generating high-quality exam paperautomatically according to instructor-specified assessment criteria. Thecurrent advances utilize the ability of heuristic algorithms to optimizeseveral well-known objective constraints, such as difficulty degree, number ofquestions, etc., for producing optimal solutions. However, in real scenarios,considering other equally relevant objectives (e.g., distribution of examscores, skill coverage) is extremely important. Besides, how to develop anautomatic multi-objective solution that finds an optimal subset of questionsfrom a huge search space of large-sized question datasets and thus composes ahigh-quality exam paper is urgent but non-trivial. To this end, we skillfullydesign a reinforcement learning guided Multi-Objective Exam Paper Generationframework, termed MOEPG, to simultaneously optimize three exam domain-specificobjectives including difficulty degree, distribution of exam scores, and skillcoverage. Specifically, to accurately measure the skill proficiency of theexaminee group, we first employ deep knowledge tracing to model the interactioninformation between examinees and response logs. We then design the flexibleExam Q-Network, a function approximator, which automatically selects theappropriate question to update the exam paper composition process. Later, MOEPGdivides the decision space into multiple subspaces to better guide the updateddirection of the exam paper. Through extensive experiments on two real-worlddatasets, we demonstrate that MOEPG is feasible in addressing the multipledilemmas of exam paper generation scenario.

Reinforcement Learning Guided Multi-Objective Exam Paper Generation