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.

翻译：为减轻教师的重复性和复杂性工作，试卷生成技术已成为智能教育领域的重要课题，其目标是根据教师指定的评估标准自动生成高质量试卷。当前研究利用启发式算法优化难度系数、题目数量等经典目标约束以生成最优解。然而实际场景中，考虑同等重要的其他目标（如考试成绩分布、技能覆盖度）至关重要。此外，如何在大规模题库的庞大搜索空间中自动选取最优子集以生成高质量试卷的多目标解决方案，既是迫切需求又充满挑战。为此，我们巧妙设计了强化学习引导的多目标试卷生成框架MOEPG，同时优化难度系数、考试成绩分布与技能覆盖度三项学科特定目标。具体而言，为精确评估考生群体的技能熟练度，我们首先采用深度学习知识追踪模型建模考生与答题日志的交互信息，进而设计灵活的试卷Q网络——一种函数逼近器，自动筛选合适题目以更新试卷组成过程。随后，MOEPG将决策空间划分为多个子空间以更好引导试卷更新方向。通过两个真实数据集的广泛实验，我们证明了MOEPG在解决试卷生成场景中多重困境的可行性。