Reinforcement Learning Guided Multi-Objective Exam Paper Generation

by   Yuhu Shang, et al.

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.


page 1

page 2

page 3

page 4


A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-...

Multi-Objective Congestion Control

Decades of research on Internet congestion control (CC) has produced a p...

PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm

Many real-world problems involve multiple, possibly conflicting, objecti...

Heuristic Search for Multi-Objective Probabilistic Planning

Heuristic search is a powerful approach that has successfully been appli...

Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

Computerized Adaptive Testing (CAT) is emerging as a promising testing a...

Multi-objective evolution for Generalizable Policy Gradient Algorithms

Performance, generalizability, and stability are three Reinforcement Lea...

Optimal Weighting for Exam Composition

A problem faced by many instructors is that of designing exams that accu...

Please sign up or login with your details

Forgot password? Click here to reset