ExamGAN and Twin-ExamGAN for Exam Script Generation

Zhengyang Wu, Ke Deng, Judy Qiu, Yong Tang

Abstract: Nowadays, the learning management system (LMS) has been widely used in different educational stages from primary to tertiary education for student administration, documentation, tracking, reporting, and delivery of educational courses, training programs, or learning and development programs. Towards effective learning outcome assessment, the exam script generation problem has attracted many attentions recently. But the research in this field is still in its early stage. Two essential issues have been ignored largely by existing solutions. First, given a course, it is unknown yet how to generate an quality exam script which concurrently has (i) the proper difficulty level, (ii) the coverage of essential knowledge points, (iii) the capability to distinguish academic performances between students, and (iv) the student scores in normal distribution. Second, while frequently encountered in practice, it is unknown so far how to generate a pair of high quality exam scripts which are equivalent in assessment (i.e., the student scores are comparable by taking either of them) but have significantly different sets of questions. To fill the gap, this paper proposes ExamGAN (Exam Script Generative Adversarial Network) to generate high quality exam scripts, and then extends ExamGAN to T-ExamGAN (Twin-ExamGAN) to generate a pair of high quality exam scripts. Based on extensive experiments on three benchmark datasets, it has verified the superiority of proposed solutions in various aspects against the state-of-the-art. Moreover, we have conducted a case study which demonstrated the effectiveness of proposed solution in the real teaching scenarios.