Counteracting exam cheating by leveraging configuration and recommendation techniques

Research output: Contribution to journalConference articlepeer-review


Exam cheating indicates behaviors of students to fraudulently achieve their desired grades through various forms, such as item harvesting, item pre-knowledge, item memorizing, collusion and answer copying, and answer checking from available sources. Such dishonesty behaviors become manifest in e-learning scenarios, where exams are often conducted via online assessment platforms without the physical supervision of proctors. In this paper, we propose an approach to counteract exam cheating based on configuration and recommendation techniques. Our approach allows examiners to configure questions and exams using feature models. We support the configuration of parameterized questions, which helps to generate a large number of exam instances. Besides, a content-based recommendation mechanism is integrated into the exam configuration process, which helps examiners to select questions that have not appeared in the latest exams.We also propose mock-ups to show how question and exam generation processes can be proceeded in a real exam generator system.

Original languageEnglish
Pages (from-to)73-80
Number of pages8
JournalCEUR Workshop Proceedings
Publication statusPublished - 2021
Event23rd International Configuration Workshop: ConfWS 2021 - Conference Center of Siemens City Vienna (Siemensstraße 90, 1210 Wien), Vienna, Austria
Duration: 16 Sept 202117 Sept 2021

ASJC Scopus subject areas

  • Computer Science(all)

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