Verifying student identity in oral assessments with deep speaker

Abstract

Contract cheating, a form of academic misconduct in which students outsource assessment activities to third parties, is a topic of concern among educators. As similarity-detection systems are ineffective at detecting contract cheating, some institutions have turned to intensely criticised proctoring systems, however student and educator bodies report high costs and privacy concerns. Oral assessment is an alternative assessment approach that provides valuable interpersonal and communication skills in graduates and can naturally help detect and deter cheating. However, oral assessment is typically time-consuming, and in larger courses, it is challenging to validate respondents' identity. Advancements in machine learning approaches can scale time-consuming tasks that previously required prohibitive educator effort. One such system, Deep Speaker, is a speaker identification and verification system that can verify if two audio samples resemble speech from the same person with high accuracy. This paper presents an innovative tool that integrates an online oral assessment tool, Real Talk, with Deep Speaker. This proposed system facilitates scalable student-tutor discussions while providing longitudinal student identity validation with minimal cost and impact for institutions and addressing student privacy concerns. We evaluated the system and showed that student audio responses collected via oral discussion tools are suitable for verification. We then discuss the impact our system may have when applied in higher education. We posit that institutions can use such approaches to detect cases of contract cheating, enhance learning outcomes, and pave the way for more student-friendly assessment and discussion models in online education.