First, do no harm: automated detection of abusive comments in student evaluation of teaching surveys

ABSTRACT

Student evaluation of teaching (SET) surveys are the most widely used tool for collecting higher education student feedback to inform academic quality improvement, promotion and recruitment processes. Malicious and abusive student comments in SET surveys have the potential to harm the wellbeing and career prospects of academics. Despite much literature highlighting abusive feedback in SET surveys, little research attention has been given to methods for screening student comments to identify and remove those that may cause harm to academics. This project applied innovative machine learning techniques, along with a dictionary of keywords to screen more than 100,000 student comments made via a university SET during 2021. The study concluded that these methods, when used in conjunction with a final stage of human checking, are an effective and practicable means of screening student comments. Higher education institutions have an obligation to balance the rights of students to provide feedback on their learning experience with a duty to protect academics from harm by pre-screening student comments before releasing SET results to academics.

KEYWORDS:

• Student evaluation of teaching
• text analysis
• abusive comments
• course evaluation
• machine learning

Acknowledgements

The authors acknowledge both the contributions made by QUT students in providing feedback via the Student Voice Survey. We would also like to thank Dr Le Hoa Phan for her expert advice that informed our review of evaluation and Ashleigh Quaill, Sarah Stevens and Tina Graham for their support in implementing this screening process at QUT.

Conflict of Interest

The authors declare no potential conflict of interest.