Selectively anonymous rankings: Design, analysis and impact on computer science students
Simon Spacey · 2014
This paper presents a method to deliver course rankings that maintain student confidentiality while still allowing students to selectively prove their position in a class to others if they wish. The method's selective anonymity is implemented through a secure hashing algorithm that is designed to protect student privacy even where a student's name, their student ID, their project teams and the student's ranks for other work items are known. The paper includes results showing student perceptions of the approach and the impact on their performance for a second year Computer Science course at the University of Waikato in New Zealand.