An embedded approach to plagiarism detection using the TeSLA e-authentication system

Plagiarism continues to remain an ever present issue throughout academia. It is an anathema to scholarly enterprise, where the proper attribution of the work of others is of fundamental importance. Teaching students the importance of citing and referencing the work of others, and how to correctly do so, is therefore an important role for academic institutions. It is insufficient to teach these things without assessing students’ learning. Effective and accessible tools that can assist in teaching and assessment are sought and are increasingly being developed.
This paper describes a new tool designed to assess levels of plagiarism in students’ submitted work and the initial efforts in comparing it to established tools. TeSLA is an EU funded project that brings eighteen partners together for the development of an embedded suite of tools to deliver the seamless e-authentication of students as they complete online assessments. Within the suite is a plagiarism detection tool that analyses documents and text on submission and provides immediate output.
We show that the TeSLA plagiarism detection tool highlights potential collusion, a form of plagiarism. Also that the embedded nature of the TeSLA system could be used to improve constructive alignment between teaching and assessment within modules.