Detecting student copying in a corpus of science laboratory reports

This case study is an evaluation of generic, general-purpose plagiarism detection systems applied to a specific domain and task: detecting intra-class student copying in a corpus of Biomedical Science laboratory reports. From the outset, our project had the practical, pragmatic aim to find a workable solution to a specific problem. Biomedical Science undergraduates learn experimental methods by working through a series of laboratory experiments and reporting on their results. These laboratory reports are “peer-reviewed” in large classes, following a prescribed marking scheme; as the reports are effectively marked by other students rather than by a single lecturer, there is an opportunity for an unscrupulous student to avoid having to carry out and report on an experiment, by simply copying another student’s report. To reduce this temptation, the Biomedical Science director of teaching, Paul Gent, approached Eric Atwell of the School of Computing and Clive Souter of the Centre for Joint Honours in Science, to look at ways to compare laboratory reports automatically, and flag candidates with signs of copying. We were joined by Julia Medori, forensic linguist from Trinity College Dublin, who developed and evaluated a range of possible solutions.