  Group work, where students work on projects to overcome challenges together,
has numerous advantages, including learning of important transferable skills,
better learning experience and increased motivation. However, in many academic
systems the advantages of group projects clash with the need to assign
individualised marks to students. A number of different schemes have been
proposed to individualise group project marks, these include marking of
individual reflexive accounts of the group work and peer assessment. Here we
explore a number of these schemes in computational experiments with an
artificial student population. Our analysis highlights the advantages and
disadvantages of each scheme and particularly reveals the power of a new scheme
proposed here that we call pseudoinverse marking.
