I would think that a multi-level/hierarchical/mixed GLM would be an interesting approach to their data. Multilevel modeling assumes that there is correlation between observations that are inside the same "level". This is in stark comparison to regular GLM (even one with dummy variables to represent categories), which assumes that all observations are 100% independent.
E.g. in a model that predicts students' GPA, you could divide your data into a hierarchy consisting of, at the highest level, geographic area, followed by high school, maybe followed by teacher. In that model, the correlation between students who are in the same state, the same school or in the same classroom would be accounted for. You could even go as deep as at an individual level if you have >1 observation per student.
In addition to regular predictive variables, judg.me could probably use their weblogs to group people's judgement scores by country of origin and by individuals, among other possibilities.
E.g. in a model that predicts students' GPA, you could divide your data into a hierarchy consisting of, at the highest level, geographic area, followed by high school, maybe followed by teacher. In that model, the correlation between students who are in the same state, the same school or in the same classroom would be accounted for. You could even go as deep as at an individual level if you have >1 observation per student.
In addition to regular predictive variables, judg.me could probably use their weblogs to group people's judgement scores by country of origin and by individuals, among other possibilities.