Numeracy is a key competency for living in our modern knowledge society. Therefore, it is essential to support numerical learning from basic to more advanced competency levels. From educational psychology it is known that learning is most effective when the respective content is neither too easy nor too demanding in relation to learners' prerequisites. However, so far it is difficult to assess individual's cognitive workload independently from performance to adapt learning environments accordingly. In the present study, we aim at identifying learners' cognitive workload induced by addition tasks of varying difficulty using electroencephalography (EEG). To this end, a classifier using specific features in the EEG-signal is trained to differentiate between different levels of task difficulty significantly above chance level and with high consistency over all participants. Importantly, our model even allows for the prediction of cognitive demands induced by the addition tasks in a cross-participant approach. Closer inspection of the crucial EEG features indicates that oscillations in the theta and alpha band recorded from parietal electrodes are most reflective of current task difficulty. In summary, we are able to differentiate cognitive workload of participants independently from performance based on data of only a small number of electrodes. This suggests that a reduced EEG-setup combined with cross-participant classification may be a feasible approach to assess learners' cognitive workload.