On the assistments dataset KTM training was much faster than DKT (deep knowledge tracing). It was observed that wins and fails (performance factory analysis) encoding improves performance a lot compared to encoding the number of attempts (additive factor model). When the bias was added for all the items the performance improved in the case of IRT (item response theory) but same can't be said about PFA (performance factory analysis). This is explained by the fact that the amount of items is large and adding difficulty parameter would be helpful in this case. For some of the datasets it was easy to identify skills but for others it was difficult which was caused by the fact that there were too few skills or only one KC was associated with the item. Increasing dimensions only slightly increased the performance when temporal datasets were used.