As they found it wasn't clear what kind of impact embedding dimension can have on the performance of DAS3H. Even though on every dataset model had lower performance when embedding dimension was set to 20 still, when the dimension was 5 sometimes it had better performance than when it was 0.

It was observed that using time window features(instead of regular skill wins and attempts counts) and different parameters for distinctive skills in $h_{\theta}$ increased AUC of the model. "Including both item-skill relationships and forgetting effect improves over models that consider one or the other."

Additionally, DAS3H can be well-suited for the adaptive scheduling. It can be useful for adaptively optimizing personalized skill practice schedule.