The impact of the embedding dimension on the performance of the DAS3H model is variable. Across evaluated datasets, setting the embedding dimension to 20 consistently resulted in lower performance, while a dimension of 5 sometimes yielded better performance than a dimension of 0. Furthermore, using time window features—rather than regular skill wins and attempt counts—alongside distinct parameters for individual skills in $h_{\theta}$ increased the model's Area Under the Curve (AUC). Incorporating both item-skill relationships and the forgetting effect improves performance compared to models that consider only one of these factors, making DAS3H well-suited for adaptively optimizing personalized skill practice schedules.