diff --git a/avalanche/training/plugins/ewc.py b/avalanche/training/plugins/ewc.py
index 255e686ec..882654bd6 100644
--- a/avalanche/training/plugins/ewc.py
+++ b/avalanche/training/plugins/ewc.py
@@ -121,6 +121,7 @@ def after_training_exp(self, strategy, **kwargs):
             strategy.experience.dataset,
             strategy.device,
             strategy.train_mb_size,
+            kwargs["num_workers"],
         )
         self.update_importances(importances, exp_counter)
         self.saved_params[exp_counter] = copy_params_dict(strategy.model)
@@ -129,7 +130,8 @@ def after_training_exp(self, strategy, **kwargs):
             del self.saved_params[exp_counter - 1]
 
     def compute_importances(
-        self, model, criterion, optimizer, dataset, device, batch_size
+        self, model, criterion, optimizer, dataset, device, batch_size,
+        num_workers
     ):
         """
         Compute EWC importance matrix for each parameter
@@ -152,12 +154,15 @@ def compute_importances(
 
         # list of list
         importances = zerolike_params_dict(model)
+
         collate_fn = (
             dataset.collate_fn if hasattr(dataset, "collate_fn") else None
         )
         dataloader = DataLoader(
-            dataset, batch_size=batch_size, collate_fn=collate_fn
+            dataset, batch_size=batch_size, collate_fn=collate_fn,
+            num_workers=num_workers
         )
+
         for i, batch in enumerate(dataloader):
             # get only input, target and task_id from the batch
             x, y, task_labels = batch[0], batch[1], batch[-1]