Differences between Test & Score results and calculated by Confusion Matrix (accuracy, sensitivity and specificity)
Test on train data uses the whole data set for training and then for testing. This method practically always gives wrong results.


So, yes, in this case, the model is learned and tested on the same data set. In most cases, the model learned this way will be overfitted and will perform poorly on unseen data (e.g., your testing dataset).


  Test on test data: the above methods use the data from Data signal only. To input another data set with testing examples (for instance from another file or some data selected in another widget), we select Separate Test Data signal in the communication channel and select Test on test data.


So, the answer to the second question is also "yes:" The model is trained on the data labeled Training in you diagram and then tested on the Testing data. The "quality" of the data and the way it was split in the training and testing set will influence the performance of the model.

Orange documentation on "Test &amp; Score":