Very often the direct translation of chunks within a SentenceBuilder into English can produce L1 English sentences that are far from ideal.

Here's a German example:

In this example the English translation for the sentence (e.g. I ate ice cream) has been split across the first SB row so that it matches the German, as we would expect. In fact, though, the sentences generated by the SB are, for example:

Ich habe Eis gegessen >> I ice cream ate
Du hast Fußball gespielt >> You football played
Sie hat eine Tour gemacht >> She a tour did

This is logical and inevitable if the language is entered into the SB in this way and the sentences are not transformed.

So how can we improve on this?

"Dodgy" English translations

First, I'm going to suggest that we change the language that is in the SB itself, so that it represents an even more direct translation of the German in each cell of the SB table. Why? Because, personally, I prefer to avoid (as far as possible -- I'm sure you will find examples in the Premium resources where I have not adhered to this rule...) presenting students with false equivalences such as:

Ich habe = I
Er hat = He
gegessen = ate

Instead, I prefer to present the language like this:

Here the German remains unchanged (because the whole structure of the SB is based on the German structure of course!), but the English translations of each chunk are more accurate.

And the sentences generated are even more literal:

Ich habe Eis gegessen >> I have ice cream eaten
Du hast Fußball gespielt >> You have football played
Sie hat eine Tour gemacht >> She has a tour done

This is starting to look much more like the "dodgy English" that is favoured by many language teachers because it helps students to see / remember the way in which the L2 is structured. And I personally think that this is a much better way of presenting the language within the SentenceBuilder, precisely because this "dodgy English" makes it clear to students what each part of the German says.

I would still prefer to transform the sentences that are used for the activities though, so that they say:

Ich habe Eis gegessen >> I ate ice cream
Du hast Fußball gespielt >> You played football
Sie hat eine Tour gemacht >> She did a tour

Transforming word order

The best way (that I have found so far...) of applying word order changes of this kind is to add a marker to the chunks that you wish to transform:

You can see that I've added an additional character before each of the English past participles in the right-hand column. I've chosen to use a · decimal point character because it is small and unobtrusive within the SB, and because it isn't a character that is likely to crop up naturally in any of the sentences.

Once this marker is added, it makes the transformations much easier to apply. Here is an example of how we deal with "·eaten":

·eaten|have>>ate
·eaten|has>>ate
#·eaten>>

The first one says: If the sentences to be transformed contains the text "·eaten", transform "have" to "ate". The second one does the same thing but with "has". The result after these first 2 transformations is sentences such as this:

I ate ice cream ·eaten

So that's why we have the 3rd transformation in our list above, which removes " ·eaten" from our sentences. It essentially says: Transform " ·eaten" to nothing, leaving us with our sentence "I ate ice cream". (Note the # in transformation 3, which is used in the transformations popup to maintain the space at the start of the transformation line. This is necessary because, without it, we'd end up leaving a space at the end of our sentence, which can cause problems in some activities.)

Below is the full list of transformations for the above SentenceBuilder, which I've copied and pasted here directly from the Transformations popup:

·eaten|have>>ate
·eaten|has>>ate
·played|have>>played
·played|has>>played
·done|have>>did
·done|has>>did
#·eaten>>
#·played>>
#·done>>
·>>

(The last one removes the · decimal point from all sentences. There won't actually be any sentences containing this point, but note that the same transformations are also applied to all of the vocab chunks. And for this resource, I decided not to define custom vocab chunks, so the vocab chunks are based on the contents of each cell. Without the last transformation, we'd have chunks such as "gegessen = ·eaten". By adding that transformation, we ensure that the point is removed.)

But what's the point of the point?

Good question. The point allows you to remove the marked word without affecting other examples of the same word in the sentence that you are wanting to transform. For example, imagine the sentence "Ich habe Fußball gespielt". The "raw" output from the SB would be:

I have football played

After the first 2 transformations (based on "have" and "has") we'd end up with:

I played football ·played

The · point allows us to add our 3rd transformation (#·played>>) without affecting the first example of "played" in the sentence.

This is a very simple example of a principle that can be applied to all sorts of SentenceBuilder content in order to make the SB generate exactly what you want it to generate in both languages: L1 and L2. I'll provide more examples in other posts, including ones where a marker is added to the list of transformations rather than to the SB itself :)