I gave a very short presentation about using Google Images as a picture dictionary at a training at work recently. Google Images works as a picture dictionary because it is basically a picture corpus, and the image hits are ‘concordances’ that can be used like dictionary examples.
I gave examples using words such as ‘fuzzy’ and ‘rough’. Of course, students can look these up in a text-based dictionary, but even if they understand the entries they might still not know exactly how ‘fuzzy’ differs from, say, ‘hairy’, or which sense of ‘rough’ is intended in the phrase ‘rough neighborhood’. For these types of words visual data can be useful and is sort of a form of Data-driven Learning.
Visual data such as photographs can be processed very quickly, and for some learners may be more memorable. I find that Google Images works well with concrete and descriptive words. Unfortunately, the more abstract a word or phrase is, the less likely one is to get image hits that are going to be helpful in understanding the word/phrase.
But for many words, it’s great! It’s quick, simple, can be done on the fly or integrated into a planned activity. In the training session I showed how students could create a word profile for ‘rough’ by searching for ‘rough —‘, where I supplied some of the most common collocates for rough that reflect the various senses of the word.
Using Google Images as a picture dictionary/corpus is simple enough that most people who don’t consider themselves very tech-savvy could use it to good effect, I believe.