Team Tools: Scribe

This was inspired by our daughter's SLP.  To track her progress the school team manually took data (depicted below).  She called it Scribe Data:

The challenge of meaningfully tracking language progress in AAC has always intrigued me. Language is inherently fluid; words fall in and out of use as tastes and interests evolve. For example, Quinn might be fixated on "hot dog" one week, only for "guacamole" to become her new favorite the next. A simple graph of "hot dog" frequency would misleadingly show a decline after that first week, hardly a fair representation of her overall language growth.

Another complexity is that users, like Quinn, often cycle through their known vocabulary, especially when it's still developing. She might verbalize all ten words she knows before communicating her specific want. This means that if we teach "hat" one week and "shoe" the next, "hat" could be forgotten if it's not consistently revisited. These dynamics illustrate why raw word counts alone often fail to capture true progress.

It became clear there had to be a better, more visual way to represent language development so progress could be clearly seen and understood by everyone. As a self-professed data nerd, I was eager to take on this challenge.

Our solution? We started exploring visualizations that could represent more than just frequency, leading us to the versatility of pie charts.

Our team's primary goal is to see Quinn expand the variety of words she uses. We track this with a pie chart where more segments mean more unique words used during a period, which indicates language growth.

At any time, you can generate a chart reflecting her language over the past month. The principle is simple: if there are more segments in the current chart compared to a previous one, her active vocabulary is growing.

For example, if 'hot dog' was a frequently used unique word last month and is replaced by 'guacamole' this month (and the total number of other unique words remains the same), the total number of segments would stay the same. However, if Quinn starts using additional new and different words, the number of segments will increase, visually demonstrating her vocabulary expansion.

Beyond simply tracking unique words, we've introduced further refinements to help you understand language development more deeply:

• Grouping by Communicative Function: We now categorize the vocabulary data based on its communicative function. This means you can see not just which words are being used, but how they're being employed—for example, to make requests, share comments, ask questions, engage in social routines, or protest. This helps illustrate the different ways language is being used functionally.

• Filtering by Word Frequency: To focus on more consistently used vocabulary and reduce the impact of 'outlier' words (those appearing only once or twice, perhaps fleetingly or by chance), we've added a frequency filter. This allows you to exclude words used less than five times within the selected period, providing a clearer picture of the more established, active vocabulary.

• Filtering by Sentence Word Length:  To specifically track progress in sentence construction, we've also added filters based on utterance length (number of words per sentence). This was important because we wanted to clearly see, for example, how many 2-word sentences Quinn was forming compared to her 1-word utterances. This gives us another concrete way to measure her language development.

Our 'Scribe Data' feature, accessible online anytime, relies on manual input for a crucial reason: to capture the full spectrum of communication. This human-led approach is essential for logging:

• Nuanced details like communicative function or prompt levels.
• Communication occurring through other modalities, such as verbal speech or sign language. 

While manual, we've optimized the input methods for speed and efficiency, allowing you to record these comprehensive insights with ease.