DeCoste Writing Protocol
Google for Education
Learning is For Life
Dyslexia & Dysgraphia
So, you’ve concluded that word prediction is an effective way to bridge the gap between thoughts and written expression. Now, how do you decide on a word prediction program? What are the distinguishing differences in word prediction programs? And further, what is good prediction worth? Would you ever compromise the quality of medical attention you seek for your family? Would you buy an automobile that tested “Poor” in consumer safety reports? What about air travel — would you be comfortable traveling in an obviously unsafe airplane? Reading and writing skills are just as critical and we need to consider them as such. Children with learning difficulties only open the window of effort on occasion — when that window opens we must be there with the best tools to ensure that they have a successful experience. We must look at word prediction with critical eyes if we are to offer the best possible opportunity for success. Over the years we received hundreds of questions about word prediction relating to grammar, topical vocabulary, phonetic spelling and the types of prediction available. We understand there is some confusion about functionality and supports of similarly named features.
Co:Writer® was produced and is continually updated in collaboration with Paul Schwejda and Judy McDonald, who are true pioneers and have been perfecting word prediction for over 30 years. They began their investigation into word prediction back in the late 70’s. In the early 80’s they produced “Predict It.” but memory on those old Apple II computers caused some limitations. When the Macintosh came to market, Co:Writer was born. The Macintosh presented the opportunity to develop word prediction with many enhanced features. Since Co:Writer was released in 1992, and technological advances have continued to be made, they have remained singularly devoted to the innovation, refinement, and perfection of Co:Writer’s prediction and it’s ability to support struggling writers.
This type of prediction is useful when filling out forms. If you begin to type, for example, the date “j-a-n-u,” the application predicts January xx, xxxx (the current date).
This type of prediction utilizes two and three-word pattern, and the frequency in which those two or three words appear together. There are three key issues with regard to this type of word prediction:
One of the building blocks at the foundation of Co:Writer’s prediction engine is called “Linguistic Word Prediction.” With Linguistic Word Prediction, Co:Writer knows the grammatical value of each word in its dictionaries. When Co:Writer learns new words collected from articles or student writing, it automatically assigns grammar to them. With grammar-based intelligence, Co:Writer can accurately predict words within the framework of valid sentence structures. It also gives flexibility to the words it learns by automatically predicting in multiple tenses and usages.
The foundation of Natural Language Processing (NLP) stems back to the 1950s, but it wasn’t until recently that machine learning was combined with large-scale real-world writing and human-to-machine interactions. This technology is at the foundation of automated assistants including Siri, Amazon’s Alexa, and Google Assistant. This technology continues to progress rapidly and while it’s not yet at the level where there is no distinction between machine and human, that gap is narrowing every year. NLP allows the word prediction engine to identify trigger words and phrases to better understand context so it can better predict appropriate words based on that topic.
Co:Writer’s new Neuron™ prediction engine follows the natural relationships of ideas and concepts in the brain. Co:Writer’s core word prediction engine is based on several core technologies—Natural Language Processing, Linguistic Word Prediction, Phonetic / Flexible Spelling, and Topic Dictionaries. The best of these technologies are combined using sophisticated algorithms based on real-world student writing and trillions of testing scenarios. The result is an elegantly simple-to-use word prediction engine specifically designed to free up ideas so they more naturally flow into writing—helping overcome writer’s block, word recall issues, and other writing barriers.
Topic Dictionaries are a functionality exclusive to Co:Writer where lists of words are grouped together by content areas that can be activated or deactivated manually when writing on topics containing complex topic-specific words. Rather than laboring over how to spell Pterodactyl or Tyrannosaurus, students can focus on writing for meaning and retelling their knowledge. Applying a Topic Dictionary increases students’ efficiency by getting to content-specific words in just one or two keystrokes. There are over 500 Topic Dictionaries built into Co:Writer. Additional Topic Dictionaries can be made automatically by either writing words into a Topic Dictionary, pasting entire articles in, or simply typing the name of the topic. In any of these cases, Co:Writer will automatically extract key vocabulary and create a Topic Dictionary.
Ask a student what they struggle with most when writing. Most of the time, they will say spelling. Co:Writer’s FlexSpell® provides every conceivable letter-pattern students will try in an attempt to spell out words. A great deal of assessment, using writing samples of students with developmental spelling difficulties, has been done to provide scaffolding for writers who struggle more severely. FlexSpell can be adjusted to work after just one letter is typed, for example, if a student types the letter “u” Co:Writer will predict the word “you”, or FlexSpell can be set to provide phonetic spellings only after two or three letters have been typed. FlexSpell was designed to remove the mechanical barriers that keep many students from expressing themselves and their knowledge through writing.
(you can sign up for a free Co:Writer trial to try these examples yourself) If you have access to word prediction alternatives you might try the following sentence patterns as a comparative test.
Three very mangy dogs ran down the street. (adj., adv., adj., noun, verb…etc.) (After typing three, very, mangy, you get plural noun choices, then you get plural verb tenses. This is the type of prediction you will NOT get with pattern prediction products.) All word prediction should be able to get simple sentences like, “I am happy.” or, “The fish is swimming.” with minimal keystrokes. If a student has to type fis for “fish”, the prediction is not good enough. As students learn to write richer sentences, Co:Writer goes beyond ‘simple’ by utilizing advanced linguistic prediction. It provides the critical modeling of word forms, subject-verb agreement and pronoun and article use.
Cn u txt me the infrmtn. (Can you text me the information) My sel fon rng. (My cell phone rang) The blk jragn flw ovr the gint lfnt. (The black dragon flew over the giant elephant.) R u hpy to ce me? (Are you happy to see me?—only a few keystrokes!) I no hw to nser the fon. (I know how to answer the phone)
Note—You will not need to type in all of the letters given above, but they are included as a point of reference. Try these types of spellings or, better yet, use your students’ writing samples to compare the prediction for yourself!
In addition, care was taken to make Co:Writer’s predictions appropriate for students. Unlike many Google search predictions which are often lewd because they’re based on searches from the general population, Co:Writer has built-in filters to screen out insensitive and inappropriate words.
Co:Writer is used by nearly two million users ranging from elementary through college-level. One doctoral candidate with a physical impairment used Co:Writer to write a doctoral thesis and went on to write a book.
Co:Writer can even be used on a variety of state standardized tests. The features of test mode are easy to use, secure and automatically align with state standards for use of accommodations. See More about Co:Writer in Test Mode.
Co:Writer Universal works with any application that accepts text such as:
The more an individual writes with Co:Writer, the more accurate the prediction becomes. This is because Co:Writer continually learns. And when topic dictionaries are added on topics of interest, Co:Writer provides the writer with an immense amount of support to kickstart writing and let it flow.
There are a number of options and preferences that can be fine-tuned. The Getting Started section of Co:Writer’s manual is a step-by-step tutorial that can be used as a quick reference or as a training resource. Additionally, product training, customer and technical support are available.
For more information on Co:Writer and to get a free trial, click here.