Under certain conditions in our simulation, predictive text could slow a user down by as much as eight words per minute. The deep red shows when predictive text is most effective an improvement of two words per minute compared to not using predictive text.
![keyboard predictive text keyboard predictive text](https://i1.wp.com/windowsloop.com/wp-content/uploads/2019/04/text-prediction-for-laptop-and-hadware-keyboard-03.png)
Kristensson and Müllners, 2021, Author providedĪbove the dashed line there’s an increase in net entry rate while below it, predictive text slows the user down. Although we don’t have data on the average perseverance, this seems like a reasonable estimate. So we fixed perseverance at five, meaning if there are no suitable suggestions after the user has typed five letters, they will complete the word without consulting predictive text further. While it would have been insightful to see how variation in perseverance affects the speed of typing with predictive text, even with a computer model, there were limitations to the amount of changeable data points we could include.
![keyboard predictive text keyboard predictive text](https://www.howtoisolve.com/wp-content/uploads/2015/09/Know-from-here-how-to-Hide-or-unhide-Predictive-text-on-iOS-9.jpg)
The vertical axis shows the effect of the user varying the type-then-look strategy from looking at word predictions even before typing (zero) to looking at predictions after one letter, two letters, and so on.Ī final latent strategy, perseverance, captures how long the user will type and check word predictions for before giving up and just typing out the word in full. The intuition here is that the more letters you type, the more likely the prediction will be correct. You might only look at the suggestions after typing the first three letters of a word, for example. The second strategy, “type-then-look”, governs how many letters the user will type before looking at word predictions. The horizontal axis in the visualisation below shows the effect of varying the minimum length of a word before the user seeks a word prediction, from two letters to ten. You might only look at predictions if you’re typing longer words, beyond, say, six letters – because these words require more effort to spell and type out. This means the user will tend to only look at predictions for words beyond a certain length. In our research, we looked at how different approaches to two of these strategies influence the usefulness of predictive text. These reflect the way the user engages with predictive text – or their strategies if you like. We fixed this at 0.45 seconds, again based on existing data.īeyond these, there’s a set of parameters which are less clear. The second fundamental parameter is the average time it takes a user to look at a predictive text suggestion and select it. We estimated this at 0.26 seconds, based on earlier research. The first is the average time it takes a user to hit a key on the keyboard (essentially a measure of their typing speed). We built a couple of fundamental parameters associated with predictive text performance into our simulation. We combined some of these conditions, or parameters, to simulate a large number of different scenarios and therefore determine when predictive text is effective – and when it’s not. In my most recent study on this topic, a colleague and I explored the conditions that determine whether predictive text is effective.
![keyboard predictive text keyboard predictive text](https://cdn.cultofmac.com/wp-content/uploads/2015/01/IMG_0582.jpg)
The idea seems to make sense: if the system can predict your intended word before you type it, this should save you time. It’s interesting to consider the poor correlation between predictive text and typing performance. This was slower than those who didn’t use an intelligent text entry method (35 words per minute) and significantly slower than participants who used autocorrect (43 words per minute). Participants who used predictive text typed an average of 33 words per minute. Participants were asked to copy sentences as quickly and accurately as possible. In 2019, my colleagues and I published a study in which we looked at mobile typing data from more than 37,000 volunteers, all using their own mobile phones. But this study only had 17 participants – and all used the same type of mobile device. A study published in 2016 found predictive text wasn’t associated with any overall improvement in typing speed.