Analysis: research found that predictive text is unlikely to improve typing
performance for the average user and may be slowing them down
By <https://theconversation.com/profiles/per-ola-kristensson-1282225> Per
Ola Kristensson,
<https://theconversation.com/institutions/university-of-cambridge-1283>
University of Cambridge
Typing is one of the most common things we do on our mobile phones. A recent
survey suggests that millenials spend
<https://www.provisionliving.com/blog/smartphone-screen-time-baby-boomers-an
d-millennials/> 48 minutes each day texting, while boomers spend 30 minutes.
Since the advent of mobile phones, the way we text has changed. We've seen
the introduction of autocorrect, which corrects errors as we type, and word
prediction (often called predictive text), which predicts the next word we
want to type and allows us to select it above the keyboard.
Functions such as autocorrect and predictive text are designed to make
typing faster and more efficient. But research shows this isn't necessarily
true of predictive text. A <https://dl.acm.org/doi/10.1145/2858036.2858305>
study published in 2016 found predictive text wasn't associated with any
overall improvement in typing speed. But this study only had 17 participants
and all used the same type of mobile device.
In 2019, my colleagues and I published
<https://doi.org/10.1145/3338286.3340120> a study in which we looked at
mobile typing data from more than 37,000 volunteers, all using their own
mobile phones. Participants were asked to copy sentences as quickly and
accurately as possible.
Participants who used predictive text typed an average of 33 words per
minute. 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).
It's interesting to consider the poor correlation between predictive text
and typing performance. The idea seems to make sense: if the system can
predict your intended word before you type it, this should save you time.
In my most <https://doi.org/10.1145/3411764.3445566> recent study on this
topic, a colleague and I explored the conditions that determine whether
predictive text is effective. 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.
We built a couple of fundamental parameters associated with predictive text
performance into our simulation. 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 estimated this at 0.26 seconds, based on
<https://dl.acm.org/doi/10.1145/2470654.2466180> earlier research. The
second fundamental parameter is the average time it takes a user to look at
a predictive text suggestion and select it. We fixed this at 0.45 seconds,
again based on <https://dl.acm.org/doi/10.1145/1240624.1240723> existing
data.
Research has shown using predictive text doesn't improve typing speed. But
why?
Beyond these, there's a set of parameters which are less clear. These
reflect the way the user engages with predictive text or their strategies,
if you like. In our research, we looked at how different approaches to two
of these strategies influence the usefulness of predictive text.
The first is minimum word length. This means the user will tend to only look
at predictions for words beyond a certain length. 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. 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.
The second strategy, "type-then-look", governs how many letters the user
will type before looking at word predictions. You might only look at the
suggestions after typing the first three letters of a word, for example. The
intuition here is that the more letters you type, the more likely the
prediction will be correct. 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.
A 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. 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.
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. Although we don't have data
on the average perseverance, this seems like a reasonable estimate.
What did we find?
When predictive text is most effective; an improvement of two words per
minute compared to not using predictive text. Under certain conditions in
our simulation, predictive text could slow a user down by as much as eight
words per minute.
Where you get the best results from predictive text. This occurs when word
predictions are only sought for words with at least six letters and the user
looks at a word prediction after typing three letters.
So, for the average user, predictive text is unlikely to improve
performance. And even when it does, it doesn't seem to save much time. The
potential gain of a couple of words per minute is much smaller than the
potential time lost.
It would be interesting to study long-term predictive text use and look at
users' strategies to verify that our assumptions from the model hold in
practice. But our simulation reinforces the findings of previous human
research: predictive text probably isn't saving you time and could be
slowing you down.
<https://theconversation.com/profiles/per-ola-kristensson-1282225> Per Ola
Kristensson is Professor of Interactive Systems Engineering at the
<https://theconversation.com/institutions/university-of-cambridge-1283>
University of Cambridge.
This article was originally published by <https://theconversation.com/> The
Conversation.
RTÉ Technology.
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