No one who ever found it too difficult or embarrassing to try to stop working a project or relationship or even a joke that clearly wasn't working knows that forewarned is not forarmed.
Forwarned may be pre-corrected, however.
A researcher at the University of Arizona claims to be able to predict with 80 percent accuracy when someone is about to make a mistake in a standardized math test.
Computer-science Ph.D. candidate Federico Cirett noticed students at UA who were still learning fluent English had more trouble answering questions than those who were fluent in English, even when the questions were on a completely different topic and difficulty expressing themselves was not a factor.
Being a good geek, Cirett knew there must be a fubared process behind that difficulty.
He arranged to study it by attaching an electroencephalograph to the heads of volunteers in order to estimate their level of engagement and cognitive workload while they tried to answer math questions.
The result was a series of brain-wave activity patterns that indicated when the brain was approaching limits on the amount of processing it could handle at any one time.
That workload includes all kinds of problem-solving, including struggling to express thoughts in a language only partly understood as well as math problems.
In fact, the overall workload and likelihood of making a mistake increases with emotion as well as processing requirements according to a paper published in the journal Cerebral Contex last October.
In that study researchers taking images of the brains of volunteers preparing to do ordinary schoolwork noticed an increase in activity in the frontal and parietal lobes responsible for regulating negative emotions and keeping our attention fixed on the task at hand.
At even the mention of a math problem, that increase in some volunteers increased the workload of the brain, increased the level of confusion and made mistakes far more likely than in calm subjects. s
Cirett wrote a series of algorithms that can identify the patterns in a volunteer's thinking that are likely to result in an error 20 seconds or so before it's made.
The goal is to create assistive systems that will intervene before or just as a student is beginning to struggle, making the process of learning quicker and more efficient for both the teacher and student.
"If we can detect when they are going to fail, maybe we can change the text or switch the question to give them another one at a different level of difficulty, but also to keep them engaged," Cirett said. "Brain wave data is the nearest thing we have to really know when the students are having problems."
Read more of Kevin Fogarty's CoreIT blog and follow the latest IT news at ITworld. Follow Kevin on Twitter at @KevinFogarty. For the latest IT news, analysis and how-tos, follow ITworld on Twitter and Facebook.