Frank Ritter, associate professor of IST and psychology from Penn State's College of Information Sciences and Technology, led the research team.
For the study, they used the Soar programming language, which is designed to represent human knowledge, on a 20-trial circuit-troubleshooting task most recently done by 10 students at the University of Nottingham, UK.
All the participants in the study were required to identify faults in a circuit system after memorizing the organization of its components and switches.
The same process was repeated 20 times for each person, with the series of tests chosen randomly each time. The researchers recorded their choices and reaction times and compared with the computer model's results.
Just like the students, the computer model, called Diag, learned as it went through each test and developed the knowledge for completing the task quickly and efficiently.
"The model does not merely accurately predict problem-solving time for the human participants; it also replicates the strategy that human participants use, and it learns at the same rate at which the participants learn," said Ritter.
Many times, the model came within two to four seconds of predicting how long it would take each participant to solve the problem and it fit eight out of the 10 participants' problem-solving times very well.
Ritter said that the results outlined in the paper were consistent with previous trials, showing the development of regularity in the model.
"The project shows we can predict human learning on a fine-grained level. Everyone thinks that's possible, but here's an actual model doing it. The model provides a detailed representation of how a transfer works, and that transfer process is really what education is about," said Ritter.