Cognitive abilities that predict success in a computer-based training program.
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PURPOSE: The purposes of this study were (a) to identify cognitive abilities and other factors related to successful completion of training for computer-based tasks that simulated real jobs and (b) to create a brief assessment battery useful in assessing older adults for these kinds of jobs.
DESIGN AND METHODS: Participants from three age groups (young, middle-aged, and older) completed a battery of cognitive measures. They then trained on one of three computer-based tasks that simulated actual jobs and were asked to perform the tasks for 3 days. We recorded whether they completed training and whether and how well they did the tasks. In a series of logistic regressions, we evaluated the ability of a subset of cognitive measures drawn from a larger battery to predict participants' ability to successfully complete training and go on to task performance.
RESULTS: Results confirmed theory-based expectations that measures of domain knowledge, crystallized intelligence, memory, and psychomotor speed would predict success in computer-based activities. A brief battery was able to predict older adults' successful completion of training for one task but was less useful for another.
IMPLICATIONS: A brief battery of cognitive measures may be useful in evaluating individuals for job selection. Different measures are related to job-related criteria depending on task and group evaluated, although it was not possible to identify a reduced battery for one task. The specific cognitive abilities related to participants' success have implications for task and interface design for the elderly population.
Medical Specialties | Medicine and Health Sciences | Osteopathic Medicine and Osteopathy
Adult, Aged, Aptitude, Cognition, Computer-Assisted Instruction, Female, Forecasting, Humans, Male, Middle Aged, Task Performance and Analysis, User-Computer Interface
Ownby, Raymond L.; Czaja, Sara J.; Loewenstein, David A.; and Rubert, Mark, "Cognitive abilities that predict success in a computer-based training program." (2008). Faculty Articles. 72.