Predicting Cognitive Load of an Individual With Knowledge Gained From Others: Improvements in Performance Using Crowdsourcing

Understanding cognitive load is important due to its inherent implications across many different disciplines. This is, in general, a difficult task due to personal nature of data normally used to infer cognitive load. In addition, an individual changes over time and his/her pattern of data changes as well, which implies past data from an individual may not reliably predict the future cognitive load of the same individual. In this article, we show that utilization of data from other people (a.k.a. crowdsourcing) offers a significant improvement in classifier performance when predicting cognitive load. We reveal that the improvement is substantial compared to an individualistic model and is statistically significant.