1 research outputs found
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
Assessment of job performance, personalized health and psychometric measures
are domains where data-driven and ubiquitous computing exhibits the potential
of a profound impact in the future. Existing techniques use data extracted from
questionnaires, sensors (wearable, computer, etc.), or other traits, to assess
well-being and cognitive attributes of individuals. However, these techniques
can neither predict individual's well-being and psychological traits in a
global manner nor consider the challenges associated to processing the data
available, that is incomplete and noisy. In this paper, we create a benchmark
for predictive analysis of individuals from a perspective that integrates:
physical and physiological behavior, psychological states and traits, and job
performance. We design data mining techniques as benchmark and uses real noisy
and incomplete data derived from wearable sensors to predict 19 constructs
based on 12 standardized well-validated tests. The study included 757
participants who were knowledge workers in organizations across the USA with
varied work roles. We developed a data mining framework to extract the
meaningful predictors for each of the 19 variables under consideration. Our
model is the first benchmark that combines these various instrument-derived
variables in a single framework to understand people's behavior by leveraging
real uncurated data from wearable, mobile, and social media sources. We verify
our approach experimentally using the data obtained from our longitudinal
study. The results show that our framework is consistently reliable and capable
of predicting the variables under study better than the baselines when
prediction is restricted to the noisy, incomplete data