Intelligent skill development
Iris is a new way to measure and evolve technology skills. She powers our assessment algorithms and guides you to the skills you need now. The more you learn about technology, the more Iris learns about you. She uses data to create a smarter, personalized skill development journey.
Machine learning
Iris uses a modified Bayes Network algorithm to predict assessment responses. She updates certainty, question difficulty and skill ratings as she collects feedback. Using a text mapping technique called term frequency–inverse document frequency (TF-IDF), Iris recommends content based on your Skill IQ.
Modern test theory
Iris builds on Item Response Theory (IRT) and applies Bayesian approximation. Learners are scored against a representative global estimate of all users of a given technology or skill, and questions are usable far sooner than with traditional IRT-based methods while maintaining similar levels of accuracy and reliability.
Bayesian statistics
Iris applies Bayesian statistics to assign scores to learners and characterize questions quickly and accurately. Using a modified Glicko scoring algorithm, Iris powers adaptive assessments; the algorithm applies Markov Chain Monte Carlo methods to evaluate each learner’s skill level, providing adaptively selected questions until a high level of certainty is attained.