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.
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.
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.
Introducing the new standard for measuring tech skills
The brains behind Pluralsight IQ
Iris is disrupting skill measurement for individuals, businesses and the world. She provides a single source of truth for evaluating skill level and competency.
As learners and businesses get smarter, so does Iris. With every assessment and course completed, Iris absorbs information about the state of technology skills. She collects feedback and adapts as learners seek skills in new technologies. She uses this data to inform your technology strategy and recommend what skills are needed to keep pace with change.