Key features of Tucareers Framework
This is a follow up to our last post detailing the 'Success Analytics' framework. In this post we highlight some of the value added features which Tucareers.com provides which can facilitate your education and career related decisions.
Multi trait assessment evaluates an individual on multiple dimensions and then statistically compare suitability of ones profile across the world of work to recommend the best options. The multi trait assessment as per research is superior in approach to recommendations based on a single dimension (e.g. interest, skill or work style / personality as these have limitations in their usage (e.g. people with a collectivist mindset will choose career’s less based on interests as provided for in the Holland theory than a person with an individualistic mindset). The Multi Trait Assessment includes the assessment of abilities, interest, work values and work style (personality traits) and based on a predictive framework includes projections of career satisfaction; life time earnings and tenures for the different occupations recommended. A phased approach, helps users to further narrow down / expand their options.
Given the wide taxonomy used, the site can be used by both career starters and career professionals (working professional, who wants to switch careers or explore jobs in a new industry etc.). For the career professional, additional feature of experience-matching is included which recommends related careers wherein the relevance of your earlier work experience (context, tasks and activities) is also considered and scores are reported for the different options recommended. Given that some of the traits do vary as per age a user can come back to take the assessments again.
Career intervention literature highlights the importance of providing World of Work information to a career decision maker for exploration before he makes his / her choice. We provide rich navigation and search tools, which can help educate a decision maker in her exploration journey. Given that occupations in areas other than fields one majors/graduates in, significantly lowers ones earning potential, we provide cross linkage of careers with education and courses (courses are based on the comprehensive CIP taxonomy).
For advanced usage we integrate cultural evaluation and a group decision making system which helps get inputs from experts and significant others so that recommendations generated also considers these important aspects (esp. important with people from a collectivist society like India). A machine learning algorithm helps in recommending the best options.