Building Blocks of the Tucareers framework

The Tucareers career assessment framework overcomes the issues and improvement opportunities we had identified in the previous post

Some key aspects of our framework are -

Research Driven Framework

The Tucareers framework has been developed based on extensive cross disciplinary research in the areas of Decision sciences, IT, data sciences & career decision making. We leverage career data from O*NET the world’s largest occupational data base in the world. The assessment framework uses standardized & non proprietary scales that provide accurate career recommendations based on assessment of multiple traits. Our framework includes cultural elements that facilitates use of O*NET outside USA. Career recommendations are based on established career taxonomies and provide cross linkages to national and international standards in several countries. Read More

Analytics Based Approach

We base our recommendations  on data collected from industry participants across the world of work.  Since we can statistically compare profiles on several aspects, both strengths and weaknesses are recognized and considered for identifying most accurate career matches.  For advanced usage we provide a prediction model for determining an individual’s satisfaction, tenure and life time earnings in different careers, thereby facilitating choices. A culture based learning and recommendation framework is provided to help choose from between shortlisted options. Read More 

Phased decision model

Latest research recommends that career decision should be a process and not an event. Based on existing research and best practices from existing career decision models we have developed a six phased career planning model which we call ONETCPTM .  The different phases (Orient, Navigate, Explore, Track, Choose & Plan) makes it easier for the individual to explore the world of work systematically and discover his / her best fitment in it. Besides considering the internal factors or   critical individual attributes, the model also considers external factors like salary, job opportunities etc, and cultural and social factors like opinion of parents etc. The phased approach largely reduces risks of wrong recommendations and also makes it easy for the career decision maker reach a final choice. Read More

Features for mass adoption

Giving the growing need for career guidance in emerging economies, there is a need for a robust platform keeping group counseling and concurrent usage in mind. We provide several features to facilitate this. The multi trait assessment capabilities provide detailed reports with navigable recommendations at multiple levels (clusters, job families, pathways, specific careers). The framework provides multi lingual support with recommendations mapped to configurable job zones, support for paper/pencil tests etc. Additionally, there are several features for counsellors, like detailed reports that help to field queries and a counsellor back end panel for coupon management, bulk invitations, notes  etc. Read More