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Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

In recent years, one of the main issues for companies in hiring new employees consists in the bridge between skills provided by HE and the ones required by a more and more fast and agile market. This is why it is particular important for students to identify as soon as possible what can be their career fields, taking into consideration natural attitudes and personal wishes.

In this scientific paper, authors describe the development of a tool, or a Portal, to improve career readiness of students during Higher Education. IT tools like this are particularly interesting, because gives to companies the potential capability to have visibility on the areas of interests of the best graduates, and contacting them soon as they finish their studies, as well as compare these attitudes with workplace results and skill improvements.

This Portal consists of three major processes:

1) Career Readiness

2) Career Prediction

3) Career Development.

Career Readiness is formed  by different software modules which allows to measure the general professional dispositions required for a successful career in the 21st Century (21st Century Skills). These professional dispositions have been divided in 6 macro-areas, which in general describe  "the natural tendencies, mind state and preparations of each individual towards a professional practice": Openness to challenge, Critical Thinking, Resilience, Learning Relationships, Responsibility for Learning, and Creativity.

Career Prediction is calculated on some indicators, which are transformed in "Raw scores", that allows to cluster individuals and compare them with the features of different Community of Practice (people who share knowledge, experiences and passion on the same set of topics).

Career Development, which allows people to join different Communities of Practice and build their career domain awareness and skills.

The  tool integrates Learning Analytics techniques for career readiness "by focusing on meta-learning dimensions that accompany formal education while positioning learners and instructors at the center of the analytics process". Learning Analytics allows to provide solutions to help learners to reflect and act upon feedback about their learning performance.  Furthermore, Learning Analytics reveals hidden patterns of common traits among learners viewed as future candidates of the job market.

Citation: AbuKhousa, E.; Atif, Y., "Big learning data analytics support for engineering career readiness," in Interactive Collaborative Learning (ICL), 2014 International Conference on , vol., no., pp.663-668, 3-6 Dec. 2014 doi: 10.1109/ICL.2014.7017849 | Url: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7017849&punumber%3D7002490%26sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A7017737)%26pageNumber%3D4