TEACHING THE MULTIVARIATE IMPLICATIONS OF QUALITY: AN APPLICATION TO CURRICULUM DESIGN

摘要

The gap between academics and industry is still a problem in many technical universities. As a consequence, after finishing a technical degree and applying for a job, many professionals find that companies demand specific talents and skills that nobody told them about at university. Thus, it is a duty of technical universities to teach their students about what are the talents and skills that they should focus on in order to be prepared for the labour market, so that they can design their curriculum accordingly. Aiming to address this situation, a novel proposal is described here based on teaching statistics and quality control applied to the quality assessment of a future graduate. The project simulates the real situation of applying for a job. First, the lecturer chooses a job position from local newspapers. Students are required to imagine how would look like their curriculum vitae with about one year of professional experience after getting their degree. Each student is requested to write a curriculum as well as a presentation letter. This information is shared with all the class. Next, students take the role of selecting the most suitable candidate for that particular job. For this purpose, each student has to read the curriculum and presentation letter of other colleagues and to rate them on a numeric scale according to different items related with experience, skills, talents, education, etc. After gathering the results and averaging the scores for each curriculum, data will be analysed with multivariate statistical methods. Students will have to interpret the result, and will conclude what are the aspects that contributed the most in those curricula that got the highest scores. With this activity students will realise by their own about the criteria that companies take into account when selecting a candidate. This information will be crucial to plan their curriculum and future strategies for job search. Moreover, this exercise will be useful for teaching multivariate statistics using real data.