Evidence-based Course Accreditation Based on Real Time Job Postings
One of the challenges with the rapid technology changes in engineering and technology is to ensure that the courses developed by colleges match with what the job market actually requires. An innovative way is in using online job postings to provide an evidence-based outcome to these courses created.
Although one has traditionally referred to industry experts there is often a lack of up-to-date objective information. Sophisticated spidering and machine learning techniques can access this information and provide a real time source of information in real time for each key location in the world. An estimate from the reputable Burning Glass company is that up to 85% of jobs are now posted online.
EIT would like to use the ubiquitous Python language with its machine learning libraries to classify the huge array of engineering and technology job data and then to display this in a real time format in a simple to read dashboard including job title, occupation, employer and location. Natural language processing was used to identify specific skill and knowledge requirements – all of which is then fed through to development of courses.
Finally, the results of the research will be demonstrated with a real time dashboard showing jobs in different locations around the world with evolving job descriptions for the different disciplines.
Throughout the Python programming work on a daily basis, the programmer will tutor the client in simple language on how the program is constructed and how the machine learning algorithms are applied. This will be used to skill up EIT staff to work on related sections of the program from an academic point of view and to research the findings.