Abstract:

In an age of rapid technological advancements and shifting economic landscapes, the continuous acquisition of new skills and knowledge is crucial for maintaining relevance and adaptability. As traditional job roles evolve, merge, or become obsolete, the onus of staying competitive in the job market falls on the shoulders of individuals. However, while much focus has been given to acquiring new skills, there is a parallel and often overlooked narrative: the importance of recognizing and understanding skills one already possesses, the skills required for career progression (or self-improvement), and a road map to get there. In that regard, course descriptions are pivotal in guiding students' educational choices and aspirations. Course descriptions are often the first point of contact between students and the courses they are considering. These descriptions are crucial in communicating the benefits of the course and offer insights into how a particular course can contribute to a student's academic and career aspirations. By detailing what skills and knowledge the course will impart, universities can align their offerings with the student's future goals, making their courses more appealing. They are thus crucial touchpoints in this decision-making process, an encapsulated narrative bridging the gap between educational offerings and students’ career aspirations. Further, we are at a stage of development in Artificial Intelligence where it is possible to harness its capabilities at scale to close the gap at scale.. The critical question is: How can universities strategically leverage artificial intelligence (AI) to analyze and optimize their course content, ensuring it is in sync with the dynamic demands of the industry? This process involves tagging courses with relevant skills and identifying emerging trends and gaps in their current curriculum. By using AI to perform skills tagging and augmenting this with the workforce data, universities can gain valuable insights into how well their offerings align with the evolving needs of the job market, enabling educational institutions to adjust and develop their curriculum proactively. This predictive analysis can inform universities about the skills that will be in high demand, allowing them to tailor their courses to equip students with future-ready competencies. In this evaluative study, we intend to share our analysis of the skills extracted by three skills tagging platforms on 100 course descriptions across four disciplines from a university in Singapore.

It would focus on:
(1) casting a spotlight on available skills tagging platforms,
(2) outlining a human-in-the-loop form of skills validation,
(3) providing an analysis of the efficacy of various platforms in performing skills tagging, and
(4) forecasting avenues for academic and learning analytics research with skills representation in the course syllabus.

Keywords:

Higher education, skills extraction, prescriptive analytics, predictive analytics, natural language processing application.