Developing an employability prediction model for university students in an online environment: Resolving skills mismatch by using LMS data

The significant changes witnessed in the global and learning economy imposed by several socioeconomic challenges, including the Covid-19 pandemic, require a rapid response, new knowledge, and the adjustment of skills and competences of employees in all areas of work. Namely, the needs of the economy show an increase in technological and digital skills of employees and an increase in soft and transversal skills, which include social and emotional intelligence, critical and creative thinking and complex information processing. However, due to the high speed and unpredictability of changes in the economy, higher education systems need to equip the students with the knowledge and skills needed in the labour market that will enable us to face future challenges successfully. Moreover, the most considerable disruption of higher education in history caused by the Covid-19 has seriously questioned the quality of students' knowledge, skills and competences gained in the online learning environment. Nevertheless, the future also largely depends on each individual – student, with tax policy impacting the financial initiatives of individuals to develop skills and activate them in the labour market. Accordingly, there is an urgent need for higher education systems to consider the challenges and opportunities of online learning approaches for students' skills development and the future needs of the labour market. The mission of the project is to improve the higher education and tax system resilient to current online learning trends and labour market demands. Accordingly, an employability prediction model for university students in an online environment for resolving skills mismatch in the labour market will be developed by using learning management system (LMS) data, serving as holistic guidance for higher education and tax authorities as well as other main stakeholders such as teachers, student advisors, program managers, students and business human resources specialists. Specifically, the innovative employability prediction model for university students in an online environment will be based on an original methodology, combining LMS data and others, that builds and extends the existing student employability frameworks in a traditional educational setup. The proposed student employability prediction model will be tailored to the specifics of the online learning environment, the process of digital skills development with special attention to student characteristics. In addition, the student employability prediction model will consider unpredictable dynamics in the labour market, emphasizing student performance, skills mismatch, and employability. Finally, the student employability prediction model will be developed according to higher education and tax policy frameworks. Accordingly, a series of remarkable research results is therefore expected, contributing to the scientific and practical development of the fields of education, tax and data sciences. The student employability prediction model is to be based on several quantitative and qualitative literature review techniques (e.g., bibliometric analysis, content analysis, factor synthesis), data collection approaches (e.g., LMS data, questionnaires, interviews) and data analysis methods (e.g., logistic regression, discriminant analysis, decision tree). Intensive use of the proposed student employability prediction model will enable higher education and tax systems as well as other stakeholders to make better informed and more reliable data-driven decisions. The unpredictable nature of student employability will be addressed by the holistic approach, facilitating the hitherto still not achieved systemic way of harnessing student employability prediction modelling for higher education transformation. This will lay important foundations for further modernisation of higher education and tax systems, including other stakeholders, to suitably adapt to future challenges.